In February, Snowflake released a slew of new features that continue to expand the scope of what you’re able to do with Snowflake, including the public preview of Hybrid Tables. Read on to learn more about everything we announced last month.
Application Development
Hybrid Tables – public preview on AWS
Hybrid Tables is a new table type that enables transactional use cases within Snowflake with fast, high-concurrency point operations. Customers are simplifying their architectures by using Hybrid Tables for a variety of use cases, including storing app and workflow state, serving data, and building lightweight transactional applications. Learn more.
Snowflake Horizon
Universal Search – public preview in select regions
With Universal Search, an LLM-enabled experience based on Snowflake Cortex, Snowflake users can easily and quickly discover content in the Data Cloud by using natural language, surfacing relevant database objects within their account (databases, schemas, tables, views, functions and procedures), and apps shared with their account or published on Snowflake Marketplace, as well as articles from Snowflake Documentation. To learn more and see current region availability, see documentation.
Ask any question in natural language to surface relevant objects and resources with Universal Search.
Aggregation Policies – public preview
Snowflake’s Aggregation Policies allow data providers to protect sensitive data by enforcing that the consumer can only aggregate the data rather than retrieve individual records. When creating an aggregation policy, the provider specifies a minimum group size; for example, the number of rows that must be aggregated together into a group. The larger the minimum group size, the less likely it is that a consumer could use the query results to deduce the contents of a single record, allowing the provider to protect the privacy of the data. To learn more, see documentation.
Projection Policies – public preview
Snowflake’s Projection Policies allow data providers to protect sensitive data by constraining what columns a data consumer can query and join with other data while sharing access to raw data. For example, Projection Policies can be used to constrain identified columns such as names and phone numbers. The consumer can still match records based on a particular value but is unable to view the constrained value, providing analytical utility while protecting the privacy of the data. To learn more, see documentation.
Sensitive Data Classification in Snowsight – public preview
Sensitive Data Classification in Snowsight is a native user experience to run and review data classification within Snowflake’s UI. This simple experience lets you start a sensitive data classification job for an entire schema or a subset of tables within it, allowing you to choose when you want to review and apply the classification results, or automate the tag application via auto-tagging. You can then protect the data with a row access policy, masking policy or both. To learn more, see this blog post.
Secrets for Snowflake – general availability
Secrets are native Snowflake objects that store sensitive data or authentication credentials. Secrets are protected by Role Based Access Control (RBAC) and Snowflake’s key encryption hierarchy. This enables developers, admins and owners of user-defined functions (UDFs) to create, own and use credential data without the risk of unauthorized exposure. Secrets are used for connecting and authenticating with external API endpoints or resources via Snowpark external network access. They are also used by Snowflake’s first-party connectors, such as Servicenow and Google Analytics. Learn more.
Python Updates
Snowpark External Access – general availability on AWS and Azure
Snowpark External Access, now generally available on AWS and Azure regions, provides flexibility to reach public internet endpoints from Snowpark without any additional infrastructure setup. Users can now easily connect to external network locations from their Snowpark code (UDFs/UDTFs and Stored Procedures) while maintaining high security and governance over their data. Learn more.
Snowpark for Python extension in Visual Studio Code – public preview
The Snowflake Extension for Visual Studio Code now supports authoring and debugging of Snowpark for Python code. Writing and executing Snowpark for Python code is more seamless than ever before from VS Code. Learn more.
Granular controls for setting allowlists and blocklists for Anaconda packages – general availability
Using a packages policy enables you to set allowlists and blocklists for third-party Python packages from Anaconda at the account level. This lets you meet stricter auditing and security requirements, and gives you more fine-grained control over which packages are available or blocked in your environment. Learn more.
Python worksheets in Snowsight – general availability
Python worksheets let you write and run Snowpark for Python code in a worksheet in Snowsight. By writing code in Python worksheets, you can perform your development and testing in Snowflake without needing to install dependent libraries. Learn more.
Advanced Analytics
Time Series ASOF JOIN – public preview
Many industries, such as IoT and FinServ, deal with temporal data, frequently requiring the ability to join together time series data sets where timestamps don’t exactly match. Snowflake now enables exactly this with ASOF JOIN, providing a performant, easy-to-use SQL syntax to join together time series data sets where there is no exact timestamp or date match between the data. To learn more, see documentation.
Discrete Global Grid H3 – general availability
Snowflake now supports H3 functions, resulting in highly efficient processing of geospatial data for analytical and machine learning use cases. These functions help represent geographical areas using a uniform grid system that allows fast calculations between predictable cells in the grid. To learn more, see documentation.
Data Loading
Loading files is now easier with Snowflake – public preview
With this release, COPY FILES command is available in preview to load your files more easily. You can use COPY FILES to copy files from one named stage to another. Learn more.
Collaboration
Secure sharing for Iceberg Tables – public preview
Snowflake enables collaboration within and across organizations using a variety of architecture patterns. As part of the public preview for Iceberg tables, Snowflake now supports directly sharing an Iceberg table without having to create a secure view. Learn more.
Snowflake Marketplace
Snowflake customers can tap into Snowflake Marketplace for access to more than 2,400 live, up-to-date and ready-to-query third-party data sets, data services and Snowflake Native Apps all in one place (as of January 31, 2024). Here are all the providers who posted new listings in February:
AI/ML Solutions
Hazy – Synthetic data
Shutterstock, Inc. – Images
Connectors/SaaS Data
Data Governance, Quality and Cost Optimization
Data Providers
ChainXY Solutions Inc. – Geospatial data
CitiusTech Inc. – Healthcare data
Compstak Inc – Real estate data
DataNexify – Energy data
Dune Analytics AS – Crypto data
Echo Analytics – Geospatial data
Fantastic Enterprises, Inc. – Consumer data
GWI – Consumer data
Markaaz, Inc. – Demographic data
OpenSecrets – Government data
Patsnap – Patent data
Resights ApS – Real estate data in Denmark
Salient Predictions, Inc. – Weather data
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Forward-Looking Statements
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.
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