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Intelligent Query Processing takes the automated performance tuning of Adaptive Query Processing in SQL Server 2017 further, building on the performance tuning that’s done in SQL Azure.
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SQL Server Configuration Manager now includes certificate management for deploying certificates to failover clusters and Always On Availability Groups, and viewing installed certificates (including a handy view showing certificates that will expire soon).
#Sql server 2019 plus#
The integrated security tier in SQL Server covers the Spark and HDFS integration, protecting data at rest and in motion with the Always Encrypted option (which requires secure enclaves on your servers but now allows more complex operations), plus built-in data discovery, classification and auditing (which can now log the sensitivity classification of data returned in a query) across all data stores. Polybase still supports Spark, Hortonworks and Cloudera Hadoop, but there are new connectors to query Oracle, Teradata and MongoDB (including Cosmos DB), as well as generic ODBC data sources (like DB2, SAP HANA and even Excel) and even other SQL Server databases directly from SQL Server without needing to move or replicate it, making it much faster to generate reports that need information from external tables. Whether you’re moving to the new big data clusters or sticking to a conventional SQL Server architecture, Polybase still gives you more connectivity in the 2019 release. The big data clusters are in a limited public preview you have to register and request access. If you need to provide a quorum within the availability group, Microsoft is working on an open source Paxos implementation (which will be available on GitHub) that will provide a similar architecture to failover cluster instances.
#Sql server 2019 full#
To do that, you first deploy an operator role into Kubernetes that orchestrates the deployment of pods, connects to them and then orchestrates the full deployment of the availability group onto that pod deployment – which allows for rolling upgrades to apply updates with less downtime. These new SQL Server Big Data Clusters create an elastic scale-out data virtualisation platform where you can deploy both SQL and Spark Linux containers on Kubernetes, including deploying SQL Server Availability Groups in Kubernetes.
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With SQL Server 2016, the Docker container support has sometimes been seen more as a way to speed up deployment initially for development and test but the Kubernetes support in SQL Server 2019 is much broader, supporting the features needed for production deployments. The Spark engine is now part of SQL Server, so you can combine SQL compute nodes with either SQL or HDFS storage nodes depending on whether you need relational tables or a data lake, using Spark for data science, advanced analytics and machine learning tasks, and have SQL Server and Spark running in the same Kubernetes container deployment. This gives SQL Server a distributed architecture, where you can pick and mix the elements that best suit your data needs. A year later, the CTP 2.0 preview of SQL Server 2019 announced at the Ignite conference also goes beyond the familiar relational database, with a new architecture that combines the SQL Server database engine, Apache Spark and Hadoop distributed file system (HDFS) support as a hybrid platform for big data, so you can connect to relational, NoSQL and big data sources and work with them all in a unified way. The big news with the SQL Server 2017 release was support for running on Linux and in containers, graph queries, and running machine learning where your data is using R and Python.