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|Name:||SQL Server 2019 Standard||Version:||Standard Verison|
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New features of SQL Server 2019
Big data cluster
SQL Server 2019 makes it easier to manage the big data environment. It provides the key elements of the data Lake - Hadoop distributed file system (HDFS), spark and analysis tools - which are deeply integrated with SQL server and fully supported by Microsoft. Easy deployment using Linux containers on clusters managed by kubernetes.
Mission critical availability - high availability
Enhanced availability, including automatic redirection of connections to the primary server based on read / write intent.
You can use kubernetes' AlwaysOn availability groups to enable the high availability configuration of SQL server running in containers.
Resumable online indexes support creation operations and include database scope defaults.
Performance improvement - Intelligent Database
Intelligent query processing series functions are based on adaptive query processing in sqlserver2017, including row mode memory feedback, approximate countdistinct, batch mode on row storage and delayed compilation of table variables.
1. Microsoft's focus on data science scenarios is very consistent with the company's intelligent cloud / intelligent edge strategy. Data is very important for building machine learning tools. By running R and Python code in the database, you can provide complex queries from the SQL server command line and use familiar tools to build and test the code before deploying and running the code. Microsoft is providing sample code through GitHub to show how to mix relational data with big data. It also shares the sample architecture to show you how to use it as a basis for building machine learning systems based on other open source technologies such as Kafka.
2.Large scale data processing has always been a problem. Few database engines are designed as part of distributed architecture. Using sqlserver2019, you can now use the existing POLYBASE function of SQL server to build what Microsoft calls big data clusters by using the mixture of SQL server and Apache spark containers on kubernetes. With the public cloud supporting local kubernetes, you will be able to deploy big data clusters on azure, AWS, GCP and your own infrastructure. Integration with azure Data Studio tools makes it easier to build, run, and share complex queries.
3.Other new features, such as static data masking, focus on protecting and cleaning up data to use it without impacting compliance. Applying static data masking to columns in a database export allows developers to use actual data while preventing the disclosure of sensitive information. There is no way to retrieve the original data because it is a one-way process. Earlier versions of SQL Server introduced dynamic data shielding, which is only applicable to the original database. Through static shield export, developers are careless or do not have the risk of exposing or affecting real-time data, and let them generate code that can be put into production without any change.
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