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SQL Server Blog

We’re excited to announce the monthly release of SQL Server 2019 community technology preview (CTP) 3.1. Customers can download this free build, try out features in this release and prior releases, and provide feedback to the engineering team. Your feedback is very helpful in refining features to be the most useful and avoid unintended regressions.

Launched in preview last year, SQL Server 2019 combines SQL Server with Apache Spark™ and Hadoop Distributed File System for a unified data platform that enables analytics and AI over all data relational and non-relational. In addition, SQL Server 2019 will deliver a comprehensive data virtualization solution that enables customers to combine data sets across multiple databases in a performant manner directly through SQL Server. SQL Server 2019 continues to deliver on its promise of delivering unparalleled security and performance across all data with new enhanced security and performance capabilities across both Windows and Linux.

Check out what’s new in the SQL Server 2019 CTP 3.1 preview documentation to learn more.

Database engine

  • Graph database: This release extends the graph functionality first released in SQL Server 2017. In addition to the previously-shipped abilities to work with large graphs as nodes and edges, this release extends that capability with the initial release of the shortest path computation capability. Graph tables now support multiple filegroups which should improve performance and scale on larger graph installations.
  • Encrypted query processing via the Always Encrypted with secure enclaves feature has also delivered the ability to support indexes to speed up queries running in the encrypted enclaves. This should significantly expand the set of applications which can run fully encrypted with the application owner holding the encryption key so it can’t be seen by the operator of SQL Server or Azure SQL Database. This gives users more options to run applications on untrusted hosting environments without allowing database administrators to have access to the unencrypted plaintext data stored in the database. SQL Server supports both hardware and software enclaves, so it’s possible to test this functionality without having specialized hardware available.

SQL Server big data clusters

The big data clusters feature continues to add key capabilities for its initial release in SQL Server 2019. This month, the release extends the Apache Spark™ functionality for the feature by supporting the ability to read and write to data pool external tables directly as well as a mechanism to scale compute separately from storage for compute-intensive workloads. Both enhancements should make it easier to integrate Apache Spark™ workloads into your SQL Server environment and leverage each of their strengths. Beyond Apache Spark™, this month’s release also includes machine learning extensions with MLeap where you can train a model in Apache Spark™ and then deploy it for use in SQL Server through the recently released Java extensibility functionality in SQL Server CTP 3.0. This should make it easier for data scientists to write models in Apache Spark™ and then deploy them into production SQL Server environments for both periodic training and full production against the trained model in a single environment.

Ready to learn more?