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In March, Snowflake announced exciting releases, including advances in AI and ML with new features in Snowflake Cortex, new governance and privacy features in Snowflake Horizon, and broader developer support with the Snowflake CLI. Read on to learn more about everything we announced last month. Snowflake Cortex LLM Functions – in public preview Snowflake Cortex is an intelligent, fully managed service that delivers state-of-the-art large language models (LLMs) as serverless SQL/Python functions; there are no integrations to set up, data to move or GPUs to provision. In Snowflake Cortex, there are task-specific functions that teams can use to quickly and cost-effectively execute complex tasks, such as translation, sentiment analysis and summarization. Additionally, to build custom apps, teams can use the complete function to run custom prompts using LLMs from Mistral AI, Meta and Google. Learn more. Streamlit Streamlit 1.26 – in public preview We’re excited to announce support for Streamlit version 1.26 within Snowflake. This update, in preview, expands your options for building data apps directly in Snowflake’s secure environment. Now you can leverage the latest features and functionalities available in Streamlit 1.26.0 — including st.chat_input and st.chat_message, two powerful primitives for creating conversational interfaces within your data apps. This addition allows users to interact with your data applications using natural language, making them more accessible and user-friendly. You can also utilize the new features of Streamlit 1.26.0 to create even more interactive and informative data visualizations and dashboards. To learn more and get started, head over to the Snowflake documentation. Snowflake Horizon Sensitive Data Custom Classification – in public preview In addition to using standard classifiers in Snowflake, customers can now also write their own classifiers using SQL with custom logic to define what data is sensitive to their organization. This is an important enhancement to data classification and provides the necessary extensibility that customers need to detect and classify more of their data. Learn more. Data Quality Monitoring – in public preview Data Quality Monitoring is a built-in solution with out-of-the-box metrics, like null counts, time since the object was last updated and count of rows inserted into an object. Customers can even create custom metrics to monitor the quality of data. They can then effectively monitor and report on data quality by defining the frequency it is automatically measured and configure alerts to receive email notifications when quality thresholds are violated. Learn more. Snowflake Data Clean Rooms – generally available in select regions Snowflake Data Clean Rooms allow customers to unlock insights and value through secure data collaboration. Launched as a Snowflake Native App on Snowflake Marketplace, Snowflake Data Clean Rooms are now generally available to customers in AWS East, AWS West and Azure West. Snowflake Data Clean Rooms make it easy to build and use data clean rooms for both technical and non-technical users, with no additional access fees set by Snowflake. Find out more in this blog. DevOps on Snowflake Snowflake CLI – public preview The new Snowflake CLI is an open source tool that empowers developers with a flexible and extensible interface for managing the end-to-end lifecycle of applications across various workloads (Snowpark, Snowpark Container Services, Snowflake Native Applications and Streamlit in Snowflake). It offers features such as user-defined functions, stored procedures, Streamlit integration and direct SQL execution. Learn more. Snowflake Marketplace Snowflake customers can tap into Snowflake Marketplace for access to more than 2,500 live and ready-to-query third-party data, apps and AI products all in one place (as of April 10, 2024). Here are all the providers who launched on Marketplace in March: AI/ML Products Brillersys – Time Series Data Generator Atscale, Inc. – Semantic Modeling Data paretos GmbH – Demand Forecasting App Connectors/SaaS Data HALitics – eCommerce Platform Connector Developer Tools DataOps.live – CI/CD, Automation and DataOps Data Governance, Quality and Cost Optimization Select Labs US Inc. – Snowflake Performance & Cost Optimization Foreground Data Solutions Inc – PII Data Detector CareEvolution – Data Format Transformation Merse, Inc – Snowflake Performance & Cost Optimization Qbrainx – Snowflake Performance & Cost Optimization Yuki – Snowflake Performance Optimization DATAN3RD LLC – Data Quality App Third-Party Data Providers Upper Hand – Sports Facilities & Athletes Data Sporting Group – Sportsbook Data Quiet Data – UK Company Data Manifold Data Mining – Demographics Data in Canada SESAMm – ESG Controversy Data KASPR Datahaus – Internet Quality & Anomaly Data Blitzscaling – Blockchain Data Starlitics – ETF and Mutual Fund Data SFR Analytics – Geographic Data SignalRank – Startup Data GfK SE – Purchasing Power Data —- ​​Forward-Looking Statement This post contains express and implied forward-looking statements, including statements regarding (i) Snowflake’s business strategy, (ii) Snowflake’s products, services, and technology offerings, including those that are under development or not generally available, (iii) market growth, trends, and competitive considerations, and (iv) the integration, interoperability, and availability of Snowflake’s products with and on third-party platforms. These forward-looking statements are subject to a number of risks, uncertainties, and assumptions, including those described under the heading “Risk Factors” and elsewhere in the Quarterly Reports on Form 10-Q and Annual Reports of Form 10-K that Snowflake files with the Securities and Exchange Commission. In light of these risks, uncertainties, and assumptions, actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements. As a result, you should not rely on any forward-looking statements as predictions of future events. © 2024 Snowflake Inc. All rights reserved. Snowflake, the Snowflake logo, and all other Snowflake product, feature, and service names mentioned herein are registered trademarks or trademarks of Snowflake Inc. in the United States and other countries. All other brand names or logos mentioned or used herein are for identification purposes only and may be the trademarks of their respective holder(s). Snowflake may not be associated with, or be sponsored or endorsed by, any such holder(s). The post New Snowflake Features Released in March 2024 appeared first on Snowflake. View the full article

The rise of data collaboration and use of external data sources highlights the need for robust privacy and compliance measures. In this evolving data ecosystem, businesses are turning to clean rooms to share data in low-trust environments. Clean rooms enable secure analysis of sensitive data assets, allowing organizations to unlock insights without compromising on privacy. To facilitate this type of data collaboration, we launched the preview of data clean rooms last year. Today, we are excited to announce that BigQuery data clean rooms is now generally available. Backed by BigQuery, customers can now share data in place with analysis rules to protect the underlying data. This launch includes a streamlined data contributor and subscriber experience in the Google Cloud console, as well as highly requested capabilities such as: Join restrictions: Limits the joins that can be on specific columns for data shared in a clean room, preventing unintended or unauthorized connections between data. Differential privacy analysis rule: Enforces that all queries on your shared data use differential privacy with the parameters that you specify. The privacy budget that you specify also prevents further queries on that data when the budget is exhausted. List overlap analysis rule: Restricts the output to only display the intersecting rows between two or more views joined in a query. Usage metrics on views: Data owners or contributors see aggregated metrics on the views and tables shared in a clean room. Using data clean rooms in BigQuery does not require creating copies of or moving sensitive data. Instead, the data can be shared directly from your BigQuery project and you remain in full control. Any updates you make to your shared data are reflected in the clean room in real-time, ensuring everyone is working with the most current data. Create and deploy clean rooms in BigQuery BigQuery data clean rooms are available in all BigQuery regions. You can set up a clean room environment using the Google Cloud console or using APIs. During this process, you set permissions and invite collaborators within or outside organizational boundaries to contribute or subscribe to the data. Enforce analysis rules to protect underlying data When sharing data into a clean room, you can configure analysis rules to protect the underlying data and determine how the data can be analyzed. BigQuery data clean rooms support multiple analysis rules including aggregation, differential privacy, list overlap, and join restrictions. The new user experience within Cloud console lets data contributors configure these rules without needing to use SQL. Lastly, by default, a clean room employs restricted egress to prevent subscribers from exporting or copying the underlying data. However, data contributors can choose to allow the export and copying of query results for specific use cases, such as activation. Monitor usage and stay in control of your data The data owner or contributor is always in control of their respective data in a clean room. At any time, a data contributor can revoke access to their data. Additionally, as the clean room owner, you can adjust access using subscription management or privacy budgets to prevent subscribers from performing further analysis. Additionally, data contributors receive aggregated logs and metrics, giving them insights into how their data is being used within the clean room. This promotes both transparency and a clearer understanding of the collaborative process. What BigQuery data clean room customers are saying Customers across all industries are already seeing tremendous success with BigQuery data clean rooms. Here’s what some of our early adopters and partners had to say: “With BigQuery data clean rooms, we are now able to share and monetize more impactful data with our partners while maintaining our customers' and strategic data protection.” - Guillaume Blaquiere, Group Data Architect, Carrefour “Data clean rooms in BigQuery is a real accelerator for L'Oréal to be able to share, consume, and manage data in a secure and sustainable way with our partners.” - Antoine Castex, Enterprise Data Architect, L’Oréal “BigQuery data clean rooms equip marketing teams with a powerful tool for advancing privacy-focused data collaboration and advanced analytics in the face of growing signal loss. LiveRamp and Habu, which independently were each early partners of BigQuery data clean rooms, are excited to build on top of this foundation with our combined interoperable solutions: a powerful application layer, powered by Habu, accelerates the speed to value for technical and business users alike, while cloud-native identity, powered by RampID in Google Cloud, maximizes data fidelity and ecosystem connectivity for all collaborators. With BigQuery data clean rooms, enterprises will be empowered to drive more strategic decisions with actionable, data-driven insights.” - Roopak Gupta, VP of Engineering, LiveRamp “In today’s marketing landscape, where resources are limited and the ecosystem is fragmented, solutions like the data clean room we are building with Google Cloud can help reduce friction for our clients. This collaborative clean room ensures privacy and security while allowing Stagwell to integrate our proprietary data to create custom audiences across our product and service offerings in the Stagwell Marketing Cloud. With the continued partnership of Google Cloud, we can offer our clients integrated Media Studio solutions that connect brands with relevant audiences, improving customer journeys and making media spend more efficient.” - Mansoor Basha, Chief Technology Officer, Stagwell Marketing Cloud “We are extremely excited about the General Availability announcement of BigQuery data clean rooms. It's been great collaborating with Google Cloud on this initiative and it is great to see it come to market.. This release enables production-grade secure data collaboration for the media and advertising industry, unlocking more interoperable planning, activation and measurement use cases for our ecosystem.” - Bosko Milekic, Chief Product Officer, Optable Next steps Whether you're an advertiser trying to optimize your advertising effectiveness with a publisher, or a retailer improving your promotional strategy with a CPG, BigQuery data clean rooms can help. Get started today by using this guide, starting a free trial with BigQuery, or contacting the Google Cloud sales team. View the full article

In December 2023, Snowflake announced its acquisition of data clean room technology provider Samooha. Samooha’s intuitive UI and focus on reducing the complexity of sharing data led to it being named one of the most innovative data science companies of 2024 by Fast Company. Now, Samooha’s offering is integrated into Snowflake and launched as Snowflake Data Clean Rooms, a Snowflake Native App on Snowflake Marketplace, generally available to customers in AWS East, AWS West and Azure West. Snowflake Data Clean Rooms make it easy to build and use data clean rooms in Snowflake, with no additional access fees set by Snowflake. What is a data clean room? Data clean rooms provide a controlled environment that allows multiple companies, or divisions of a company, to securely collaborate on sensitive or regulated data while fully preserving the privacy of the enterprise data. Enterprises should not have to make challenging trade-offs between following compliance regulations and making sensitive data available for collaboration. With data clean rooms, organizations have an opportunity to unlock the value of sensitive data by allowing for joint data analytics, machine learning and AI by anonymizing, processing and storing personally identifiable information (PII) in a compliant way. Data clean rooms allow for multiple parties to securely collaborate on sensitive or regulated data, surfacing valuable insights while preserving the privacy of the data. How does a data clean room work? Data clean rooms can be used to control the following: What data comes into the clean room How the data in the clean room can be joined to other data in the clean room What types of analytics each party can perform on the data What data, if any, can leave the clean room Any sensitive or regulated data, such as PII, that is loaded into the clean room is encrypted. The clean room provider has full control over the clean room environment, while approved partners can get a feed with anonymized data. Why Snowflake Data Clean Rooms? Until now, data clean room technology was generally deployed by large organizations with access to technical data privacy experts. Snowflake Data Clean Rooms remove the technical and financial barriers, allowing companies of all sizes to easily build, use and benefit from data clean rooms. Unlock value with data clean rooms easily and at no additional license cost Teams can stand up new data clean rooms quickly, easily and at no additional license fees through an app that is available on Snowflake Marketplace. Built for business and technical users alike, Snowflake Data Clean Rooms allow organizations to unlock value from data faster with industry-specific workflows and templates such as audience overlap, reach and frequency, last touch attribution and more. As a Snowflake Native App, Snowflake Data Clean Rooms makes it easy for technical and business users to build and use data clean rooms in Snowflake. Tap into the open and interoperable ecosystem of the Snowflake Data Cloud The Snowflake Data Cloud provides an open, neutral and interoperable data clean room ecosystem that allows organizations to collaborate with all their partners seamlessly, regardless of whether they have their own Snowflake accounts. Companies can also leverage turnkey third-party integrations and solutions for data enrichment, identity, activation and more across providers. Snowflake Data Clean Rooms allows you to collaborate with your partners seamlessly across regions and clouds thanks to Cross-Cloud Snowgrid (Snowflake Data Clean Rooms is currently available in AWS East/West and Azure West). It provides a cross-cloud technology layer that allows you to interconnect your business’ ecosystems across regions and clouds and operate at scale. Take advantage of Snowflake’s built-in privacy and governance features Unlock privacy-enhanced collaboration on your sensitive data through an app built on the Snowflake Native App Framework. By bringing the clean room solution to your data, Snowflake Data Clean Rooms removes the need for data to ever leave the governance, security and privacy parameters of Snowflake. By leveraging Snowpark for AI/ML, cryptographic compute support, differential privacy models, security attestation guarantees and more, Snowflake Data Clean Rooms helps companies maintain privacy while allowing for deeper analytical insight with business partners. You can easily integrate data and activation partners to realize use cases in marketing, advertising, and across other industries. Snowflake Data Clean Rooms is a Snowflake Native App that runs directly in your Snowflake account, eliminating the need to move or copy data out of the governance, security and privacy parameters of Snowflake. Data clean room use cases across industries Key use cases for data clean rooms are found in marketing, media and advertising. However, organizations across industries are realizing value with data clean rooms, including financial services and healthcare and life sciences. Attribution for advertising and marketing One popular use case for data clean rooms is to link anonymized marketing and advertising data from multiple parties for attribution. Suppose a company has its own first-party data containing attributes about its customers and their associated sales SKUs. In that case, the company can use a data clean room to improve audience insights for advertising. Let’s say the company wants to find new customers with the same attributes as its best customers, and combine those attributes with other characteristics to drive upsell opportunities. To create the target segments and comply with privacy requirements, the company uploads its data into a clean room that it creates or is shared by its ad partner. Participants can securely join any first-party data without exposing IDs. Without a data clean room, only limited amounts of data could flow between the various parties due to data privacy, regulations and competitive concerns. Measurement for advertising and marketing Another key data clean room use case is the measurement of the effectiveness of advertising and marketing campaigns. Advertisers want to understand who saw an advertisement, for example, as well as who engaged with it. This information will be distributed across the different media partners it takes to serve an ad to a consumer. Creating a joint analysis across the data of these different media partners is important for advertisers to understand campaign results and to optimize future campaigns. Such measurement can only be realized through a data clean room as it protects the sensitivity of the consumer data across all parties while surfacing valuable analytical insights. Monetizing proprietary data The omnichannel customer journey is complex, and it rarely starts with a brand’s advertisement. For example, if a consumer is planning an upcoming purchase of a kitchen appliance, the journey is likely to start with online review sites. A reviews site collects top-of-funnel data that would be invaluable to the appliance brand. With a data clean room, the reviews website could create a compliant third-party data product, manage access to it through the clean room, and monetize it. Consumer goods-retail collaboration Data clean rooms allow retailers and consumer goods companies to collaborate with brands that advertise with them. For example, a retailer can share transaction data in a privacy- and governance-friendly manner to provide insights into conversion signals and enable better targeting, personalization and attribution. Enhancing financial service customer data Similar to use cases in marketing, data clean rooms enable financial institutions to securely collaborate across a variety of use cases like credit fraud modeling and money laundering. Sensitive financial consumer data can be enhanced with second and third-party data sources and analyzed across institutional boundaries to detect anomalous patterns and behaviors, all while protecting consumer data privacy. Enriching patient health data In healthcare and life sciences, a hospital can use data clean rooms to share regulated patient data with a pharmaceutical company. The company can enrich and analyze the data to identify patterns in patient outcomes across clinical trials. The data clean room environment enables the patient data to remain private while still contributing to meaningful insights. Learn more about Snowflake Data Clean Rooms Get started today with Snowflake Data Clean Rooms: visit the listing on Snowflake Marketplace for additional details. To see a demo of Snowflake Data Clean Rooms, register for Snowflake’s virtual Accelerate Advertising, Media, & Entertainment event and learn how media and advertising organizations collaborate in the Media Data Cloud to enhance business growth and data monetization, develop new products, and harness the power of AI and ML. The post Snowflake Data Clean Rooms: Securely Collaborate to Unlock Insights and Value appeared first on Snowflake. View the full article