What’s new in SQL Server 2019

Now generally available, here’s what’s new.

Each version of SQL Server tends to build and add additional capabilities onto the prior version. SQL Server 2019 is no exception this trend.

Probably the most interesting enhancements are:

SQL Server 2019 introduces Big Data Clusters for SQL Server.

While you can still use SQL Server 2019 in the traditional way, it now expands to offer far more capabilities.


New feature or update


Scalable big data solution

Deploy scalable clusters of SQL Server, Spark, and HDFS containers running on Kubernetes.
Read, write, and process big data from Transact-SQL or Spark.
Easily combine and analyze high-value relational data with high-volume big data.
Query external data sources.
Store big data in HDFS managed by SQL Server.
Query data from multiple external data sources through the cluster.
Use the data for AI, machine learning, and other analysis tasks.
Deploy and run applications in Big Data Clusters.
The SQL Server master instance provides high availability and disaster recovery for all databases by using Always On availability group technology.

Data virtualization with PolyBase

Query data from external SQL Server, Oracle, Teradata, MongoDB, and ODBC data sources with external tables, now with UTF-8 encoding support. For more information, see What is PolyBase?.

What does this mean? Consider the following scenarios:


SQL Server Big Data Clusters provide flexibility in how you interact with your big data. You can query external data sources, store big data in HDFS managed by SQL Server, or query data from multiple external data sources through the cluster. You can then use the data for AI, machine learning, and other analysis tasks. The following sections provide more information about these scenarios.

Data virtualization

By leveraging SQL Server PolyBase, SQL Server Big Data Clusters can query external data sources without moving or copying the data. SQL Server 2019 (15.x) introduces new connectors to data sources.


Data lake

A SQL Server big data cluster includes a scalable HDFS storage pool. This can be used to store big data, potentially ingested from multiple external sources. Once the big data is stored in HDFS in the big data cluster, you can analyze and query the data and combine it with your relational data.


Scale-out data mart

SQL Server Big Data Clusters provide scale-out compute and storage to improve the performance of analyzing any data. Data from a variety of sources can be ingested and distributed across data pool nodes as a cache for further analysis.


Integrated AI and Machine Learning

SQL Server Big Data Clusters enable AI and machine learning tasks on the data stored in HDFS storage pools and the data pools. You can use Spark as well as built-in AI tools in SQL Server, using R, Python, Scala, or Java.



It also provides additional capability and improvements for the SQL Server database engine, SQL Server Analysis Services, SQL Server Machine Learning Services, SQL Server on Linux, and SQL Server Master Data Services.


What’s new in SQL Server 2019

SQL Server 2019: Not your Grandpa’s SQL Server

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