The world’s leading publication for data science Imagine you’re staring at a database containing thousands of merchants across multiple countries Identify the top candidates to partner with in a new business proposal Manually browsing each site is impossible at scale so you need an automated way to gauge “how good” each merchant’s online presence is Enter the website quality score: a numeric feature (0-10) that captures key aspects of a site’s professionalism By integrating this score into your machine learning pipeline you gain a powerful signal that helps your model distinguish the highest-quality merchants and dramatically improve selection accuracy Here’s your folder structure once you clone the repository https://github.com/lucasbraga461/feat-eng-websites/ : Your dataset should be ideally in Snowflake here’s to give an idea on how you should prepare it refer to src/process_data/s1_gather_initial_table.sql Here’s what this initial table should look like: let’s say you have your data in Snowflake: p1_fetch_html_from_websites.py using Snowflake dataset That will open a window on your browser asking you to authenticate to Snowflake it’ll pull the data from the designated table and proceed with fetching the website content If you choose to pull this data from a CSV file then don’t use the flag at the end and call it this way: p1_fetch_html_from_websites.py using CSV dataset Here’s why this script is powerful at fetching website content comparing to a more basic approach Advantages of this Fetch HTML script comparing with a basic implementation ARG and JAM this is how your data folder will look like Refer to Figure 2 to visualize what the output of the first script generates visualize the table website_scraped_data_BRA Note that one of the columns is html_content which is a very large field since it takes the whole HTML content of the website Because each page’s HTML can be massive, and you’ll have hundreds or thousands of pages, you can’t efficiently process or store all that raw text in flat files. Instead, we hand off to Spark via Snowpark (Snowflake’s Pyspark engine) for scalable feature extraction See notebooks/ps_website_quality_score.ipynb for a ready-to-run example: just select the Python kernel in Snowflake and import the built-in Snowpark libraries to spin up your Spark session (see Code Block 6) Each market speaks its own language and follows different conventions so we bundle all those rules into a simple country-specific config For each country we define the contact/about keywords and price‐pattern regexes that signal a “good” merchant site then point the script at the corresponding Snowflake input and output tables This makes the feature extractor fully data-driven reusing the same code for every region with just a change of config Before we can register and use our Python scraper logic inside Snowflake This creates a named location @STAGE_WEBSITES under your DATABASE.SCHEMA where we’ll upload the UDF package (including dependencies like BeautifulSoup and lxml) making it available to any Snowflake session for HTML parsing and feature extraction we set the country_code variable to kick off the pipeline for a specific country before looping through other country codes as needed Create a stage folder to keep the UDFs created we’ll define the UDF function ‘extract_features_udf’ that will extract information from the HTML content here’s what this part of the code does: And the final part of the pyspark notebook Process and generate output table does four main things:First it applies the UDF extract_features_udf on the raw HTML producing a single features column that holds a small dict of counts/flags for each page it turns each key in the features dict into its own column in the DataFrame (so you get separate word_count it builds a 0-10 score by assigning points for each feature (e.g And finally it writes the final table back into Snowflake (replacing any existing table) so you can query or join these quality scores later the website quality score becomes a straightforward input to virtually any predictive model whether you’re training a logistic regression it quantifies a merchant’s online maturity and reliability complementing other data like sales volume or customer reviews By combining this web-derived signal with your existing metrics and recommend partners far more effectively and ultimately drive better business outcomes The screenshots and figures in this article (e.g of Snowflake query results) have been created by the author None of the numbers are drawn from real business data but were manually generated for illustrative purposes all SQL scripts are handcrafted examples; they are not extracted from any live environment but are designed to closely resemble what a company using Snowflake might encounter Step-by-step code guide to building a Convolutional Neural Network Here’s how to use Autoencoders to detect signals with anomalies in a few lines of… An illustrated guide on essential machine learning concepts Derivation and practical examples of this powerful concept Columns on TDS are carefully curated collections of posts on a particular idea or category… An illustrated guide to everything you need to know about Logistic Regression The world’s leading publication for data science Altimeter Capital Management LP lessened its position in shares of Snowflake Inc. (NYSE:SNOW - Free Report) by 42.4% in the 4th quarter according to the company in its most recent 13F filing with the Securities & Exchange Commission The institutional investor owned 4,978,310 shares of the company's stock after selling 3,669,649 shares during the quarter Snowflake comprises 13.9% of Altimeter Capital Management LP's holdings Altimeter Capital Management LP owned 1.51% of Snowflake worth $768,701,000 at the end of the most recent reporting period Other hedge funds and other institutional investors also recently bought and sold shares of the company Stonebridge Financial Group LLC acquired a new stake in shares of Snowflake in the fourth quarter worth $29,000 purchased a new stake in shares of Snowflake during the fourth quarter worth $31,000 Quadrant Capital Group LLC lifted its stake in Snowflake by 74.6% in the 4th quarter Quadrant Capital Group LLC now owns 213 shares of the company's stock worth $33,000 after purchasing an additional 91 shares in the last quarter Blue Bell Private Wealth Management LLC boosted its position in shares of Snowflake by 189.5% during the 4th quarter Blue Bell Private Wealth Management LLC now owns 220 shares of the company's stock valued at $34,000 after acquiring an additional 144 shares during the last quarter Perkins Coie Trust Co grew its stake in shares of Snowflake by 136.3% in the 4th quarter Perkins Coie Trust Co now owns 241 shares of the company's stock valued at $37,000 after buying an additional 139 shares during the period Hedge funds and other institutional investors own 65.10% of the company's stock insiders sold 371,963 shares of company stock worth $61,001,558 Corporate insiders own 7.80% of the company's stock Ten investment analysts have rated the stock with a hold rating twenty-nine have assigned a buy rating and two have given a strong buy rating to the company's stock the company has an average rating of "Moderate Buy" and a consensus target price of $200.28 Get Our Latest Stock Analysis on Snowflake SNOW stock traded up $0.21 during mid-day trading on Monday 2,493,546 shares of the company were exchanged compared to its average volume of 6,399,747 The company has a market capitalization of $55.41 billion The firm has a fifty day simple moving average of $153.50 and a 200 day simple moving average of $157.82 a quick ratio of 1.88 and a debt-to-equity ratio of 0.77 has a twelve month low of $107.13 and a twelve month high of $194.40 MarketBeat keeps track of Wall Street's top-rated and best performing research analysts and the stocks they recommend to their clients on a daily basis. MarketBeat has identified the five stocks that top analysts are quietly whispering to their clients to buy now before the broader market catches on.. While Snowflake currently has a Moderate Buy rating among analysts top-rated analysts believe these five stocks are better buys View The Five Stocks Here It's the hottest energy sector of the year and BWX Technologies were all up more than 40% in 2024 The biggest market moves could still be ahead of us and there are seven nuclear energy stocks that could rise much higher in the next several months Sign up for MarketBeat All Access to gain access to MarketBeat's full suite of research tools This article first appeared in The CEO Signal. Request an invitation When the data warehousing company Snowflake went public in September 2020 investor excitement for its cloud-driven growth story propelled its first-day valuation to $70 billion — more than 100 times its annualized revenues at the time That made it the biggest software IPO on record which helps companies store and analyze large volumes of data had come to fame with a technical accomplishment that allowed clients to scale up their computing power without buying more storage its shares had fallen below their 2020 price as it became clear that AI was ushering in a period of disruptive technological change On the day it appointed Google veteran Sridhar Ramaswamy to succeed Frank Slootman as CEO a disappointing financial update sent its stock down another 20% “I told my team that they had to earn their way back to a higher stock price Rethinking the company’s product lineup was the obvious priority for the new CEO who earlier in his career ran Google’s entire advertising business overseeing everything from search to shopping was figuring out Snowflake’s future: “How do we take new products to market in a company that has been enormously successful with an old product?” Companies can go from being hungry for success to just expecting success for all that they do “Part of what I’ve tried to drill into every Snowflake person’s way of thinking is the need to strive for success the need to be excellent on an ongoing basis the need to be gripped to capture opportunities that are there Your president might announce tariffs that shut the world down.” ‘No-regrets’ moves to turn crisis to opportunity Snowflake supports AI models from OpenAI to DeepSeek and the AI spending cycle looks likely to shelter it from some of the pressures other companies will face in a trade war “Our core product is very strong,” Ramaswamy says but he still expects an impact if current economic uncertainties persist ‘I don’t really want to commit large dollars over the next three years,’” he predicts he is using that prospect to inject a sense of urgency into the company he joined two years ago when Snowflake bought Neeva the privacy-focused search company he launched after leaving Google there are no-regrets moves that you should take ‘don’t let any crisis go to waste,‘” Ramaswamy observes and points to his appointment of a chief people officer as a way to put more “back to basics” HR rigor into the 7,500-employee business “You cannot talk about performance if you’re not willing to have a conversation about what excellence means,” he notes saying leaders “need to have conversations with people about expectations and what happens if they don’t meet expectations.” Turning AI ‘shopping’ into tangible results That includes not just telling staffers what they need to do to improve their performance and Ramaswamy has focused on helping Snowflake’s 3,000 salespeople adapt to the fast-changing technology backdrop “I have a lot of sympathy for our sales folks,” he says “They have to go pitch a product in… an environment that they don’t always understand talking to experts who sometimes know way more about these areas than they do.” It’s one thing for Snowflake to add AI capabilities to a product; it’s another for a salesperson to be able to speak eloquently about what that product can do for a customer “You can’t get 3,000 people educated on a new thing overnight especially when they’re uncomfortable with it.” His solution has been to create a dedicated team of AI experts that can help the wider sales force with early pitches to clients He also made a point of meeting each of Snowflake’s 100 largest customers in his first five months Despite the complexities he sees in this technology shift Ramaswamy’s watchword is that his people need to “demystify” AI “Our motto for AI on Snowflake is that it’s going to be easy for you to see what value you can get as a customer,” he says So he will tell clients that he’s happy to send a team over to run a half-day hackathon to show what they can do by applying AI to their data relevant examples of what AI can do is “by far the single biggest thing that CEOs are not doing,” he says Too many executives are still “shopping for AI,” Ramaswamy says likening what he sees to a consumer enjoying the fleeting dopamine hit of an online purchase before wondering whether it will need to be returned The technology cannot be abstract for executives adding that he subscribes to a host of different AI products so he can better understand their capabilities “You begin to get more and more of a feel for what’s the utility that you can expect from AI It doesn’t automatically create business value,” he says adding that he expects enterprise adoption of AI to be “slow and steady,” and driven by simple applications that are able to show real value creation a professorial graduate of the Indian Institute of Technology Madras and Brown University earned the respect of many peers in Silicon Valley for launching Neeva a search engine which relied on subscriptions rather than advertising to protect users’ privacy But the admiration it earned was not matched by commercial success and he shut the product down even before the sale to Snowflake went through What Snowflake was buying was Neeva’s people and Ramaswamy told them that “getting acquired like that meant that we really had to embrace the aspirations and the North Stars of our new home.” He had seen many of Google’s acquisitions “go horribly wrong” because of a lack of alignment between the incoming team and their new employer he admits that his heart “still skips a beat” when he reads about a related product or when he bumps into a former Neeva subscriber adding that he has used the experience to stress the need for resilience to his children “I tell them that it’s very easy to just deal with success You’ve got to deal with failure and internalize that a lot more than your success.” Running a startup was an amazing experience more because I dropped too much of what I was good at.” He learned a lot at Google about how to run a business when people consult him about changing jobs he tells them to make a list of their skills It can take years to get good at something and people should think hard before making any move in which they would set those skills aside “You’re not going to like being mediocre at a whole bunch of other things,” he says you will find “incredibly motivated people that have been working on them for 20 years You will compare yourself to them and feel pretty miserable.” “We’re in Silicon Valley, right? This is the land of Only the Paranoid Survive,” he says, in reference to the 1988 book by Andy Grove, who ran Intel before the chipmaker lost its way. That line, Ramaswamy reminds people, was “said by a company that failed to be paranoid.” Transparent news, distilled views, and global perspectives. Join fellow data and AI pioneers this June at Snowflake's annual user conference in San Francisco. Industry leading applications are Powered by Snowflake! If you have a Powered by Snowflake application, use the logos and content and brand guidelines to support your marketing. DIRECTORY POWERED BY SNOWFLAKE APPLICATIONS DIRECTORY K-12’s platform for success is Powered by Snowflake PowerSchool, the leading K-12 education platform, builds its product on Snowflake. In doing so, they’re able to scale seamlessly and provide real-time insights to school systems across the country. Your CISO’s exposure management platform is Powered by Snowflake With Tenable One and Snowflake, customers can easily centralize all vulnerability and threat data in one place to unlock a holistic view of their entire attack surface and glean actionable insights. The world’s leading RPA platform is Powered by Snowflake UIpath, a leading enterprise automation software company, built their web app for data modeling and analytics on Snowflake, allowing users to see a library of curated dashboard templates including business ROI, robots, processes and queues. Devs’ favorite cybersecurity platform is Powered by Snowflake Snyk is the developer-loved, security-trusted platform that helps secure code, dependencies, containers, and infrastructure as code. By building on Snowflake, Snyk was able to consolidate apps on their platform to a single data model, enabling their customers to have a holistic view of their security posture. Your CISO’s cloud security app is Powered by Snowflake By building on Snowflake, Lacework is able to take tens of billions of security data points and — through intelligent automation and a patented analytics engine — surface the handful of security events that matter most in a given day. Your go-to-grow eCommerce app is Powered by Snowflake Cart.com, the leading provider of comprehensive eCommerce solutions, builds on Snowflake giving customers end-to-end insights of their eCommerce operations, all in one centralized location. HOW RUDDERSTACK’S CONNECTED APPLICATION ON SNOWFLAKE MAKES IT EASY TO INGEST, TRANSFORM, AND INTEGRATE DATA Watch this webinar to understand how developers and marketers are using RudderStack, powered by Snowflake, to capture a complete view of customer engagement and the customer lifecycle.  Bring Identity Resolution to Your Data with Native Applications Powered by Snowflake: Why the Hunters Team Embraces a Connected App Model Automating Cloud Security with Lacework and Snowflake How to Make Customer Data Accessible and Actionable with Simon Data Understand User Behavior Across Multiple Channels with Piano Analytics Smart, Scalable, and Safe—How Habu Automates and Scales Data Clean Rooms on Snowflake Data Trust in the AI Data Cloud With Monte Carlo Capital One Software's First Native Snowflake App Where Data Collaboration Is Going With Habu Industry leading applications are Powered by Snowflake! If you have a Powered by Snowflake application, use the logos and content and brand guidelines to support your marketing they’re able to scale seamlessly and provide real-time insights to school systems across the country customers can easily centralize all vulnerability and threat data in one place to unlock a holistic view of their entire attack surface and glean actionable insights a leading enterprise automation software company built their web app for data modeling and analytics on Snowflake allowing users to see a library of curated dashboard templates including business ROI security-trusted platform that helps secure code Snyk was able to consolidate apps on their platform to a single data model enabling their customers to have a holistic view of their security posture Lacework is able to take tens of billions of security data points and — through intelligent automation and a patented analytics engine — surface the handful of security events that matter most in a given day the leading provider of comprehensive eCommerce solutions builds on Snowflake giving customers end-to-end insights of their eCommerce operations Watch this webinar to understand how developers and marketers are using RudderStack to capture a complete view of customer engagement and the customer lifecycle and Safe—How Habu Automates and Scales Data Clean Rooms on Snowflake Dimensional Fund Advisors LP cut its position in shares of Snowflake Inc. (NYSE:SNOW - Free Report) by 2.3% in the fourth quarter according to the company in its most recent filing with the Securities & Exchange Commission The institutional investor owned 337,560 shares of the company's stock after selling 7,891 shares during the period Dimensional Fund Advisors LP owned about 0.10% of Snowflake worth $52,125,000 as of its most recent filing with the Securities & Exchange Commission Several other hedge funds have also made changes to their positions in the company Stonebridge Financial Group LLC bought a new stake in shares of Snowflake in the fourth quarter valued at about $29,000 Quadrant Capital Group LLC raised its holdings in shares of Snowflake by 74.6% in the 4th quarter Quadrant Capital Group LLC now owns 213 shares of the company's stock valued at $33,000 after purchasing an additional 91 shares in the last quarter Blue Bell Private Wealth Management LLC lifted its position in shares of Snowflake by 189.5% in the 4th quarter Blue Bell Private Wealth Management LLC now owns 220 shares of the company's stock worth $34,000 after purchasing an additional 144 shares during the period Perkins Coie Trust Co boosted its stake in shares of Snowflake by 136.3% during the fourth quarter Perkins Coie Trust Co now owns 241 shares of the company's stock valued at $37,000 after purchasing an additional 139 shares in the last quarter increased its holdings in Snowflake by 1,437.5% during the fourth quarter now owns 246 shares of the company's stock valued at $38,000 after buying an additional 230 shares during the period 65.10% of the stock is currently owned by hedge funds and other institutional investors Insiders sold 371,963 shares of company stock valued at $61,001,558 over the last three months 7.80% of the stock is owned by corporate insiders NYSE:SNOW traded up $0.21 during trading hours on Monday The company's stock had a trading volume of 2,493,546 shares compared to its average volume of 6,399,836 has a 52-week low of $107.13 and a 52-week high of $194.40 The stock has a market capitalization of $55.41 billion The stock has a fifty day moving average of $153.46 and a 200-day moving average of $157.49 SNOW has been the topic of several recent research reports Truist Financial dropped their target price on Snowflake from $225.00 to $210.00 and set a "buy" rating on the stock in a research report on Monday Jefferies Financial Group cut their target price on Snowflake from $220.00 to $190.00 and set a "buy" rating on the stock in a research note on Monday UBS Group boosted their target price on shares of Snowflake from $190.00 to $200.00 and gave the stock a "neutral" rating in a report on Thursday Evercore ISI raised their price target on shares of Snowflake from $190.00 to $200.00 and gave the company an "outperform" rating in a report on Wednesday Macquarie assumed coverage on shares of Snowflake in a research report on Wednesday They set a "neutral" rating and a $160.00 target price for the company Ten research analysts have rated the stock with a hold rating twenty-nine have issued a buy rating and two have assigned a strong buy rating to the stock the stock currently has an average rating of "Moderate Buy" and an average price target of $200.28 Check Out Our Latest Analysis on Snowflake MarketBeat's analysts have just released their top five short plays for May 2025. Learn which stocks have the most short interest and how to trade them. Enter your email address to see which companies made the list. Sign up for MarketBeat All Access to gain access to MarketBeat's full suite of research tools. © MarketBeat Media, LLC 2010-2025. All rights reserved. EfficientSkip the infrastructure management with serverless AI to analyze unstructured data, build data agents and other AI apps. TrustedProtect the value of your data and models with industry-leading security and unified governance trusted by thousands of organizations. Enable business users to interact with data using natural language, helping them to find answers faster, self-serve insights and save valuable time. Cortex SearchQuickly and securely find information by asking questions within a given set of documents with fully managed text embedding, hybrid search (semantic + keyword) and retrieval. LLMs and embed modelsAccess top-tier large language models (LLMs) such as Anthropic Claude, Meta Llama 3, Mistral Large and more using serverless functions and APIs. Cortex AgentsBreak down user questions and route data requests to Cortex AI retrieval services. AI & ML StudioEmpower users of all technical levels to securely use AI with a built-in no-code development interface.† Customize LLMs securely and effortlessly to increase the accuracy and performance of models for use-case specific tasks. OUR CUSTOMERSLEADING DATA AND ENGINEERING TEAMSUSE SNOWFLAKE CORTEX AI“With Snowflake, I can empower smart people to bring AI to life in one place. Cortex is a one-stop shop. It scales, it’s easy, and the data stays 100% in Snowflake’s environment.” COO & Chief Data & Analytics Officer, TS Imagine Delivery rich analytics on text-based datasets with custom summaries, sentiment analysis, translation and other NLP-based tasks. Intelligent document processingExtract content like invoice amounts or contract terms across a large volume of documents. SQL development assistantBoost productivity of analysts, data engineers and other SQL developers with AI that understand the context of your data to turn questions into SQL queries. AI-powered business intelligenceEnable business users to self-serve highly accurate insights using natural language from any application. RAG-based document searchDemocratize knowledge with applications that can quickly search multiple documents and other text-based assets such as wikis and FAQs using retrieval augmented generation (RAG). Cortex AIIntegration PartnersSelect a partner logo below to learn more about its integration COURSE Blog Start your 30-DayFree TrialTry Snowflake free for 30 days and experience the AI Data Cloud that helps eliminate the complexity, cost and constraints inherent with other solutions.  Quickly analyze unstructured data and build generative AI applications using fully managed LLMs Enable multiple users to use AI services with no-code state-of-the-art RAG and other gen AI services next to your governed data Skip the infrastructure management with serverless AI to analyze unstructured data Protect the value of your data and models with industry-leading security and unified governance trusted by thousands of organizations Enable business users to interact with data using natural language self-serve insights and save valuable time Quickly and securely find information by asking questions within a given set of documents with fully managed text embedding hybrid search (semantic + keyword) and retrieval Access top-tier large language models (LLMs) such as Anthropic Claude Mistral Large and more using serverless functions and APIs Break down user questions and route data requests to Cortex AI retrieval services Empower users of all technical levels to securely use AI with a built-in no-code development interface.† Customize LLMs securely and effortlessly to increase the accuracy and performance of models for use-case specific tasks I can empower smart people to bring AI to life in one place and the data stays 100% in Snowflake’s environment.” COO & Chief Data & Analytics Officer Delivery rich analytics on text-based datasets with custom summaries Extract content like invoice amounts or contract terms across a large volume of documents data engineers and other SQL developers with AI that understand the context of your data to turn questions into SQL queries Enable business users to self-serve highly accurate insights using natural language from any application Democratize knowledge with applications that can quickly search multiple documents and other text-based assets such as wikis and FAQs using retrieval augmented generation (RAG) Select a partner logo below to learn more about its integration Try Snowflake free for 30 days and experience the AI Data Cloud that helps eliminate the complexity cost and constraints inherent with other solutions.  Protect your data with built-in governanceSecure your data lake with granular access controls, and out-of-the-box telemetry to audit usage. Lower Total Cost of Ownership With a Fully Managed ServiceSpend less time and money keeping the lights on—and more on strategic work—by letting Snowflake handle upgrades, availability, storage maintenance, compliance certifications and more. Reduce the complexity of stitching together multiple services and systems by supporting many use cases on unstructured, semi-structured and structured data — all in one platform. With efficient compression, automatic micro-partitioning and encryption in transit and at rest, Snowflake’s fully managed storage helps spare you the hassle of securing, backing up and optimizing your data files. Manage and access files and tables stored in external data lake storage—including open file formats and Apache Iceberg—without having to copy or move data. Easily integrate third-party data with direct access to live data sets from Snowflake Marketplace, which reduces the costs and burden associated with traditional extract, transform and load (ETL) pipelines and API-based integrations. Or, simply use native connectors to bring data in. Know, Protect and Connect Your DataWhether you’re storing data in Snowflake or your own object storage, you can use Snowflake Horizon Catalog to understand, govern and audit your data, apps and models. Run pipelines with Snowflake’s elastic multi-cluster compute for reliable performance, cost savings and near-zero maintenance. Ingest and transform files or tables with SQL, Python, Scala or Java — without the need for additional clusters, services or data copies to manage. Snowflake’s near-instant elasticity rightsizes compute resources, and consumption-based pricing ensures you only pay for what you use. Ongoing performance improvements and native optimizations continue to make costs increasingly efficient. "By taking advantage of the Snowflake virtual warehouse, we were able to meet our one-to-three-minute SLA for processing pipelines and bring down total runtimes by as much as 75%." Director, Data Management and Analytics, AMN Healthcare Use a fully managed platform connecting businesses globally across any type or scale of data. PlatformPlatformUse a fully managed platform connecting businesses globally across any type or scale of data. AnalyticsDo data analytics faster with optimal pricing and near-zero maintenance. AnalyticsAnalyticsDo data analytics faster with optimal pricing and near-zero maintenance. AISecurely build and deploy LLMs and ML models customized with your data.  AIAISecurely build and deploy LLMs and ML models customized with your data.  Data EngineeringBuild reliable, continuous data pipelines at scale in the language of your choice. Data EngineeringData EngineeringBuild reliable, continuous data pipelines at scale in the language of your choice. Applications & CollaborationShare live data across clouds and orgs, plus easily develop, distribute and scale apps. Applications & CollaborationApplications & CollaborationShare live data across clouds and orgs, plus easily develop, distribute and scale apps. In addition to building data lakes, you can use the Snowflake platform for a variety of architecture patterns to best suit your needs—all globally connected in the AI Data Cloud. Deliver domain-driven ownership with self-service infrastructure as a platform. Data LakehouseBuild a transactional data lake architecture pattern for unified analytics, AI/ML and other collaborative workloads. DATA LAKEQUICKSTARTSFollow along with Snowflake’s Quickstart guides to learn hands-on how to work with data in semi-structured, unstructured, and open formats. Webinar Ebook data lake resource libraryStart your 30-DayFree TrialTry Snowflake free for 30 days and experience the AI Data Cloud that helps eliminate the complexity, cost and constraints inherent with other solutions.  performant data lake that supports a variety of data sources and integrations to unify data for analytics semi-structured and structured data together whether it’s stored externally or in Snowflake’s optimized Secure your data lake with granular access controls and out-of-the-box telemetry to audit usage Spend less time and money keeping the lights on—and more on strategic work—by letting Snowflake handle upgrades Reduce the complexity of stitching together multiple services and systems by supporting many use cases on unstructured semi-structured and structured data — all in one platform automatic micro-partitioning and encryption in transit and at rest Snowflake’s fully managed storage helps spare you the hassle of securing Manage and access files and tables stored in external data lake storage—including open file formats and Apache Iceberg—without having to copy or move data Easily integrate third-party data with direct access to live data sets from Snowflake Marketplace which reduces the costs and burden associated with traditional extract transform and load (ETL) pipelines and API-based integrations simply use native connectors to bring data in Whether you’re storing data in Snowflake or your own object storage you can use Snowflake Horizon Catalog to understand Run pipelines with Snowflake’s elastic multi-cluster compute for reliable performance Ingest and transform files or tables with SQL Scala or Java — without the need for additional clusters Snowflake’s near-instant elasticity rightsizes compute resources and consumption-based pricing ensures you only pay for what you use Ongoing performance improvements and native optimizations continue to make costs increasingly efficient "By taking advantage of the Snowflake virtual warehouse we were able to meet our one-to-three-minute SLA for processing pipelines and bring down total runtimes by as much as 75%." users and use cases directly to your data—all within the AI Data Cloud Use a fully managed platform connecting businesses globally across any type or scale of data Do data analytics faster with optimal pricing and near-zero maintenance Securely build and deploy LLMs and ML models customized with your data.  continuous data pipelines at scale in the language of your choice you can use the Snowflake platform for a variety of architecture patterns to best suit your needs—all globally connected in the AI Data Cloud Deliver domain-driven ownership with self-service infrastructure as a platform Build a transactional data lake architecture pattern for unified analytics Follow along with Snowflake’s Quickstart guides to learn hands-on how to work with data in semi-structured 2025|5 min readMeet the SnowConvert Migration Assistant: Your Key to Faster AI-Powered Data Warehouse ModernizationAdvanced SQL and complex stored procedures form the very backbone of data warehouses these intricate constructs frequently turn into costly and time-consuming hurdles While many tools may offer automation to streamline the migration process the unavoidable manual review of flagged issues – Errors and Issues (EWIs) – has historically demanded specialized expertise and a significant investment of time This bottleneck can dramatically inflate project timelines delaying access to critical insights and the full benefits of a new environment That’s why we are excited to announce the public preview of the SnowConvert Migration Assistant a new AI-powered feature specifically designed to tackle this migration bottleneck Integrated directly into the Snowflake Visual Studio extension this assistant uses the power of Snowflake Cortex AI to empower your team to resolve complex migration challenges with speed and efficiency By providing AI-driven explanations and suggested fixes for EWIs directly within the SnowConvert outputs this integration streamlines your development workflow concise explanations generated by large language models (LLMs) and AI-driven suggestions for effective resolutions With the new SnowConvert Migration Assistant the often-daunting task of manually reviewing and resolving complex code issues becomes significantly more efficient This innovation seeks to simplify and accelerate your data warehouse migrations allowing you to unlock the value of the Snowflake AI Data Cloud faster The SnowConvert Migration Assistant brings readily available fixes and suggestions to developers when and where they need it When working with SnowConvert output files users can interact with an AI-powered interface that explains each EWI in detail and suggests specific fixes The assistant can handle a wide range of migration scenarios from simple syntax differences to complex stored procedure conversions providing detailed explanations that help users understand not just what to change but why the change is necessary The true power of the SnowConvert Migration Assistant lies in its interactive nature Users can engage in a direct dialogue about specific EWIs asking for clarification or requesting alternative approaches tailored to their unique context This conversational interface democratizes the migration process making intelligent assistance readily available to users of all database expertise levels from seasoned migration specialists to developers newer to the Snowflake ecosystem making the assistant a dynamic and significantly more powerful tool A foundational principle in the development of this assistant was enabling uncompromising security and governance all AI operations are executed entirely within the customer's own Snowflake account This architectural decision enables code analysis and suggested fixes to adhere to the stringent security and governance standards that Snowflake customers have implemented By seamlessly integrating with the existing Snowflake VS Code extension and leveraging Snowflake's robust security infrastructure sensitive code and data don’t leave the customer's control while providing cutting-edge AI capabilities Our confidence in the SnowConvert Migration Assistant is deeply rooted in practical experience and the specific challenges encountered when dealing with complex SQL This feature has been optimized based on data and learnings from numerous real-world data warehouse migrations we've rigorously validated the assistant's effectiveness with real users during the private preview incorporating their feedback to refine its accuracy and the relevance of its AI-driven explanations and suggested fixes grounded in real-world scenarios and user validation means the SnowConvert Migration Assistant directly addresses the practical hurdles of migration offering improvements in speed and efficiency This release marks a significant step in our broader strategic direction: leveraging the power of AI to fundamentally simplify data warehouse migrations By augmenting SnowConvert's robust conversion capabilities with the intelligence of Cortex AI we are creating solutions that not only enhance efficiency but also maintain the high standards of reliability that our enterprise customers demand This deep integration of AI into our migration product highlights our commitment to reducing the inherent complexity and time typically associated with data warehouse migrations without compromising the precision and reliability our customers depend on Ready to experience the power of an AI-assisted data warehouse migration Users can access the SnowConvert Migration Assistant directly through the familiar Snowflake VS Code extension by simply enabling the feature within the extension's settings the assistant automatically activates when you select SnowConvert output files containing EWIs AI-powered assistance to guide you towards resolution the assistant requires no additional configuration beyond the standard Snowflake connection settings already in use within VS Code for immediate accessibility for all existing SnowConvert users Get the best, coolest and latest delivered to your inbox each week By submitting this form, I understand Snowflake will process my personal information in accordance with their Privacy Notice. Try Snowflake free for 30 days and experience the AI Data Cloud that helps eliminate the complexity, cost and constraints inherent with other solutions.  Advanced SQL and complex stored procedures form the very backbone of data warehouses That’s why we are excited to announce the public preview of the SnowConvert Migration Assistant I understand Snowflake will process my personal information in accordance with their Privacy Notice To support a wide range of workloads, it’s optimized for performance at scale no matter whether someone’s working with SQL, Python or other languages. And it’s globally connected so organizations can securely access the most relevant content across clouds and regions, with one consistent experience.  Optimized storageBrings unstructured, semi-structured and structured data together at near-infinite scale. Elastic, multi-cluster computeScales up and down based on usage and scales out to support massive concurrent users, data volumes, and workloads all with one engine. Cloud servicesKeeps Snowflake self-managing, with automations that eliminate costly and complex resource investments. SnowgridDelivers a single, connected experience across regions and clouds globally to enable unified governance, business continuity and both intra- and inter-org collaboration. Instead of the historic pattern of data moving to different systems or teams, the AI Data Cloud enables organizations to bring all workloads directly to their data. Thus breaking down silos and fueling more powerful insights, quickly.  AnalyticsDo data analytics faster with optimal pricing and near-zero maintenance. AISecurely build and deploy LLMs and ML models customized with your data.  Data EngineeringBuild reliable, continuous data pipelines at scale in the language of your choice. Applications & CollaborationShare live data across clouds and orgs Applications & CollaborationApplications & CollaborationShare live data across clouds and orgs The AI Data Cloud also helps organizations drive new revenue with the Snowflake Marketplace. Distribute and monetize Snowflake Native Apps to the entire AI Data Cloud network of thousands of organizations, and streamline monetization with customizable billing, in-platform purchasing and fine-grained usage reports. Join the hundreds that are building and distributing modern applications with Snowflake.  Start your 30-DayFree TrialTry Snowflake free for 30 days and experience the AI Data Cloud that helps eliminate the complexity, cost and constraints inherent with other solutions.  The AI Data Cloud is a global network that connects organizations to the data and applications most critical to their business The AI Data Cloud enables a wide range of possibilities from breaking down silos within an organization to collaborating over content with partners and customers and even integrating external data and applications for fresh insights Powering the AI Data Cloud is Snowflake’s single platform. Its unique architecture connects businesses globally, at practically any scale to bring data and workloads together. Together with the Snowflake Marketplace which simplifies the sharing collaborating and monetizing of thousands of datasets services and entire data applications — this creates the active and growing AI Data Cloud Snowflake’s platform eliminates data silos and simplifies architectures so organizations can get more value from their data unified product with automations that reduce complexity and help ensure everything “just works.” it’s optimized for performance at scale no matter whether someone’s working with SQL And it’s globally connected so organizations can securely access the most relevant content across clouds and regions semi-structured and structured data together at near-infinite scale Scales up and down based on usage and scales out to support massive concurrent users with automations that eliminate costly and complex resource investments connected experience across regions and clouds globally to enable unified governance business continuity and both intra- and inter-org collaboration Instead of the historic pattern of data moving to different systems or teams the AI Data Cloud enables organizations to bring all workloads directly to their data Thus breaking down silos and fueling more powerful insights Snowflake is powering the future of application development Snowflake’s platform provides the building blocks to build test and deploy data-intensive applications easily scale to support dynamic demand and ensure everything stays up and running even across clouds The AI Data Cloud also helps organizations drive new revenue with the Snowflake Marketplace Distribute and monetize Snowflake Native Apps to the entire AI Data Cloud network of thousands of organizations and streamline monetization with customizable billing in-platform purchasing and fine-grained usage reports Join the hundreds that are building and distributing modern applications with Snowflake.  Identify and track sensitive data with built-in object tagging; Sensitive Data Automatic2 and Custom Classification2 Audit content usage through Data & Listing Access History and Schema Change Tracking Monitor data quality with both out-of-the-box and custom metrics Understand relationships through object dependencies and lineage Centrally apply granular access policies across all databases, schemas and tables (including open table formats) in an account Safeguard data, apps and models with built-in encryption, authentication, network policies, unified RBAC and Listing Discovery Controls Streamline security and compliance monitoring across clouds based on industry best practices with Trust Center 5Minutes for NYC Health + Hospitals to equip users with updated membership data — down from five days Rows of healthcare data in Snowflake for NYC Health + Hospitals to provide a holistic view of patients Tap into the vast ecosystem of Snowflake resources, partner offerings and community of Data Superheroes. Maximize existing investments and manage your entire data estate with pre-built integrations by leading partners. Snowflake CommunityMeet and learn from a global network of data governance leaders in Snowflake’s community forum and Snowflake User Groups. Snowflake DocumentationCheck out Snowflake documentation for information about features, tutorials and a detailed reference of commands, functions and operators. ResourcesExplore more governance and discovery resources by industry Docs course WEBINAR Guide Learn how implementing effective data discovery practices can unlock powerful insights from your organization's information, turning raw data into a strategic asset that reveals hidden patterns, and creating new business opportunities through a unified approach to data exploration and analysis. Guide Learn how implementing data integrity best practices will transform your business by ensuring accuracy and reliability throughout your data's lifecycle, turning your information into a trusted strategic asset that drives better decisions and protects your most valuable resources. Guide Explore how implementing a structured data governance framework secures your organization's most valuable information assets—establishing clear protocols that protect sensitive data while enabling teams to make more informed decisions and maximize the strategic value of your enterprise information. Guide Discover how Master Data Management can transform your business by creating a unified view of all your critical data—unlock deeper insights, eliminate costly errors, and deliver exceptional customer experiences. governance resourcesStart your 30-DayFree TrialTry Snowflake free for 30 days and experience the AI Data Cloud that helps eliminate the complexity, cost and constraints inherent with other solutions.  Built-in data governance and discovery for the AI Data Cloud discovery and collaboration capabilities that help data governors CISO’s/security admins and data teams both protect and unlock the value of sensitive data Centrally apply granular access policies across all databases schemas and tables (including open table formats) in an account unified RBAC and Listing Discovery Controls Snowflake Horizon Catalog’s unified security and governance features provide peace of mind in the highly regulated healthcare industry Minutes for NYC Health + Hospitals to equip users with updated membership data — down from five days Tap into the vast ecosystem of Snowflake resources partner offerings and community of Data Superheroes Maximize existing investments and manage your entire data estate with pre-built integrations by leading partners Meet and learn from a global network of data governance leaders in Snowflake’s community forum and Snowflake User Groups Check out Snowflake documentation for information about features tutorials and a detailed reference of commands Explore more governance and discovery resources by industry Learn how implementing effective data discovery practices can unlock powerful insights from your organization's information turning raw data into a strategic asset that reveals hidden patterns and creating new business opportunities through a unified approach to data exploration and analysis Learn how implementing data integrity best practices will transform your business by ensuring accuracy and reliability throughout your data's lifecycle turning your information into a trusted strategic asset that drives better decisions and protects your most valuable resources Explore how implementing a structured data governance framework secures your organization's most valuable information assets—establishing clear protocols that protect sensitive data while enabling teams to make more informed decisions and maximize the strategic value of your enterprise information Discover how Master Data Management can transform your business by creating a unified view of all your critical data—unlock deeper insights and deliver exceptional customer experiences data-intensive applications with fully managed data and AI infrastructure and zero copy integration AI models and apps with your teams and partners on any cloud or region — without additional ETL or integration work Spend less time integrating data and SaaS providers — Snowflake does that work for you Extend what’s possible through apps and AI products all while adding flexibility in how you use your Snowflake budget Streamline your architecture to deliver new features faster— from embedded analytics to generative AI Easily integrate data and applications in client environments globally across clouds through Snowflake Marketplace Snowflake’s fully managed service offers automatic scaling and per-second pricing to meet demand while optimizing margins The map is intended for illustrative purposes and does not show all available regions, for a full list of regions see documentation. Reduce the risks and costs of data movement and integrations with cross-cloud, zero-ETL data sharing. Provide the right people with the right access to your data and AI models  with Snowflake Horizon Catalog’s built-in governance and discovery features. Unlock the value of your most sensitive data with advanced privacy policies and Snowflake Data Clean Rooms. Connect to 680+ data and SaaS providers within minutes, not months, by using existing, live connections and pre-built connectors. Easily extend Snowflake’s capabilities with apps and AI products that include everything from AI agents to graph analytics, geospatial, computer vision models to cost optimization tools. Add budget flexibility and increase your buying power by bundling your data and SaaS spend with Snowflake. Grow your revenue, increase margins and deliver the best customer experience by offering data, apps and AI products on Snowflake Marketplace. Accelerate production-ready innovation with a unified suite of features and services for data and AI. Learn from and connect with others in the AI Data Cloud. Explore the developer resources you need to build and scale your applications.  Free Collaboration WorkshopsTake this free hands-on workshop to learn how your organization can collaborate more effectively with Snowflake. Snowflake CommunityMeet and learn from a global network of data practitioners in Snowflake’s community forum and Snowflake User Groups. Application & CollaborationResourcesLive Demos EBOOK Use Case apps & collaboration resource libraryStart your 30-DayFree TrialTry Snowflake free for 30 days and experience the AI Data Cloud that helps eliminate the complexity, cost and constraints inherent with other solutions.  data products and AI models for your teams Get enterprise-grade scale and governance — without moving any data The map is intended for illustrative purposes and does not show all available regions, for a full list of regions see documentation Reduce the risks and costs of data movement and integrations with cross-cloud Provide the right people with the right access to your data and AI models  with Snowflake Horizon Catalog’s built-in governance and discovery features Unlock the value of your most sensitive data with advanced privacy policies and Snowflake Data Clean Rooms Connect to 680+ data and SaaS providers within minutes Easily extend Snowflake’s capabilities with apps and AI products that include everything from AI agents to graph analytics computer vision models to cost optimization tools Add budget flexibility and increase your buying power by bundling your data and SaaS spend with Snowflake increase margins and deliver the best customer experience by offering data apps and AI products on Snowflake Marketplace Accelerate production-ready innovation with a unified suite of features and services for data and AI Learn from and connect with others in the AI Data Cloud Take this free hands-on workshop to learn how your organization can collaborate more effectively with Snowflake Meet and learn from a global network of data practitioners in Snowflake’s community forum and Snowflake User Groups Simplify governance and security Maintain a single data governance and security model for both transactional and analytical data. With Hybrid Tables’ fast, high-concurrency point operations, you can store application and workflow state directly in Snowflake. Use Hybrid Tables to serve data without reverse ETL and to build lightweight transactional apps. Snowflake allows you to maintain a single data governance and security model for both transactional and analytical data on one platform so you can focus on accelerating innovation knowing that all your data is protected consistently. MarketWise Reduces Costs and Ops Burden While Accelerating Insights with Snowflake Hybrid Tables Start BuildingLearn from and connect with builders in the AI Data Cloud. Snowflake for Developers contains resources to help you build, operate and optimize your applications. UNISTOREResourcesWebinar EBOOK White Paper unistore resource libraryStart your 30-DayFree TrialTry Snowflake free for 30 days and experience the AI Data Cloud that helps eliminate the complexity, cost and constraints inherent with other solutions.  Unify transactional and analytical workloads in Snowflake for enhanced simplicity Execute both transactional and analytics use cases from a single database.  Maintain a single data governance and security model for both transactional and analytical data you can store application and workflow state directly in Snowflake Use Hybrid Tables to serve data without reverse ETL and to build lightweight transactional apps Snowflake allows you to maintain a single data governance and security model for both transactional and analytical data on one platform so you can focus on accelerating innovation knowing that all your data is protected consistently Learn from and connect with builders in the AI Data Cloud Snowflake for Developers contains resources to help you build Reduce operational burden from development and operation teams with fully managed AI running next to the data, eliminating costly data pipeline and integration maintenance.  Deploy trusted AI efficiently into production with unified security, access and cost controls, and natively integrated observability for both data and AI. Process text, documents, and other media with SQL functions using LLMs from providers like Anthropic and Meta running inside Snowflake. This eliminates the need for single API calls to external services, allowing you to orchestrate and scale unstructured data processing workflows with ease. Train a model in a Snowflake NotebookKEY FEATURESAccelerate production-ready innovation with a unified suite of AI running next to your data. “At Zoom Communications, our mission is to be one platform delivering limitless human connections. To accomplish this, we want to empower every team member to safely use AI to better serve our customers. Using Snowflake’s easy-to-use and secure platform for generative AI and machine learning, we continue to democratize AI to efficiently turn data into better customer experiences.” Extend Snowflake’s security, performance and ease with our technology partners. Open Source ProjectsBrowse open source projects that Snowflakes contributes to, maintains or supports. Solutions COURSE Community Guide Discover how AI-augmented business intelligence empowers non-technical users to analyze data through conversational interfaces, enabling faster insights and data-driven decision making with tools like Snowflake's AI Data Cloud. Guide Learn how RAG technology enhances LLMs with external knowledge bases, providing more accurate and trustworthy AI responses for business applications through Snowflake Cortex AI. Guide Explore how AI-powered text and document processing automates data extraction and analysis from various documents, saving time and resources while improving decision-making through enhanced insights from LLMs like Snowflake's Document AI. ai resource libraryStart your 30-DayFree TrialTry Snowflake free for 30 days and experience the AI Data Cloud that helps eliminate the complexity, cost and constraints inherent with other solutions.  and train models using both structured and unstructured data — all with minimal operational overhead and end-to-end governance genAI app and model development with a single platform made of modular Reduce operational burden from development and operation teams with fully managed AI running next to the data eliminating costly data pipeline and integration maintenance.  Deploy trusted AI efficiently into production with unified security and natively integrated observability for both data and AI and other media with SQL functions using LLMs from providers like Anthropic and Meta running inside Snowflake This eliminates the need for single API calls to external services allowing you to orchestrate and scale unstructured data processing workflows with ease Enable access and action to enterprise data via natural language interfaces with scalable processing and accurate out-of-the-box retrieval for both structured and unstructured data using Cortex Analyst text-to-SQL service and Cortex Search fully managed RAG engine Develop models faster with Notebooks using a container-based runtime that distributes data loading and model training on GPUs.2 Take features and models to production at scale with Snowflake Feature Store and Model Registry.  Accelerate production-ready innovation with a unified suite of AI running next to your data our mission is to be one platform delivering limitless human connections we want to empower every team member to safely use AI to better serve our customers Using Snowflake’s easy-to-use and secure platform for generative AI and machine learning we continue to democratize AI to efficiently turn data into better customer experiences.” performance and ease with our technology partners Browse open source projects that Snowflakes contributes to Discover how AI-augmented business intelligence empowers non-technical users to analyze data through conversational interfaces enabling faster insights and data-driven decision making with tools like Snowflake's AI Data Cloud Learn how RAG technology enhances LLMs with external knowledge bases providing more accurate and trustworthy AI responses for business applications through Snowflake Cortex AI Explore how AI-powered text and document processing automates data extraction and analysis from various documents saving time and resources while improving decision-making through enhanced insights from LLMs like Snowflake's Document AI 1Private preview\r\n2Public preview\r\n3Coming soon hosted its flagship Data for Breakfast event in Riyadh at the Crowne Plaza Riyadh RDC Hotel & Convention The event brought together technology leaders and innovators from across the Kingdom to explore how businesses can unlock the full potential of their data through AI Attendees discovered how the Snowflake platform empowers organisations to activate data across data lakehouses and warehouses through a keynote presentation on the AI Data Cloud followed by customer-led sessions from O3ai a cloud-based restaurant management and POS solution The event also featured a live demo highlighting the platform’s performance With PwC reporting that 81% of CEOs in the Kingdom have embraced AI in the past year the sessions underscored how data is the foundation for building impactful AI strategies that deliver real-world value and organisations are scaling AI use cases built on robust data strategies including our regional HQ and local cloud deployment enables us to support data sovereignty and drive forward Vision 2030 Our annual Data for Breakfast demonstrates how the Snowflake AI Data Cloud is turning strategy into execution — offering a practical roadmap for enterprises to unlock business value and realise measurable ROI through secure added: "In the fast-moving F&B industry the ability to act on data in real-time is a game changer we’re able to unify our data landscape and deliver insights at scale whether it's to enhance customer experiences our ability to leverage Snowflake’s AI-powered analytics helps us anticipate trends and respond quickly to the evolving needs of thousands of clients." Snowflake’s research reveals 93% of enterprises report their Gen AI initiatives as successful reinforcing the role of modern data platforms in realising AI’s potential The Kingdom’s $100 billion AI initiative underpins a national push to lead in global tech supported by strong digital infrastructure and an innovation-first mindset Snowflake is also investing in local talent development through its One Million Minds program by equipping Saudi professionals with advanced AI training and skills supporting Vision 2030’s goal of building a competitive With a unified platform that empowers organisations and individuals alike Snowflake is helping drive Saudi Arabia’s knowledge economy while enabling AI innovation at scale Snowflake continues to champion the importance of a strong data foundation About Snowflake Snowflake makes enterprise AI easy More than 11,000 companies around the globe use Snowflake’s AI Data Cloud to share data The press release is provided for informational purposes only legal or investment advice or opinion regarding the suitability value or profitability of any particular security Neither this website nor our affiliates shall be liable for any errors or inaccuracies in the content or for any actions taken by you in reliance thereon You expressly agree that your use of the information within this article is at your sole risk To the fullest extent permitted by applicable law its affiliates and the respective shareholders content providers and licensors will not be liable (jointly or severally) to you for any direct even if the parties have been advised of the possibility or could have foreseen any such damages Get insights and exclusive content from the world of business and finance that you can trust marketers can now take their personalization efforts beyond human scale breaking free from the constraints of rule-based marketing and using AI to autonomously experiment and optimize every customer interaction Join us to discover how the marketing team at WHOOP uses Hightouch and Snowflake to maximize customer lifetime value through AI-powered personalization Don’t miss this opportunity to explore the future of AI-powered marketing © 2025 Snowflake Inc. All Rights Reserved |  If you’d rather not receive future emails from Snowflake, unsubscribe here or customize your communication preferences Appfolio delivers Streamlit apps in minutes Turn Python Scripts Into Web AppsDefine widgets—filters, graphs, sliders and more—as variables to interact with your data and models. Add, adjust or remove components quicklyModify your code and see changes go live with side-by-side editor and app preview screens. Go from build to prod in one clickDeploy Streamlit apps in scalable, reliable infrastructure—then share via URLs that leverage existing role-based access controls. Streamlit in SnowflakeSolution GalleryReady to build your own? Discover how Streamlit in Snowflake empowers data teams to make rapid, data-informed decisions. Retrieval augmented generation with Snowflake CortexImplement RAG with a chat assistant that is knowledgeable on a specific topic. Time series forecasting and anomaly detection on product sales data using Snowflake ML Functions to train models. Allow users to input text and perform Named Entity analysis with named entities highlighted in different background colors based on their type. Explore the language preferences of users visiting your app and gain insight into global usage patterns. Gain insights into the emotions, opinions, and experiences shared by travelers posting airline-releated content online. Build charts to easily detect if a product is running low on inventory, identify best-sellers, and get insights into product performance. Provide detailed insights into various aspects of your Snowflake account usage. Gather insights about credit and compute cosumption over time, highlighting users with the highest usage of resources. Understand the basic structure of some of the most popular Streamlit widgets, including CSS code. Vizualize geo-temporal data from NY taxi pickups  Discover the most popular projects ranked by star ratings and track their growth over time. Display sales data to analyze quarter-over-quarter growth, highlighting trends to help you evalulate business performance. Discover how Streamlit in Snowflake empowers data teams to make rapid, data-informed decisions. Docs Video Turn data and AI into interactive apps with Python—now all in Snowflake Data scientists and Python developers can now combine Streamlit’s component-rich performance and security of the Snowflake platform sliders and more—as variables to interact with your data and models Modify your code and see changes go live with side-by-side editor and app preview screens reliable infrastructure—then share via URLs that leverage existing role-based access controls Discover how Streamlit in Snowflake empowers data teams to make rapid Implement RAG with a chat assistant that is knowledgeable on a specific topic Time series forecasting and anomaly detection on product sales data using Snowflake ML Functions to train models Allow users to input text and perform Named Entity analysis with named entities highlighted in different background colors based on their type Explore the language preferences of users visiting your app and gain insight into global usage patterns and experiences shared by travelers posting airline-releated content online Build charts to easily detect if a product is running low on inventory Provide detailed insights into various aspects of your Snowflake account usage Gather insights about credit and compute cosumption over time highlighting users with the highest usage of resources Understand the basic structure of some of the most popular Streamlit widgets Discover the most popular projects ranked by star ratings and track their growth over time Display sales data to analyze quarter-over-quarter growth highlighting trends to help you evalulate business performance This website is using a security service to protect itself from online attacks The action you just performed triggered the security solution There are several actions that could trigger this block including submitting a certain word or phrase You can email the site owner to let them know you were blocked Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page bringing the platform’s performance and simplicity to the open table format The recent developments from Snowflake and Amazon S3 Tables with built-in Iceberg support provide organizations with a powerful new approach to data lake management you can leverage the platform’s elastic and performant compute use query semantics of typical Snowflake tables and interact with a single copy of data in S3 Tables that is interoperable across computing environments S3 Tables introduce purpose-built optimizations for Iceberg that deliver improved performance and automatic table maintenance right out of the box This approach gives organizations a more streamlined method to leverage open data standards combining the strengths of Snowflake’s data processing capabilities with S3 Tables we will walk through setting up Snowflake to register an external catalog and query the table Note: At the time of publishing this blog post the credential vending feature offered by Snowflake is in preview external catalogs are offered in a read-only mode by Snowflake The scope of these features are subject to change in the future This post will use the catalog integration for the SageMaker Lakehouse Iceberg REST endpoint with Signature Version 4 (SigV4) authentication in Snowflake Step 1: Log in to the Amazon S3 console and choose Table buckets from the navigation panel Select the “s3tables-snowflake-integration” bucket Step 3: Create a namespace called “testnamespace” Step 5: Create a table “daily_sales” through Athena Step 6: Insert sample rows into the “daily_sales” table using Athena we walk through the configuration needed for Snowflake to access this table First, we create an IAM role that Snowflake will assume to access AWS Glue and Lake Formation APIs To do this we create the following policy and role: Step 1: Create the policy and name it “irc-glue-lf-policy”. Here are some steps to do it through the AWS Management Console: In the policy editor choose JSON and paste the following policies and <database_name> in the following policy with your values We use “myblognamespace” as the database name in the rest of this post Step 2: Create a role named “snowflake_access_role” by following these steps in the IAM console choose Roles and choose the Create role option Choose Next and choose the policy you previously created in Step 1 Choose Next and enter “snowflake_access_role” as the role name The trust relationship for this role will be updated later you need to define access to this role using Lake Formation Note: If you use encryption for AWS Glue, you must modify the policy to add AWS Key Management Service (AWS KMS) permissions. For more information, see Setting up encryption in AWS Glue Step 1: We first start with the Application integration setup which allows third-party engines to access S3 tables enable full table access for external engines to access data Sign in as an data lake admin user and go to AWS Lake Formation Choose Application integration settings and select Allow external engines to access data in Amazon S3 locations with full table access we grant the following permissions to the snowflake_access_role on the resources as shown in the following table 2.1 In the Lake Formation console navigation pane select the radio button IAM users and roles also from the drop down select snowflake_access_role In the LF-Tags or catalog resources section Select <accountid>:s3tablescatalog/s3tables-snowflake-integration for Catalogs The permission configurations through Lake Formation and IAM are now complete Step 1: Login to Snowflake as admin user who has permission to create database and create catalog integration Step 2: Navigate to worksheet and run the following command to create database and catalog integration by providing following parameters Step 3: Follow these steps to get details to update the trust relationship of the role created to access table buckets through Snowflake (“snowflake_access_role”) Step 4: Verify the catalog integration using the command below Step 5: Run the below command to mount the S3 table as a Snowflake table Step 1: Login to Snowflake as admin user who has permission to use catalog integration created Step 2: Run the following command to query the table “s3tables_dailysales” on the “S3tables” bucket we’ve looked at connecting your Snowflake environment to query S3 Tables using the SageMaker Lakehouse Iceberg REST endpoint Combining Snowflake and AWS gives you multiple options to build out a transactional data lake for analytical and other use cases such as data sharing and collaboration Aritra Gupta is a Senior Technical Product Manager on the Amazon S3 team at Amazon Web Services He helps customers build and scale data lakes he likes to play chess and badminton in his spare time Jeemin Sim is a Product Manager at Snowflake focused on simplifying data architecture and helping organizations unlock the full potential of open formats with Snowflake Jeemin also enjoys eating delicious food and spending time with her orange cat Deepmala Agarwal works as an AWS Data Specialist Solutions Architect She is passionate about helping customers build out scalable Frank Dallezotte is a Senior Solutions Architect at AWS and is passionate about working with independent software vendors to design and build scalable applications on AWS and deploying these solutions in the cloud Srividya Parthasarathy is a Senior Big Data Architect on the AWS Lake Formation team She works with product team and customer to build robust features and solutions for their analytical data platform She enjoys building data mesh solutions and sharing them with the community (SNOW) currently trades approximately 12% beneath its intrinsic value suggesting it might be fairly valued in the current market a challenging element looms with an anticipated earnings decline of -5.2% injecting some uncertainty into its future performance investors are urged to assess how market fluctuations could affect the volatility of SNOW shares before making any portfolio adjustments The consensus from 47 brokerage firms places Snowflake Inc. (SNOW, Financial) at an average recommendation rating of 2.0 where 1 equates to a Strong Buy and 5 signals a Sell Alight Capital Management LP reduced its position in Snowflake Inc. (NYSE:SNOW - Free Report) by 28.6% in the fourth quarter according to its most recent disclosure with the Securities and Exchange Commission The firm owned 50,000 shares of the company's stock after selling 20,000 shares during the quarter Snowflake comprises about 2.5% of Alight Capital Management LP's holdings Alight Capital Management LP's holdings in Snowflake were worth $7,720,000 at the end of the most recent reporting period Several other hedge funds also recently bought and sold shares of SNOW Meiji Yasuda Life Insurance Co lifted its holdings in shares of Snowflake by 12.4% in the 4th quarter Meiji Yasuda Life Insurance Co now owns 7,965 shares of the company's stock worth $1,230,000 after acquiring an additional 880 shares during the last quarter bought a new stake in Snowflake in the fourth quarter worth $31,000 BlueCrest Capital Management Ltd purchased a new stake in Snowflake during the fourth quarter valued at about $1,421,000 Eagle Strategies LLC purchased a new stake in Snowflake during the fourth quarter valued at about $878,000 Freestone Grove Partners LP bought a new position in shares of Snowflake during the fourth quarter valued at about $49,072,000 Several analysts have weighed in on SNOW shares Cantor Fitzgerald reiterated an "overweight" rating and issued a $183.00 price objective on shares of Snowflake in a research report on Wednesday Evercore ISI boosted their price target on Snowflake from $190.00 to $200.00 and gave the company an "outperform" rating in a report on Wednesday Daiwa Capital Markets initiated coverage on Snowflake in a report on Tuesday They set a "buy" rating and a $210.00 target price for the company Piper Sandler dropped their target price on shares of Snowflake from $215.00 to $175.00 and set an "overweight" rating for the company in a research report on Wednesday DA Davidson reduced their price target on shares of Snowflake from $225.00 to $200.00 and set a "buy" rating on the stock in a research report on Monday Ten analysts have rated the stock with a hold rating the company has a consensus rating of "Moderate Buy" and an average price target of $200.28 View Our Latest Stock Report on Snowflake 7.80% of the stock is currently owned by corporate insiders SNOW stock traded up $0.21 during trading on Monday 2,493,546 shares of the stock traded hands The business's 50 day moving average price is $153.46 and its 200-day moving average price is $157.49 The company has a debt-to-equity ratio of 0.77 a current ratio of 1.88 and a quick ratio of 1.88 The firm has a market capitalization of $55.41 billion ShareSaveCommentInnovationAISnowflake CEO Says AI ROI Starts With Getting The Data RightByKolawole Samuel Adebayo Forbes contributors publish independent expert analyses and insights I write about the economics of AI.Follow AuthorApr 16 11:00am EDTShareSaveCommentSridhar Ramaswamy- Snowflake CEO (Photo Credit- Snowflake) Everyone wants AI that works like magic “AI should not be a Big Bang,” the Snowflake CEO told me in a sit-down “It should be a series of little projects that show value every step of the way.” But as Ramaswamy noted Ramaswamy laid out a simple but radical roadmap for enterprise AI “Don’t start with flashy demos or massive model investments,” he said Ask 10 vendors to define “agentic AI” and you’ll likely get 10 different answers But when I asked Ramaswamy what he really thought about agentic AI his response was that we must move past semantics into doing actual work that makes AI work indeed What Ramaswamy sees is the growing desire for AI that not only retrieves and summarizes From automating pre-meeting research to updating internal systems agentic AI promises to reduce the time humans spend stitching data across platforms But that only works if the data is accessible connected and trustworthy in the first place “Step one is making information easier to access,” he explained Step three is chaining those components together he warned that enterprises can’t skip the groundwork That’s one important message that industry experts are starting to propagate across the industry today especially since it’s easy to think of AI as a magical wand that just makes all your problems disappear “the potential of AI can be tapped only when it is thoughtfully merged into the very core of the organisation’s functions.” As companies scramble to keep pace with AI trends many make a costly misstep: starting with the model instead of the mission Snowflake’s own internal example — a lightweight chat interface for its sales enablement content — is a case in point “It didn’t cost a lot of money to build,” Ramaswamy said That told us we were onto something worth growing.” The phrase “AI is only as good as its data” gets repeated often But what does that actually mean for the modern enterprise where more than 100 SaaS apps are in use across the company the answer is that unless your data is unified What that implies is that you can’t successfully deploy AI or extract actual value from your AI projects you can’t even run a proper dashboard without integrating data from different sources — like Workday ”you definitely can’t build a useful AI application.” The challenge is deeper than business intelligence Most external tools like ChatGPT or Gemini have no access to a company’s internal systems They can’t pull consumption metrics or sales rep activity unless those systems are centralized and accessible “That’s why data readiness isn’t just a technical project,” he noted “It’s the foundation of whether your AI investments will even work.” Ramaswamy believes that AI will redefine how SaaS tools function at a core level “Most SaaS applications were built to help humans be more efficient,” he explained “But the future is software that can actually handle a good chunk of the work itself.” That shift — from decision support to decision execution — is why BI tools dashboards and even customer support platforms will evolve rapidly the number of people who can directly query business data will expand beyond analysts and data teams “This technology will let anyone who understands the business ask questions,” he said Instead, he talked about malleability — the mindset to experiment, stay curious and question AI’s output. “It’s the ability to understand what’s possible and what’s fanciful,” he said. “To try new things, but also to be critical when something doesn’t look right. That’s more important than any single technical skill.” It’s also how Ramaswamy stays grounded. He still tests AI agents personally, building simple use cases just to keep his intuition sharp. “You need to live and breathe this stuff,” he noted. “It’s the only way to separate hype from reality.” As Snowflake doubles down on being an end-to-end data and AI platform — not just a warehouse — Ramaswamy sees clarity in its role. “In a world where AI is thriving, Snowflake will thrive,” he said. “Because we are the layer underneath that powers this data access.” The future may belong to agentic AI, outcome-first SaaS and open-source pressure on inference pricing. But none of that matters if enterprises can’t get their data act together. The AI promise begins — and sometimes ends — with what you feed it. ready-to-use data directly from thousands of data sets and apps via Snowflake Marketplace—all without having to build pipelines.  then execute with Snowflake’s multi-cluster compute No separate infrastructure required.  streaming and batch systems are typically complex to manage and costly to scale But Snowflake keeps things simple by handling both streaming and batch data ingestion and transformation in a single system.  Stream row-set data in near real time with single-digit latency using Snowpipe Streaming Both options are serverless for better scalability and cost-efficiency you can use SQL or Python to declaratively define data transformations Snowflake will manage the dependencies and automatically materialize results based on your freshness targets Dynamic Tables can operate only on data that has changed since the last refresh to make high data volumes and complex pipelines simpler and more cost-efficient you can easily adapt by making a batch pipeline into a streaming pipeline with a single latency parameter change Bring your workloads to the data to streamline pipeline architecture and eliminate the need for separate infrastructure.  Bring your code to the data to fuel a variety of business needs—from accelerating analytics to building apps to unleashing the power of generative AI and LLMs this code can be in whichever language you prefer Run Python and other programming code next to your data in Snowflake to build data pipelines Automatically push down processing in multi-lingual runtimes built right into Snowflake’s elastic compute engine you’ll have a vast network of data and applications at your fingertips Easily access and distribute data and applications with direct access to live data sets from Snowflake Marketplace simply use Snowflake’s native connectors to bring data in frictionlessly By migrating to Snowpark for their data engineering needs, Openstore now processes 20x more data while reducing operational burden and achieving 100% PySpark code parity. Reduction in engineering maintenance hours required All the data engineering resources you need to build pipelines with Snowflake. Get up and running quickly with Snowflake tutorials for data engineering. Virtual Hands-On LabJoin an instructor-led, virtual hands-on lab to learn how to build data pipelines with Snowflake. Start your 30-DayFree TrialTry Snowflake free for 30 days and experience the AI Data Cloud that helps eliminate the complexity 1Data Source: Snowpark customer results Build powerful streaming and batch data pipelines in SQL or Python apps and analytics and see 4.6x faster performance while maintaining full governance and control Build streaming and batch data pipelines on a single platform with the power of declarative pipelines and cost-efficient incremental refresh.   By migrating to Snowpark for their data engineering needs Openstore now processes 20x more data while reducing operational burden and achieving 100% PySpark code parity All the data engineering resources you need to build pipelines with Snowflake Get up and running quickly with Snowflake tutorials for data engineering virtual hands-on lab to learn how to build data pipelines with Snowflake 1Data Source: Snowpark customer results the next iteration of our frontier embedding models While our previous releases have been well received by our customers we have consistently received one request: Can you make this model multilingual Arctic Embed 2.0 builds on the robust foundation of our previous releases adding multilingual support without sacrificing English performance or scalability to address the needs of an even broader user base that spans a wide range of languages and applications Single-vector dense retrieval performance of open source multilingual embedding models with fewer than 1B parameters Scores are average nDCG@10 on MTEB Retrieval and the subset of CLEF (ELRA By carefully balancing multilingual needs with Snowflake’s commitment to excellence in English retrieval we built Arctic Embed 2.0 to be a universal workhorse useful for a wide range of global use cases all qualitative evaluations refer to the average NDCG@10 score across tasks the Arctic Embed 2.0 models are released under the permissive Apache 2.0 license these models support applications across verticals with reliable multilingual embeddings that generalize well We hypothesize that some earlier open source model developers may have inadvertently tuned their training recipes too aggressively toward improved MIRACL performance at the cost of generality possibly by overfitting the MIRACL training data For more details about how the Arctic Embed 2.0 models were trained and what we learned in the process look out for the forthcoming technical report As seen in Tables 2 and 3, several popular open source models score on par with Arctic Embed L 2.0 on the in-domain MIRACL evaluation but fall short on the out-of-domain CLEF evaluation We also benchmarked popular closed-source models like OpenAI’s text-embedding-3-large model and found that Arctic L 2.0’s performance is in line with leading proprietary models As seen in Table 4, existing open source multilingual models also score worse than Arctic Embed L 2.0 on the popular English-language MTEB Retrieval benchmark forcing users who seek to support multiple languages to choose between lower English-retrieval quality or more operational complexity from using a second model just for English retrieval practitioners are now able to switch to a single open source model without sacrificing English-language retrieval quality A comparison of several top open and closed source multilingual retrieval models on the in-domain MTEB Retrieval Benchmark Snowflake continues to prioritize efficiency and scale in its embedding model design. With Arctic Embed L 2.0 users can pack the quality characteristic of larger models into compact embeddings requiring as little as 128 bytes per vector for storage This makes it possible to serve retrieval over millions of documents at a lower cost on low-end hardware We achieve efficiency in embedding throughput as well by squeezing Arctic Embed 2.0’s impressive retrieval quality into just over 100M and 300M non-embedding parameters in its two sizes (medium and large) respectively — just a slight increase from our earlier English-only versions Indeed, the scale-focused regime is where Arctic Embed L 2.0 truly shines, achieving better quality under compression than other MRL-trained models, such as OpenAI’s text-embedding-3-large. Table 5. A comparison of OpenAI’s text-embedding-3-large performance with truncated embeddings compared to Arctic Embed L 2.0 on English only (MTEB Retrieval) and multilingual (CLEF). With Arctic Embed 2.0, Snowflake sets a new standard for multilingual, efficient embedding models. Additionally, we make the frontier of text-embedding quality not only efficient but permissively open sourced as well. Whether your goal is to expand reach to multilingual users, reduce storage costs or embed documents on accessible hardware, Arctic Embed 2.0 offers capabilities and flexibility to meet your needs.  Our soon-to-be-released technical report will dive deeper into the innovations behind Arctic Embed 2.0. In the meantime, we invite you to start embedding with Snowflake today. 1 This calculation uses float32 format for the uncompressed baseline, i.e. 3,072 numbers of 32 bits each for a total of 98,304 bits per vector, exactly 96x larger than the 1,024 bits per vector (equivalent to 128 bytes per vector) used when storing MRL-truncated 256-dimensional vectors from the Arctic Embed 2.0 models in int4 format. Snowflake is excited to announce the release of Arctic Embed L 2.0 and Arctic Embed M 2.0 As seen in Tables 2 and 3, several popular open source models score on par with Arctic Embed L 2.0 on the in-domain MIRACL evaluation but fall short on the out-of-domain CLEF evaluation As seen in Table 4, existing open source multilingual models also score worse than Arctic Embed L 2.0 on the popular English-language MTEB Retrieval benchmark Snowflake continues to prioritize efficiency and scale in its embedding model design. With Arctic Embed L 2.0 Indeed, the scale-focused regime is where Arctic Embed L 2.0 truly shines achieving better quality under compression than other MRL-trained models A comparison of OpenAI’s text-embedding-3-large performance with truncated embeddings compared to Arctic Embed L 2.0 on English only (MTEB Retrieval) and multilingual (CLEF) Snowflake sets a new standard for multilingual we make the frontier of text-embedding quality not only efficient but permissively open sourced as well Whether your goal is to expand reach to multilingual users reduce storage costs or embed documents on accessible hardware Arctic Embed 2.0 offers capabilities and flexibility to meet your needs Our soon-to-be-released technical report will dive deeper into the innovations behind Arctic Embed 2.0 we invite you to start embedding with Snowflake today 1 This calculation uses float32 format for the uncompressed baseline 3,072 numbers of 32 bits each for a total of 98,304 bits per vector exactly 96x larger than the 1,024 bits per vector (equivalent to 128 bytes per vector) used when storing MRL-truncated 256-dimensional vectors from the Arctic Embed 2.0 models in int4 format BlueCrest Capital Management Ltd bought a new position in Snowflake Inc. (NYSE:SNOW - Free Report) during the 4th quarter according to its most recent Form 13F filing with the Securities & Exchange Commission The fund bought 9,200 shares of the company's stock Other institutional investors and hedge funds have also bought and sold shares of the company Eagle Strategies LLC purchased a new stake in shares of Snowflake during the fourth quarter valued at $878,000 Freestone Grove Partners LP bought a new stake in shares of Snowflake in the fourth quarter worth approximately $49,072,000 Triumph Capital Management raised its holdings in shares of Snowflake by 30.3% during the fourth quarter Triumph Capital Management now owns 7,706 shares of the company's stock valued at $1,190,000 after purchasing an additional 1,791 shares during the last quarter Comerica Bank grew its holdings in shares of Snowflake by 3.9% in the fourth quarter Comerica Bank now owns 20,763 shares of the company's stock worth $3,206,000 after acquiring an additional 781 shares during the period Utah Retirement Systems boosted its position in Snowflake by 0.3% in the fourth quarter Utah Retirement Systems now owns 33,922 shares of the company's stock worth $5,238,000 after purchasing an additional 100 shares during the last quarter 65.10% of the stock is currently owned by institutional investors Cantor Fitzgerald reiterated an "overweight" rating and issued a $183.00 price target on shares of Snowflake in a research report on Wednesday Barclays boosted their price objective on Snowflake from $190.00 to $203.00 and gave the company an "overweight" rating in a research note on Thursday Bank of America lifted their target price on shares of Snowflake from $185.00 to $205.00 and gave the company a "neutral" rating in a research note on Thursday UBS Group boosted their price objective on Snowflake from $190.00 to $200.00 and gave the company a "neutral" rating in a research report on Thursday Canaccord Genuity Group boosted their price target on Snowflake from $190.00 to $220.00 and gave the stock a "buy" rating in a report on Tuesday twenty-nine have given a buy rating and two have issued a strong buy rating to the company the stock currently has an average rating of "Moderate Buy" and a consensus target price of $200.28 View Our Latest Stock Analysis on Snowflake The company had a trading volume of 2,492,670 shares compared to its average volume of 6,399,825 a price-to-earnings ratio of -49.52 and a beta of 1.11 The firm has a fifty day simple moving average of $153.46 and a 200 day simple moving average of $157.49 has a fifty-two week low of $107.13 and a fifty-two week high of $194.40 a quick ratio of 1.88 and a current ratio of 1.88 insiders have sold 361,560 shares of company stock worth $59,253,850 MarketBeat's analysts have just released their top five short plays for May 2025 Learn which stocks have the most short interest and how to trade them Enter your email address to see which companies made the list Bienville Capital Management LLC reduced its position in Snowflake Inc. (NYSE:SNOW - Free Report) by 50.9% in the fourth quarter The fund owned 46,578 shares of the company's stock after selling 48,217 shares during the period Snowflake comprises approximately 1.8% of Bienville Capital Management LLC's holdings making the stock its 22nd largest position Bienville Capital Management LLC's holdings in Snowflake were worth $7,192,000 at the end of the most recent quarter Several other institutional investors also recently modified their holdings of SNOW Norges Bank bought a new stake in Snowflake during the fourth quarter valued at approximately $988,950,000 GQG Partners LLC bought a new stake in Snowflake during the fourth quarter valued at about $750,572,000 Jennison Associates LLC increased its position in Snowflake by 92.2% in the fourth quarter Jennison Associates LLC now owns 7,637,006 shares of the company's stock worth $1,179,230,000 after buying an additional 3,662,671 shares in the last quarter FMR LLC raised its stake in shares of Snowflake by 84.9% in the fourth quarter FMR LLC now owns 6,108,123 shares of the company's stock worth $943,155,000 after buying an additional 2,805,425 shares during the period MD grew its stake in shares of Snowflake by 124.4% during the 4th quarter MD now owns 3,968,755 shares of the company's stock valued at $612,817,000 after acquiring an additional 2,200,406 shares during the period 65.10% of the stock is owned by hedge funds and other institutional investors Shares of NYSE:SNOW traded up $0.21 during trading hours on Monday The stock had a trading volume of 2,493,546 shares has a 1 year low of $107.13 and a 1 year high of $194.40 The firm's 50 day moving average is $153.46 and its 200-day moving average is $157.49 Several brokerages have weighed in on SNOW Jefferies Financial Group lowered their price target on shares of Snowflake from $220.00 to $190.00 and set a "buy" rating on the stock in a report on Monday Cantor Fitzgerald reissued an "overweight" rating and set a $183.00 price target on shares of Snowflake in a report on Wednesday Royal Bank of Canada upped their price objective on Snowflake from $210.00 to $221.00 and gave the stock an "outperform" rating in a research note on Thursday BTIG Research raised Snowflake from a "neutral" rating to a "buy" rating and set a $220.00 price target on the stock in a report on Thursday Macquarie began coverage on Snowflake in a research note on Wednesday They issued a "neutral" rating and a $160.00 price target for the company twenty-nine have assigned a buy rating and two have assigned a strong buy rating to the company's stock the stock presently has an average rating of "Moderate Buy" and a consensus target price of $200.28 Get Our Latest Report on Snowflake Insiders have sold 371,963 shares of company stock valued at $61,001,558 over the last quarter With the proliferation of data centers and electric vehicles the electric grid will only get more strained Download this report to learn how energy stocks can play a role in your portfolio as the global demand for energy continues to grow The actor appears in a series of commercials for Salesforce  (CRM) , including one where McConaughey is seated at a restaurant's outdoor patio in the pouring rain because a booking app without an AI agent handled his preferences 💵💰Don't miss the move: Subscribe to TheStreet's free daily newsletter 💰 It was looking bad for the star of "The Wolf of Wall Street," "True Detective" and "The Lincoln Lawyer," but fortunately fellow actor Woody Harrelson offers McConaughey a seat at his much drier table across the street While McConaughey escaped the rotten weather, analysts sees dark clouds on the horizon for the software sector, a list that includes such names as Salesforce, data analytics software company Snowflake  (SNOW)  and e-commerce giant Amazon  (AMZN) DA Davidson analyst Gil Luria lowered the firm's price target on Salesforce to $250 from $275 and affirmed a neutral rating on the shares He pared his price target on Snowflake to $200 from $225 and maintained a buy rating Analysts recently revised their price target for the firm's shares Bloomberg/Getty Images The price target revisions are part of a broader research note updating estimates within the firm's coverage of the software group Software and services from Salesforce and Snowflake both sit in the cloud and focus on data management Salesforce is primarily a customer relationship management platform while Snowflake is a data warehouse that offers scalable and secure data storage for analytics DA Davidson: Weaker economic growth aheadDA Davidson said that it was now assuming a base case of one or two quarters of negative GDP in the US this year which would translate to lower growth and has already translated to lower valuations The firm noted that regardless of how the current tariff regime plays out it sees consumer activity and corporate investment slowing The tech sector was excluded from President Donald Trump's sweeping tariff agenda, but Commerce Secretary Howard Lutnick said the plan to exempt electronic devices — like smartphones iPhones and laptops — from tariffs was a temporary reprieve and these products would face separate levies the Trump administration is reportedly kicking off investigations into imports of pharmaceuticals and semiconductors as part of a bid to impose tariffs on both sectors on national security grounds citing notices posted to the Federal Register DA Davidson also cut its price target on Amazon to $230 from $280 and kept a buy rating on the shares Amazon is reportedly reaching out directly to sellers for input on how Trump’s tariffs are affecting their businesses, suggesting the e-commerce giant is gathering data as sellers rethink pricing, inventory and more, according to Modern Retail Analyst cites dense fog in current economy  Meanwhile Wedbush analysts were also pulling back on their forecasts due to tariff concerns and sagging consumer confidence significant uncertainty has been introduced to the economy given ongoing macro concerns and weaker consumer confidence levels both in the US and internationally," the firm said in an April 15 note about the internet sector Wedbush said the tech-heavy Nasdaq was down about 13% year-to-date and the dollar had depreciated since management teams last provided guidance “implying a potential tailwind to first-half 2025 results for several global companies under our coverage.” Early first-quarter earnings reports from retailers contemplate a more cautious consumer in the US and certain travel operators have pulled full-year guidance recent corporate commentary has been consistent with the neutral / negative sentiment we observed in our latest consumer internet and digital advertising surveys," Wedbush said "reflecting limited visibility into current economic conditions and capturing the potential implications of a weaker demand environment." "We will continue to monitor the situation and revise estimates further as we hear from management teams in the coming weeks to gain better clarity than the current dense fog," Wedbush analysts said president of Wells Fargo Investment Institute said that equity prices started the week on a high note as the countertrend rally was holding with limited news over the weekend and Monday “We believe the combined weight of universal, reciprocal, retaliatory, and sector-specific tariffs ultimately will decide between recession and no recession but that the final tariff menu remains unsettled," Cronk said in a research note.  The analyst said the firm saw this as an opportunity for long-term investors to add exposure to high-quality U.S Want TheStreet’s best daily stock and investing news right in your inbox every weekday Sign up today for our free newsletter and you'll receive an exclusive report explaining hedge fund guru Doug Kass' winning investment style AWS’s container-optimized operating system emerged as the ideal candidate to address these challenges The migration was executed in a phased manner to minimize risks and ensure stability The migration began with cluster preparation. Bottlerocket AMIs were integrated into the EKS environment by modifying the NodePool and NodeClass configurations to use Bottlerocket as the default AMI family AWS Identity and Access Management (IAM) policies were optimized to align with Bottlerocket’s security model following the principle of least privilege This architectural diagram visualizes the migration strategy: Karpenter deployment replaced the traditional static provisioning approach, enabling just-in-time node provisioning. Workload validation followed, with the staging environment used to test workloads on Bottlerocket nodes before production rollout. Performance monitoring was implemented using Fluentd and Datadog to track real-time metrics and security compliance tests ensured that Bottlerocket’s immutable infrastructure aligned with Snowflake Corporate’s security policies and categories were used to ensure optimal workload distribution A gradual introduction of Bottlerocket nodes ensured workloads transitioned smoothly alongside existing AL2 instances Node cordoning and draining helped decommission AL2 instances without service interruptions enhanced monitoring and optimization were implemented Automated scaling with Karpenter dynamically adjusted the cluster’s node pool Performance tuning was conducted based on real-world workloads and observability improvements provided insights into system health Example of defining a NodeClass and associating it with a NodePool: the migration process presented challenges Some workloads initially experienced incompatibilities with Bottlerocket’s immutable filesystem This was resolved by modifying application images to be fully container-compliant and leveraging read-only configurations where applicable Bottlerocket required reconfiguring IAM roles to align with its security model which was resolved by implementing fine-grained access controls and leveraging Karpenter’s IAM integration ensuring that application performance remained stable before fully decommissioning AL2 nodes The migration delivered substantial improvements in security Security was enhanced with immutable nodes preventing unauthorized changes and eliminating configuration drift atomic updates ensured nodes remained securely patched without downtime Faster node boot times were achieved with optimized node startup reducing the time required for new nodes to join the cluster and improved autoscaling efficiency ensured workloads were rescheduled quickly Operational efficiency was improved with dynamic scaling by Karpenter ensuring resources were provisioned only when needed Cost savings were realized through Bottlerocket’s lightweight OS and Karpenter’s intelligent provisioning Bottlerocket consistently demonstrated faster node readiness Preliminary benchmarks showed that Bottlerocket reduced node readiness time by approximately 5 seconds compared to AL2 The native container image caching shaved off about 36 seconds per pod on a fresh node making unschedulable pods approximately 40 seconds faster compared to AL2 A direct comparison of security improvements highlights why Bottlerocket was the superior choice: with Bottlerocket’s immutable design strengthening Snowflake Corporate’s security posture as Karpenter’s real-time scaling eliminated manual interventions and phased rollouts allowed for fine-tuning configurations without production impact Snowflake Corporate not only enhanced its security posture but also achieved performance improvements through dynamic scaling underscoring the power of modern cloud-native solutions in enabling high-performance Sameeksha is a Technical Account Manager at AWS committed to accelerate the cloud journey for AWS Global Enterprise customers She has 7+ years of industry experience across cloud security cloud infrastructure management and customer advocacy She is passionate about cloud security technologies and strives to help customers secure their workloads in the cloud Gaurav Singodia is a high-tech engineering leader at Snowflake with a proven track record of driving innovation and growth through an entrepreneurial mindset He currently leads a diverse global organization encompassing SRE with a strong focus on maintaining high quality and achieving scalability across all domains Jagdish Pawar has over 18 years of leadership experience across technology startups His expertise includes building and leading cross-functional teams RK Sai (Ravikiran Koduri) is an Enterprise Support Lead at AWS he helps Independent Software Vendors (ISVs) operationalize workloads at scale RK Sai is an evangelist for AWS Deep Racer he strives to concretize an abstract sense of fulfillment Sayan Moitra is a Senior DevOps engineer who specializes in cloud engineering specializing in deploying infrastructure and applications He holds multiple AWS certifications and CKAD with recognized expertise in serverless computing He's passionate about continuous learning and solving complex problems Schonfeld Strategic Advisors LLC lifted its position in Snowflake Inc. (NYSE:SNOW - Free Report) by 335.2% during the fourth quarter according to its most recent 13F filing with the Securities and Exchange Commission (SEC) The firm owned 49,259 shares of the company's stock after acquiring an additional 37,940 shares during the period Schonfeld Strategic Advisors LLC's holdings in Snowflake were worth $7,606,000 at the end of the most recent quarter Several other hedge funds and other institutional investors have also bought and sold shares of SNOW Norges Bank acquired a new stake in Snowflake in the fourth quarter worth $988,950,000 GQG Partners LLC bought a new stake in shares of Snowflake in the 4th quarter valued at $750,572,000 Jennison Associates LLC raised its stake in Snowflake by 92.2% during the 4th quarter Jennison Associates LLC now owns 7,637,006 shares of the company's stock worth $1,179,230,000 after buying an additional 3,662,671 shares during the period FMR LLC lifted its holdings in Snowflake by 84.9% during the fourth quarter FMR LLC now owns 6,108,123 shares of the company's stock worth $943,155,000 after acquiring an additional 2,805,425 shares in the last quarter MD grew its stake in Snowflake by 124.4% in the fourth quarter Institutional investors own 65.10% of the company's stock Several research firms have recently weighed in on SNOW Truist Financial cut their price objective on Snowflake from $225.00 to $210.00 and set a "buy" rating on the stock in a report on Monday Needham & Company LLC lifted their price target on shares of Snowflake from $200.00 to $215.00 and gave the company a "buy" rating in a research note on Thursday Jefferies Financial Group lowered their price objective on shares of Snowflake from $220.00 to $190.00 and set a "buy" rating for the company in a research report on Monday Mizuho boosted their price objective on shares of Snowflake from $195.00 to $205.00 and gave the company an "outperform" rating in a research report on Wednesday Daiwa Capital Markets began coverage on shares of Snowflake in a research note on Tuesday They issued a "buy" rating and a $210.00 target price for the company twenty-nine have given a buy rating and two have assigned a strong buy rating to the stock Snowflake presently has a consensus rating of "Moderate Buy" and an average price target of $200.28 Read Our Latest Stock Analysis on Snowflake NYSE:SNOW traded up $0.21 during mid-day trading on Monday 2,493,546 shares of the stock were exchanged compared to its average volume of 6,399,829 The firm's 50-day simple moving average is $153.46 and its 200 day simple moving average is $157.49 insiders sold 361,560 shares of company stock valued at $59,253,850 Almost everyone loves strong dividend-paying stocks Discover 20 high-yield dividend stocks paying an unsustainably large percentage of their earnings Enter your email to get this report and avoid a high-yield dividend trap Generative AI is changing the demands on our data platforms Enterprises need a platform that is easy to use and scalable maintenance and performance improvements that help them launch projects and products faster Snowflake commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study to find the potential ROI enterprises may get by deploying Snowflake They spoke with four Snowflake customers and found: Forrester Consulting study commissioned by Snowflake Northern Trust Corp increased its position in Snowflake Inc. (NYSE:SNOW - Free Report) by 18.0% in the fourth quarter according to the company in its most recent Form 13F filing with the Securities and Exchange Commission (SEC) The fund owned 2,141,468 shares of the company's stock after buying an additional 326,104 shares during the period Northern Trust Corp owned about 0.65% of Snowflake worth $330,664,000 as of its most recent SEC filing Several other institutional investors and hedge funds have also modified their holdings of SNOW increased its holdings in shares of Snowflake by 47.4% during the 3rd quarter now owns 12,344 shares of the company's stock worth $1,438,000 after buying an additional 3,971 shares during the last quarter Wilmington Savings Fund Society FSB raised its holdings in shares of Snowflake by 50.3% in the 3rd quarter Wilmington Savings Fund Society FSB now owns 4,659 shares of the company's stock worth $535,000 after purchasing an additional 1,559 shares during the period Blankinship & Foster LLC purchased a new stake in shares of Snowflake during the 3rd quarter worth about $896,000 Synovus Financial Corp increased its position in Snowflake by 28.6% during the 3rd quarter Synovus Financial Corp now owns 6,077 shares of the company's stock worth $698,000 after purchasing an additional 1,352 shares in the last quarter raised its holdings in Snowflake by 1.4% in the third quarter now owns 536,588 shares of the company's stock valued at $66,086,000 after buying an additional 7,373 shares during the period Shares of Snowflake stock traded up $0.21 during trading on Monday The stock had a trading volume of 1,839,684 shares compared to its average volume of 6,398,740 The business has a fifty day moving average of $153.46 and a two-hundred day moving average of $157.49 The firm has a market cap of $55.41 billion Several equities research analysts have recently issued reports on the stock Piper Sandler dropped their price objective on shares of Snowflake from $215.00 to $175.00 and set an "overweight" rating for the company in a research report on Wednesday Cantor Fitzgerald restated an "overweight" rating and set a $183.00 price objective on shares of Snowflake in a research note on Wednesday KeyCorp lowered their target price on Snowflake from $220.00 to $192.00 and set an "overweight" rating for the company in a research note on Thursday Barclays increased their price objective on shares of Snowflake from $190.00 to $203.00 and gave the company an "overweight" rating in a research report on Thursday Stifel Nicolaus boosted their target price on shares of Snowflake from $187.00 to $210.00 and gave the stock a "buy" rating in a research report on Thursday twenty-nine have issued a buy rating and two have issued a strong buy rating to the company Snowflake currently has an average rating of "Moderate Buy" and an average target price of $200.28 Read Our Latest Stock Report on SNOW Almost everyone loves strong dividend-paying stocks, but high yields can signal danger. Discover 20 high-yield dividend stocks paying an unsustainably large percentage of their earnings. Enter your email to get this report and avoid a high-yield dividend trap. Days to build a new AI summarization model As one of the corporation’s key business units, Penske Logistics offers dedicated contract carriage, warehousing, transportation management and customized supply chain solutions to customers around the globe. With so many constantly moving parts, data is central to Penske Logistics, playing a critical role in optimizing routes, ensuring the seamless flow of goods and reducing disruptions.  The organization needed a way to manage all its performance indicators in a seamless, scalable way while creating a solid foundation on which to build new AI innovations. Thanks to Snowflake’s robust data platform and the generative AI capabilities through Cortex AI, Penske Logistics has now consolidated all performance indicators while successfully launching generative AI projects that have improved employee productivity and performance. Robust reporting for better decision-making: Thanks to the scalability and automation of the Snowflake platform, Penske now equips its leaders with rich reports spanning years for data comparisons in a matter of minutes.  Easy, efficient and trusted gen AI: After trying to gain traction with gen AI for a year, Penske found near-immediate success with Cortex AI thanks to its simplicity, ease of implementation and built-in security.  Penske Logistics is no stranger to driving competitive advantage through data. It was an early adopter of data science to enhance fleet operations and customer service, and the company was among the first to integrate electronic logging and telematics devices into its trucks to better understand driving data and improve safety and service levels.  To improve data capabilities, the data science team needed to move to a cloud-based, data lake-driven solution. Snowflake’s ease of use, scalability and built-in data science functionality quickly won the team over compared with competitors. Snowflake's platform-agnostic capabilities allowed the company to overcome the limitations of on-premises systems and achieve a broader view of the organization. “Snowflake has become a critical part of all that we do — not just for my team, but also for other teams in the organization. We have really benefited from the relationship and support from Snowflake’s team.” The leadership team now uses these reports to gain critical data insights and make more informed decisions. Every department of Penske Logistics makes use of data science through Snowflake, from operations to human resources. Data scientists spend less time accessing data, and Snowflake helps them create repeatable processes for handling and analyzing the data, improving consistency and productivity. “The efficiency we’ve gained with Snowflake has enabled us to grow faster. Based on our success, other areas of the Penske organization are also looking at ways they could leverage Snowflake.” As an added bonus, Ram says, Snowflake has become an incentive for potential prospects looking to join his team: “For data scientists, Snowflake makes our jobs much easier.” In keeping with its early tech adopter roots, Penske Logistics’ leaders wanted to harness the benefits of generative AI for their operations. To help provide managers with a comprehensive view of performance indicators, the data science team explored various AI solutions that could analyze and consolidate data to look for trends and proactively raise awareness to spark conversation. Snowflake Cortex AI solved these challenges. Because it hosts and serves large language models (LLMs) within Snowflake’s governed perimeter, Cortex AI made it easy and cost-effective for the team to securely bring LLMs to their data — without requiring additional approvals or investment for new AI vendors. “The game-changer in Snowflake Cortex AI is its simplicity and ease of implementation. Our data already sits in Snowflake, so we can make use of the LLMs without needing to use anything external. Everything is already set up for us. And the models are maintained and updated for us, ensuring their safety and security.” Safety, performance and retention are paramount for Penske Logistics. Managers aim to improve these areas with personalized insights and recommendations to their teams informed by massive amounts of data. However, insights from this data were not easily accessible and analysis was a manual process to parse through nearly 30 data columns in a dashboard. Now, Penske uses LLMs in Cortex AI to process performance indicators in record time and produce relative talking points and recommendations for each associate. These insights allow managers to guide conversations with more clarity, precision and actionable next steps. Since rolling out the solution, managers are enjoying productivity gains from reading a comprehensive paragraph, eliminating multiple sources and columns of data. The company is already seeing improvements in associate performance, safety and retention.   “A lot of organizations right now are struggling with whether generative AI is worth the expense and risk. Snowflake Cortex AI provides a solution that doesn’t require initial investment or risk, and that’s what has made our gen AI projects successful.” Penske turned to Snowflake’s AI platform to easily and securely harness the power of generative AI — delivering operational efficiency and improving associate safety and retention across two product lines Penske is a global leader in supply chain and logistics solutions Whether they’re coordinating a critical shipment or designing an entire network businesses rely on Penske’s top-notch services daily for timely deliveries with flawless execution.  As one of the corporation’s key business units Penske Logistics offers dedicated contract carriage transportation management and customized supply chain solutions to customers around the globe playing a critical role in optimizing routes ensuring the seamless flow of goods and reducing disruptions.  The organization needed a way to manage all its performance indicators in a seamless scalable way while creating a solid foundation on which to build new AI innovations Thanks to Snowflake’s robust data platform and the generative AI capabilities through Cortex AI Penske Logistics has now consolidated all performance indicators while successfully launching generative AI projects that have improved employee productivity and performance Robust reporting for better decision-making: Thanks to the scalability and automation of the Snowflake platform Penske now equips its leaders with rich reports spanning years for data comparisons in a matter of minutes.  efficient and trusted gen AI: After trying to gain traction with gen AI for a year Penske found near-immediate success with Cortex AI thanks to its simplicity ease of implementation and built-in security.  Penske Logistics is no stranger to driving competitive advantage through data It was an early adopter of data science to enhance fleet operations and customer service and the company was among the first to integrate electronic logging and telematics devices into its trucks to better understand driving data and improve safety and service levels.  Penske logs performance data across different sources the company found it challenging to effectively use this data due to limitations with its on-premises data solutions Reporting processes didn’t provide leadership with accessible visibility across the organization while business analysts relied on other departments to create business intelligence reports The reporting tool was limited to running in one or two locations using just a month’s worth of data — and would crash if used for extended periods.  the data science team needed to move to a cloud-based scalability and built-in data science functionality quickly won the team over compared with competitors Snowflake's platform-agnostic capabilities allowed the company to overcome the limitations of on-premises systems and achieve a broader view of the organization Penske Logistics has centralized data in a secure environment scaled its infrastructure to improve performance and immediately improved its reporting capabilities business analysts create BI reports with companywide data spanning five years in just 15 minutes analysts can use certified self-service tools like Tableau to build their own reports This has freed up the data engineering and analytics teams to focus on expanding service delivery and building out more value-driven features and capabilities.   The leadership team now uses these reports to gain critical data insights and make more informed decisions Every department of Penske Logistics makes use of data science through Snowflake Data scientists spend less time accessing data and Snowflake helps them create repeatable processes for handling and analyzing the data Snowflake has become an incentive for potential prospects looking to join his team: “For data scientists In keeping with its early tech adopter roots Penske Logistics’ leaders wanted to harness the benefits of generative AI for their operations To help provide managers with a comprehensive view of performance indicators the data science team explored various AI solutions that could analyze and consolidate data to look for trends and proactively raise awareness to spark conversation Snowflake Cortex AI solved these challenges Because it hosts and serves large language models (LLMs) within Snowflake’s governed perimeter Cortex AI made it easy and cost-effective for the team to securely bring LLMs to their data — without requiring additional approvals or investment for new AI vendors performance and retention are paramount for Penske Logistics Managers aim to improve these areas with personalized insights and recommendations to their teams informed by massive amounts of data insights from this data were not easily accessible and analysis was a manual process to parse through nearly 30 data columns in a dashboard Penske uses LLMs in Cortex AI to process performance indicators in record time and produce relative talking points and recommendations for each associate These insights allow managers to guide conversations with more clarity managers are enjoying productivity gains from reading a comprehensive paragraph eliminating multiple sources and columns of data The company is already seeing improvements in associate performance Penske Logistics plans to integrate Cortex AI’s capabilities into more areas of the business to supplement performance interactions and improve insights Machine learning models drive several list-oriented processes in the organization including getting feedback from warehouse associates and the data science team will use LLMs to enhance sentiment analysis the team plans to implement Document AI to automatically extract data from handwritten and printed logbooks improving operational efficiency and data accuracy Freestone Grove Partners LP bought a new position in Snowflake Inc. (NYSE:SNOW - Free Report) during the fourth quarter according to the company in its most recent 13F filing with the SEC The fund bought 317,801 shares of the company's stock Freestone Grove Partners LP owned about 0.10% of Snowflake as of its most recent SEC filing Stonebridge Financial Group LLC bought a new position in shares of Snowflake during the 4th quarter worth $29,000 Quadrant Capital Group LLC boosted its position in Snowflake by 74.6% during the fourth quarter Quadrant Capital Group LLC now owns 213 shares of the company's stock worth $33,000 after purchasing an additional 91 shares during the period Blue Bell Private Wealth Management LLC increased its stake in shares of Snowflake by 189.5% in the fourth quarter Blue Bell Private Wealth Management LLC now owns 220 shares of the company's stock worth $34,000 after purchasing an additional 144 shares in the last quarter Perkins Coie Trust Co raised its position in shares of Snowflake by 136.3% in the fourth quarter Perkins Coie Trust Co now owns 241 shares of the company's stock valued at $37,000 after purchasing an additional 139 shares during the period lifted its stake in shares of Snowflake by 1,437.5% during the 4th quarter now owns 246 shares of the company's stock valued at $38,000 after buying an additional 230 shares in the last quarter 65.10% of the stock is owned by institutional investors Company insiders own 7.80% of the company's stock Several research firms have issued reports on SNOW Piper Sandler dropped their target price on Snowflake from $215.00 to $175.00 and set an "overweight" rating on the stock in a research report on Wednesday DA Davidson lowered their price target on shares of Snowflake from $225.00 to $200.00 and set a "buy" rating on the stock in a report on Monday Bank of America boosted their price objective on shares of Snowflake from $185.00 to $205.00 and gave the company a "neutral" rating in a research note on Thursday Macquarie started coverage on shares of Snowflake in a research note on Wednesday They issued a "neutral" rating and a $160.00 target price on the stock KeyCorp decreased their price target on shares of Snowflake from $220.00 to $192.00 and set an "overweight" rating on the stock in a report on Thursday Ten equities research analysts have rated the stock with a hold rating twenty-nine have given a buy rating and two have given a strong buy rating to the stock the company currently has a consensus rating of "Moderate Buy" and a consensus price target of $200.28 Get Our Latest Research Report on SNOW Shares of NYSE:SNOW traded up $0.21 during midday trading on Monday 2,460,238 shares of the company's stock traded hands compared to its average volume of 6,399,700 has a one year low of $107.13 and a one year high of $194.40 The stock has a 50 day moving average of $153.46 and a 200 day moving average of $157.49 The company has a market cap of $55.41 billion Which stocks are likely to thrive in today's challenging market Enter your email address and we'll send you MarketBeat's list of ten stocks that will drive in any economic environment Eagle Strategies LLC acquired a new stake in shares of Snowflake Inc. (NYSE:SNOW - Free Report) in the fourth quarter according to the company in its most recent filing with the SEC The firm acquired 5,684 shares of the company's stock Other large investors also recently added to or reduced their stakes in the company Stonebridge Financial Group LLC bought a new stake in shares of Snowflake in the 4th quarter valued at about $29,000 Quadrant Capital Group LLC boosted its stake in Snowflake by 74.6% in the fourth quarter Quadrant Capital Group LLC now owns 213 shares of the company's stock valued at $33,000 after acquiring an additional 91 shares in the last quarter Blue Bell Private Wealth Management LLC grew its holdings in Snowflake by 189.5% in the fourth quarter Blue Bell Private Wealth Management LLC now owns 220 shares of the company's stock worth $34,000 after purchasing an additional 144 shares during the last quarter Perkins Coie Trust Co increased its position in shares of Snowflake by 136.3% during the fourth quarter Perkins Coie Trust Co now owns 241 shares of the company's stock worth $37,000 after purchasing an additional 139 shares in the last quarter lifted its holdings in shares of Snowflake by 1,437.5% during the 4th quarter now owns 246 shares of the company's stock valued at $38,000 after purchasing an additional 230 shares during the last quarter Several research analysts recently issued reports on SNOW shares Evercore ISI upped their target price on Snowflake from $190.00 to $200.00 and gave the company an "outperform" rating in a research report on Wednesday Wedbush set a $210.00 price objective on Snowflake in a report on Wednesday Wells Fargo & Company decreased their price target on Snowflake from $215.00 to $200.00 and set an "overweight" rating for the company in a report on Tuesday Macquarie began coverage on shares of Snowflake in a research note on Wednesday They set a "neutral" rating and a $160.00 price objective on the stock Citigroup raised their price target on shares of Snowflake from $230.00 to $235.00 and gave the company a "buy" rating in a research report on Tuesday twenty-nine have issued a buy rating and two have given a strong buy rating to the stock the company has a consensus rating of "Moderate Buy" and a consensus price target of $200.28 Check Out Our Latest Research Report on SNOW The stock had a trading volume of 2,460,238 shares compared to its average volume of 6,399,701 a current ratio of 1.88 and a debt-to-equity ratio of 0.77 has a 12 month low of $107.13 and a 12 month high of $194.40 The business's 50-day simple moving average is $153.46 and its 200-day simple moving average is $157.49 Insiders have sold a total of 361,560 shares of company stock worth $59,253,850 over the last 90 days Insiders own 7.80% of the company's stock Explore Elon Musk’s boldest ventures yet—from AI and autonomy to space colonization—and find out how investors can ride the next wave of innovation Stifel Financial Corp lessened its stake in Snowflake Inc. (NYSE:SNOW - Free Report) by 9.4% in the 4th quarter according to its most recent Form 13F filing with the Securities and Exchange Commission (SEC) The institutional investor owned 57,422 shares of the company's stock after selling 5,962 shares during the period Stifel Financial Corp's holdings in Snowflake were worth $8,867,000 as of its most recent filing with the Securities and Exchange Commission (SEC) Other large investors also recently bought and sold shares of the company Asset Dedication LLC boosted its position in shares of Snowflake by 6.8% in the 4th quarter Asset Dedication LLC now owns 922 shares of the company's stock valued at $142,000 after purchasing an additional 59 shares during the period Breakwater Capital Group boosted its holdings in shares of Snowflake by 2.2% in the fourth quarter Breakwater Capital Group now owns 2,938 shares of the company's stock valued at $454,000 after acquiring an additional 62 shares during the period Gabelli Funds LLC grew its position in shares of Snowflake by 3.3% during the fourth quarter Gabelli Funds LLC now owns 2,050 shares of the company's stock worth $317,000 after acquiring an additional 65 shares during the last quarter Ltd Zurich increased its holdings in shares of Snowflake by 0.6% in the 4th quarter Ltd Zurich now owns 12,191 shares of the company's stock valued at $2,077,000 after acquiring an additional 69 shares during the period Larson Financial Group LLC raised its position in Snowflake by 21.8% in the 4th quarter Larson Financial Group LLC now owns 413 shares of the company's stock valued at $64,000 after purchasing an additional 74 shares during the last quarter Institutional investors and hedge funds own 65.10% of the company's stock Insiders have sold a total of 374,342 shares of company stock worth $61,578,513 in the last three months twenty-nine have assigned a buy rating and two have assigned a strong buy rating to the company the company presently has a consensus rating of "Moderate Buy" and an average price target of $200.28 NYSE:SNOW traded up $2.35 during mid-day trading on Friday The company's stock had a trading volume of 3,331,414 shares compared to its average volume of 6,415,425 The stock has a market cap of $55.36 billion The company's fifty day simple moving average is $153.46 and its two-hundred day simple moving average is $157.22 intelligent document processing has been crucial for managing large volumes of documents that need to transition from printed to digital formats challenges with accuracy and scalability have often hindered its potential combined with Snowflake’s powerful and cost-efficient processing engine Boothbay Fund Management LLC raised its holdings in shares of Snowflake Inc. (NYSE:SNOW - Free Report) by 212.5% during the 4th quarter according to the company in its most recent Form 13F filing with the Securities & Exchange Commission The fund owned 24,067 shares of the company's stock after purchasing an additional 16,365 shares during the quarter Boothbay Fund Management LLC's holdings in Snowflake were worth $3,716,000 as of its most recent filing with the Securities & Exchange Commission A number of other institutional investors and hedge funds have also recently added to or reduced their stakes in SNOW Stonebridge Financial Group LLC purchased a new position in Snowflake in the 4th quarter valued at approximately $29,000 Quadrant Capital Group LLC grew its position in shares of Snowflake by 74.6% in the fourth quarter Blue Bell Private Wealth Management LLC raised its stake in shares of Snowflake by 189.5% during the 4th quarter Blue Bell Private Wealth Management LLC now owns 220 shares of the company's stock valued at $34,000 after purchasing an additional 144 shares during the period Perkins Coie Trust Co lifted its holdings in Snowflake by 136.3% during the 4th quarter Union Bancaire Privee UBP SA bought a new position in Snowflake in the 4th quarter worth about $38,000 Piper Sandler decreased their target price on Snowflake from $215.00 to $175.00 and set an "overweight" rating on the stock in a research note on Wednesday thirty have given a buy rating and two have assigned a strong buy rating to the stock the company currently has a consensus rating of "Moderate Buy" and a consensus target price of $200.27 View Our Latest Analysis on SNOW insiders sold 374,342 shares of company stock valued at $61,578,513 Shares of Snowflake stock traded up $3.21 during trading hours on Friday 2,004,709 shares of the stock traded hands compared to its average volume of 6,384,407 The company has a 50 day moving average price of $153.39 and a 200 day moving average price of $156.65 The firm has a market capitalization of $55.64 billion a price-to-earnings ratio of -49.74 and a beta of 1.13 Discover the next wave of investment opportunities with our report Explore companies poised to replicate the growth and value creation of the tech giants dominating today's markets Oak Wealth Advisors LLC purchased a new stake in Snowflake Inc. (NYSE:SNOW - Free Report) in the 4th quarter according to the company in its most recent Form 13F filing with the SEC The firm purchased 4,766 shares of the company's stock Snowflake accounts for approximately 0.6% of Oak Wealth Advisors LLC's holdings Other institutional investors have also modified their holdings of the company Prospect Financial Services LLC purchased a new stake in Snowflake during the 4th quarter worth about $288,000 boosted its holdings in shares of Snowflake by 1.3% in the 4th quarter now owns 673,715 shares of the company's stock valued at $104,028,000 after buying an additional 8,857 shares in the last quarter Bourgeon Capital Management LLC purchased a new position in shares of Snowflake in the 4th quarter valued at $5,851,000 Stephens Consulting LLC lifted its position in Snowflake by 7,407.4% in the fourth quarter Stephens Consulting LLC now owns 2,027 shares of the company's stock valued at $313,000 after acquiring an additional 2,000 shares during the last quarter Kirtland Hills Capital Management LLC boosted its holdings in shares of Snowflake by 40.1% in the fourth quarter Kirtland Hills Capital Management LLC now owns 15,920 shares of the company's stock valued at $2,458,000 after acquiring an additional 4,559 shares during the period insiders have sold 374,342 shares of company stock worth $61,578,513 7.80% of the stock is currently owned by company insiders Several equities analysts recently issued reports on the company. StockNews.com upgraded Snowflake from a "sell" rating to a "hold" rating in a research report on Friday Evercore ISI boosted their price objective on shares of Snowflake from $190.00 to $200.00 and gave the company an "outperform" rating in a research note on Wednesday Guggenheim reaffirmed a "neutral" rating on shares of Snowflake in a report on Thursday Oppenheimer increased their price target on shares of Snowflake from $200.00 to $220.00 and gave the stock an "outperform" rating in a report on Thursday Canaccord Genuity Group lifted their price objective on Snowflake from $190.00 to $220.00 and gave the company a "buy" rating in a report on Tuesday thirty have given a buy rating and two have issued a strong buy rating to the company the stock currently has an average rating of "Moderate Buy" and an average target price of $200.27 Read Our Latest Analysis on Snowflake NYSE SNOW traded up $3.14 during trading hours on Friday The company had a trading volume of 2,004,709 shares The firm's 50 day moving average is $153.39 and its 200 day moving average is $156.65 The company has a market capitalization of $55.62 billion Adage Capital Partners GP L.L.C. trimmed its holdings in Snowflake Inc. (NYSE:SNOW - Free Report) by 17.3% during the fourth quarter according to its most recent filing with the Securities and Exchange Commission (SEC) The firm owned 107,500 shares of the company's stock after selling 22,500 shares during the quarter Adage Capital Partners GP L.L.C.'s holdings in Snowflake were worth $16,599,000 at the end of the most recent quarter Several other institutional investors and hedge funds have also recently bought and sold shares of SNOW Stonebridge Financial Group LLC acquired a new position in shares of Snowflake during the fourth quarter worth about $29,000 Quadrant Capital Group LLC increased its position in Snowflake by 74.6% during the 4th quarter Blue Bell Private Wealth Management LLC now owns 220 shares of the company's stock worth $34,000 after buying an additional 144 shares in the last quarter Perkins Coie Trust Co lifted its holdings in shares of Snowflake by 136.3% in the 4th quarter Union Bancaire Privee UBP SA purchased a new stake in shares of Snowflake in the fourth quarter valued at approximately $38,000 Insiders sold a total of 374,342 shares of company stock valued at $61,578,513 over the last three months SNOW traded up $3.44 during trading on Friday The company had a trading volume of 1,778,025 shares compared to its average volume of 6,383,507 The company has a market cap of $55.72 billion The business has a 50-day moving average of $153.39 and a 200-day moving average of $156.65 has a 1-year low of $107.13 and a 1-year high of $194.40 A number of research firms have commented on SNOW Cantor Fitzgerald reissued an "overweight" rating and issued a $183.00 target price on shares of Snowflake in a report on Wednesday Piper Sandler decreased their price objective on shares of Snowflake from $215.00 to $175.00 and set an "overweight" rating on the stock in a report on Wednesday Truist Financial lowered their target price on shares of Snowflake from $225.00 to $210.00 and set a "buy" rating on the stock in a research report on Monday Royal Bank of Canada lifted their price target on shares of Snowflake from $210.00 to $221.00 and gave the company an "outperform" rating in a research report on Thursday UBS Group upped their price target on Snowflake from $190.00 to $200.00 and gave the stock a "neutral" rating in a report on Thursday thirty have issued a buy rating and two have assigned a strong buy rating to the company the stock presently has an average rating of "Moderate Buy" and an average price target of $200.27 Read Our Latest Stock Report on Snowflake Which stocks are hedge funds and endowments buying in today's market Enter your email address and we'll send you MarketBeat's list of thirteen stocks that institutional investors are buying now