{"id":28020,"date":"2019-08-21T10:00:05","date_gmt":"2019-08-21T17:00:05","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/sql-server\/blog\/?p=28020"},"modified":"2019-08-21T10:10:45","modified_gmt":"2019-08-21T17:10:45","slug":"sql-server-2019-release-candidate-is-now-available","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/sql-server\/blog\/2019\/08\/21\/sql-server-2019-release-candidate-is-now-available\/","title":{"rendered":"SQL Server 2019 release candidate is now available"},"content":{"rendered":"

Today we\u2019re announcing the availability of the first public release candidate for SQL Server 2019<\/strong>, which is now available for download. SQL Server 2019 brings the industry-leading performance and security of SQL Server to Windows, Linux, and containers and can tackle any data workload from business intelligence to data warehousing to analytics and AI over all your data both structured and unstructured.<\/p>\n

In our nine community technology previews (CTPs) to date, SQL Server 2019 has delivered:<\/p>\n

1)\u00a0\u00a0\u00a0 <\/strong>Intelligence over all of your data with <\/strong>SQL Server 2019 Big Data Clusters<\/strong><\/a><\/p>\n

Your business is not constrained by the type of data that gets in the database. Now with SQL Server 2019, you can do analytics and AI over any type of data, structured, or unstructured with the power SQL and Apache Spark\u2122. You can enhance your high-value structured data by combining it with big data and the ability to dynamically scale-out compute to support analytics over the Hadoop Distributed File System (HDFS) at scale.<\/p>\n

2)\u00a0\u00a0\u00a0 <\/strong>Data virtualization with PolyBase<\/strong><\/a><\/p>\n

Data virtualization allows you to have a single query point where you run your T SQL code or connect your BI tools to, to join your disparate data and fetch the results. No more data movement, just a semantic layer to abstract the complexity of your underlying estate. You can query data stored in Oracle, Teradata, HDFS or any other data sources without moving or replicating the data and can do it in a performant manner by caching the key data in a scale out data mart.<\/p>\n

3)\u00a0\u00a0\u00a0 <\/strong>Choice of platform and language<\/strong><\/p>\n