Software Update: Microsoft SQL Server 2019 CTP 2.4

Microsoft has released a new community technology preview of SQL Server 2019 with version number 2.4. SQL Server is a relational database management system that supports T-SQL, a dialect of SQL. It is available in different flavors and can therefore be used for small, medium and large databases. It runs on the Windows platform of course, but also on Linux, such as Red Hat Enterprise Server and Ubuntu. For the 2019 edition, a lot of work done to improve big data clusters and the database engine. The SQL Server 2019 CTP 2.4 announcement looks like this:

SQL Server 2019 community technology preview 2.4 is now available

Continuing with our monthly release cadence, we’re excited to announce the release of SQL Server 2019 community technology Preview 2.4. Previewed in September 2018, SQL Server 2019 is the first release of SQL Server to closely integrate Apache Spark™ and the Hadoop Distributed File System (HDFS) with SQL Server in a unified data platform.

The CTP 2.4 preview brings the following new features and capabilities to SQL Server 2019:

Big data clusters

• Introducing the use of powerful GPUs for running deep learning workloads using industry standard TensorFlow libraries in Spark.
• The Spark runtime engine has been upgraded to Spark 2.4, which provides a new scheduler that works better with MPI workloads , new high-order functions that enable complex data types, as well as many SparkSQL and Pandas improvements.

database engine

• New diagnostics have been added for actual execution plans through extended events and a Dynamic Management Function (DMF) that leverages lightweight Query profiling.
• This preview also introduces transparent data encryption (TDE) scan with suspend and resume syntax so that you can pause the scan while the workload on the system is heavy, or during business-critical hours, and then resume the scan later.

SQL Server Analysis Services (SSAS)

• Introducing many to many relationships in tabular models where both columns are non-unique. Many-to-many relationships avoid having to normalize dimension tables and can improve the user experience because the resulting model has a smaller number of tables with logically grouped columns.
• The new memory settings for resource governance protect server resources from expensive user queries that extract large amounts of data. The Memory\QueryMemoryLimit property can be used to limit memory spools built by DAX queries submitted to the model.