SQL Server 2019: Intelligence over all of your Data

In my last article on SQL Server 2019, I described SQL Server 2019 has the language and platform you need, your choice. SQL Server 2019 runs on Windows, Linux, in a container, and on Kubernetes. And you have application and database compatibility across all of these platforms. You have just about any language to meet your needs including the ability to extend T-SQL with languages like Java.

One of the most compelling capabilities of SQL Server 2019 is data virtualization. Imagine the ability to access just about any data source without having to move the data or write expensive Extract, Transform, and Load (ETL) applications. What if SQL Server became your data hub?

Consider a company that has all of these types of data sources:

With SQL Server 2019 and Polybase, you can create external tables in SQL Server and access all of this type of data and let the data stay where it lives. For Oracle, SQL Server (which include Azure), MongoDB (which includes Azure CosmosDB), and Teradata, SQL Server 2019 comes with built-in drivers installed with the product to access these data sources. Learn more about Polybase in SQL Server 2019 at https://docs.microsoft.com/en-us/sql/relational-databases/polybase/polybase-guide?.

Early in the days of building out the plan for SQL Server 2019, we knew that Big Data matters. And if you are talking Big Data it usually means HDFS and Spark. What if you needed to build out a system that included SQL Server for all your typical data needs such as OLTP workloads, included Polybase so you could access all of your data sources in one place, a cluster for HDFS to store unstructured and semi-structured data, Spark for distributed computing across your data, and capabilities to build Machine Learning applications? And let's say you thought Kubernetes provided an excellent platform to deploy all of this software. Oh and you also want a single deployment method for this system and have the ability to monitor this so of course you want metrics and logging.

That is exactly SQL Server 2019 Big Data Clusters provides in a single solution! This is why we use the term Intelligence over all of your data.

With SQL Server 2019 Big Data Clusters, we are using the power of Kubernetes to deploy a containerized solution that includes SQL Server (built-in with High Availability using Always On Availability Groups including system databases), Polybase scale-out groups deployed to access all of your data sources, a HDFS cluster deployed with Spark, Machine Learning languages such a R, Python, and SparkML, and an application deployment model so you can expose your machine learning models through REST APIs compliant with Swagger. And you can deploy and use your entire solution through Azure Data Studio using a Notebook experience. This is why we know Big Data matters and why SQL Server 2019 Big Data Clusters is a solution that delivers.

Learn more about how to deploy and use SQL Server 2019 Big Data Clusters at https://docs.microsoft.com/en-us/sql/big-data-cluster/big-data-cluster-overview. You can try out all of the capabilities of data virtualization and Big Data Clusters by using our free training at https://aka.ms/sqlworkshops. Start learning SQL Server 2019 today using our new Video Learning Series at https://cloudblogs.microsoft.com/sqlserver/2019/10/28/introducing-the-sql-server-2019-video-learning-series/.

If you want more details on what's new in SQL Server 2019, check out our documentation at https://docs.microsoft.com/en-us/sql/sql-server/what-s-new-in-sql-server-ver15. Want to follow other posts and new articles where I talk more about the details behind SQL Server 2019, follow #sqlserver2019. Or follow me on LinkedIn at linkedin.com/in/bobwardms or Twitter at @bobwardms.

You can also learn more about SQL Server 2019 in my new book SQL Server 2019 Revealed by Apress at https://aka.ms/sql2019book or on Amazon at https://amzn.to/33R8gaR.

Bob Ward, Principal Architect, Microsoft Azure Data