SQL Server brings intelligent performance, availability, and security to Windows, Linux, and containers and can tackle any data workload from BI to AI from online transaction processing (OLTP) to data warehousing. You get mission-critical high availability and disaster recovery features that allow you to implement various topologies to meet your business SLAs. A customer with SQL Server licenses with Software Assurance has historically benefited from a free passive instance of SQL Server for their high availability configurations. That helps to lower the total cost of ownership (TCO) of an application using SQL Server.

Today, we are enhancing the existing Software Assurance benefits for SQL Server which further helps customers implement a holistic business continuity plan with SQL Server.

Starting Nov 1^st, every Software Assurance customer of SQL Server will be able to use three enhanced benefits for any SQL Server release that is still supported by Microsoft:

• Failover servers for high availability – Allows customers to install and run passive SQL Server instances in a separate operating system environment (OSE) or server for high availability on-premises in anticipation of a failover event. Today, Software Assurance customers have one free passive instance for either high availability or DR
• Failover servers for disaster recovery ^NEW – Allows customers to install and run passive SQL Server instances in a separate OSE or server on-premises for disaster recovery in anticipation of a failover event
• Failover servers for disaster recovery in Azure ^NEW – Allows customers to install and run passive SQL Server instances in a separate OSE or server for disaster recovery in Azure in anticipation of a failover event

With these new benefits, Software Assurance customers will be able to implement hybrid disaster recovery plans with SQL Server using our features like Always On Availability Groups without incurring additional licensing costs for the passive replicas.

In the image below, the setup uses SQL Server running on an Azure Virtual Machine utilizing 12 cores as a disaster recovery replica for an on-premises SQL Server deployment using 12 cores. In the past, you would need to license 12 cores of SQL Server for the on-premises and the Azure Virtual Machine deployment. The new benefit offers passive replica benefits running on an Azure Virtual Machine. Now a customer would need to only license 12 cores of SQL Server running on-premises as long as the disaster recovery criteria for the passive replica on Azure Virtual Machine is met.

In the image below, the primary or the active replica uses 12 cores hosting two virtual machines. The topology has two secondary replicas: one sync replica for high availability supporting automatic failovers and one asynchronous replica for disaster recovery without automatic failover. With the announcement of the new benefits, the number of SQL Server core licenses required to operate this topology would be only 12 cores as opposed to 24 cores in the past.

SQL Server 2019 provides a number of improvements for availability, performance, and security along with new capabilities like the integration of HDFS and Apache Spark™ with the SQL Server database engine. Try SQL Server 2019 today and create a robust business continuity plan using our industry leading Software Assurance benefits.

The above high availability and disaster recovery benefits will be applicable to all releases of SQL Server.

In addition to the high availability and disaster recovery benefits, the following operations are allowed on the passive replicas:

• Database consistency checks
• Log backups
• Full backups
• Monitoring resource usage data

The updated benefits will be available in the next refresh of the Microsoft Licensing Terms.

Learn more

Introducing SQL Server 2019
What’s new in SQL Server 2019
SQL Server 2019 editions and features
Microsoft Licensing Terms

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Back in July, we announced the preview of Big Data Clusters in SQL Server 2019 and since then we’ve seen our customers actively bringing their big data analytical workloads to SQL Server 2019 to operationalize their AI and machine learning projects.

“Building and deploying our vertical AI-solution for clinical radiology combines very diverse implementation paradigms, data formats, and regulatory requirements. SQL Server 2019 Big Data Clusters allowed us to accommodate and integrate all aspects from one shared platform – for our data scientists with their deep learning as well as for our software engineers who wire up workflows, security, and scalability. At runtime, our healthcare customers benefit from simple containerized deployment and maintenance while being able to move our solution between on-prem and the cloud easily.” – René Balzano, Founder and CEO of Balzano

Big Data Clusters in SQL Server 2019 gives you the ability to build new big data applications in SQL Server or move your existing big data workloads to SQL Server to gain transformative insights from your data.

Some common use cases for Big Data Clusters in SQL Server 2019 include:

• Analytics over big data: With SQL Server 2019, you can analyze any type of data, structured, or unstructured with the power SQL and Apache Spark™
• Data virtualization: You can gain insights from all of your data by querying across data stored in Oracle, Teradata, other SQL Servers, Azure SQL Database, Azure SQL Data Warehouse, Azure Cosmos DB, Cloudera, Azure Data Lake, or the Hadoop Distributed File System (HDFS) built in to SQL Server Big Data Clusters without moving or replicating the data. You can have one single query point from where you run your T SQL code or connect your apps and BI tools to, joining your disparate data together into a single query result.
• AI in SQL Server: You can deploy scalable clusters of SQL Server, Apache Spark™, and HDFS running on Kubernetes. It provides all the tools and popular machine learning libraries to ingest, store, and prepare data for analysis as well as to train and operationalize machine learning models.

There are several scenarios in which SQL Server Big Data Clusters lets you interact with your big data. Please visit the SQL Server Big Data Clusters documentation to learn more.

To deploy the release candidate for SQL Server 2019, please review the documentation to learn more.

Ready to learn more?

• To experience release candidate bits for SQL Server 2019, please find the installation instructions on SQL Server 2019 release candidate
• To get started with SQL Server 2019 release candidate on Windows, Linux, or containers, you can find the download and installation instructions on the SQL Server 2019 web page or use a SQL Server 2019 community technology preview virtual machine in Microsoft Azure.
• Check out what features were introduced in SQL Server 2019 release candidate.

The post SQL Server 2019 release candidate refresh with Big Data Clusters appeared first on Microsoft SQL Server Blog.

In our nine community technology previews (CTPs) to date, SQL Server 2019 has delivered:

1)    Intelligence over all of your data with SQL Server 2019 Big Data Clusters

Your business is not constrained by the type of data that gets in the database. Now with SQL Server 2019, you can do analytics and AI over any type of data, structured, or unstructured with the power SQL and Apache Spark™. You can enhance your high-value structured data by combining it with big data and the ability to dynamically scale-out compute to support analytics over the Hadoop Distributed File System (HDFS) at scale.

2)    Data virtualization with PolyBase

Data virtualization allows you to have a single query point where you run your T SQL code or connect your BI tools to, to join your disparate data and fetch the results. No more data movement, just a semantic layer to abstract the complexity of your underlying estate. You can query data stored in Oracle, Teradata, HDFS or any other data sources without moving or replicating the data and can do it in a performant manner by caching the key data in a scale out data mart.

3)    Choice of platform and language

• SQL Server 2019 now runs on Windows, Linux, and containers and has support for deployment on Kubernetes
• You can deploy on multiple Linux distributions including RedHat, SUSE, and Ubuntu with full database engine support including transaction replication and machine learning services
• SQL Server extensibility framework now supports the execution of custom Java code along the same lines it executed R and Python. Free supported Java in SQL Server 2019 is now available.
• Support UTF-8 character for applications extending to a global scale, where the requirement of providing global multilingual database applications and services is critical to meeting customer demands, and specific market regulations.

4)    Industry leading performance and availability

• Enhancements to intelligent query processing (IQP): Having a family of intelligent features to improve query performance with minimal coding effort
• Automatic tuning uses built-in intelligence to continuously monitor queries executed on a database, and automatically improves their performance
• Performance recommendations allow customers to analyze a database and get a list of recommendations for improving the performance of that database
• Improve database availability with accelerated database recovery gets your databases online faster
• Compatibility certification: Upgrade and modernize your SQL Server database on-premises, in the cloud, and on the edge with the compatibility certification that eliminates risks of application compatibility

5. Unparalleled security

• Always encrypted with secure enclaves allows data computations inside a secure enclave on the server side, eliminating the need to move sensitive data outside the database.
• Add custom security to your application with advanced features like row-level security and dynamic data masking. You can track compliance for common organizational and regulatory standards with a vulnerability assessment.
• Transparent data encryption encrypts the data at rest. SQL Server ensure data files, log files, and even specific database backup files that are encrypted to prevent unauthorized access to the data in the transparent data encryption enabled database.

Check out what’s new in SQL Server documentation to learn about all of the scenarios that are available with the release candidate.

To download the release candidate bits for SQL Server 2019 when they are available, please review the documentation to learn more.

Customers are encouraged to deploy this free preview, try out features in this release, and provide feedback to the engineering team. Your feedback is very helpful in refining features to be the most useful.

Ready to learn more?

• To get started with SQL Server 2019 release candidate, you can find the download instructions on the SQL Server 2019 web page or use a SQL Server 2019 community technology preview virtual machine in Microsoft Azure.
• To experience SQL Server 2019 Big Data Clusters, use the bits provided for SQL Server 2019 CTP 3.2. The release candidate bits for SQL Server 2019 Big Data Clusters will be available soon.
• Check out what features were introduced in SQL Server community technology preview 3.2

The post SQL Server 2019 release candidate is now available appeared first on Microsoft SQL Server Blog.

SQL Server 2016 introduced a new feature called PolyBase that enables your SQL Server instance to process Transact-SQL queries that read data from Hadoop. The same query can also join with relational tables in your SQL Server. SQL Server 2019 CTP 2.0 introduces new connectors for PolyBase including SQL Server, Oracle, Teradata, MongoDB, Azure SQL DB, Azure SQL DW, Cosmos DB, and virtually any ODBC-accessible data source.

The arrival of the new connectors enables customers to leverage PolyBase for creating a virtual data hub for a wide variety of data sources within the enterprise. It’s very common to find an enterprise scenario where data from an Oracle database needs to be joined with data from another SQL Server instance for serving a business purpose. Starting with SQL Server 2019, you can leverage PolyBase for creating a source-agnostic solution using external tables. This opens multiple possibilities like building a modern data warehousing solution spanning SQL Server, Oracle, and Teradata.

Let’s consider an example where we have a set of tables in multiple heterogeneous data sources such as Oracle which stores information that needs to be joined with tables in SQL Server. You can create an external table in SQL Server that retrieves data from the Oracle database, making the Oracle data available as a virtual part of the SQL Server database. The screenshot below shows how you can create an External Table using the Create External Table Wizard in Azure Data Studio.

The ability to reference an external data source like an Oracle database table in an SQL Server database table opens multiple possibilities:

• Unified Security Model – Users now have the ability to access external tables in other data sources such as Oracle and implement a common security model for applications to access the data using SQL Server database roles and permissions.
• Data Virtualization – This allows you to virtualize an external entity as a SQL Server table to build a data hub that abstracts the need for the application to know the actual schema, data source location, and protocols required to access different data sources. You can take this concept further by creating SQL Server views for applications to access the external tables which would further abstract and protect the application from any schema and design changes in the external tables in the future date due to a migration or upgrade.
• Performance – This gives you the ability to push computation to Hadoop or other data sources which will improve your query performance. For example, querying Hadoop through an external table creates MapReduce jobs and leverages Hadoop’s distributed computational resources.
• Data Marts – You can use external tables to create data marts within SQL Server by pulling data into In-Memory OLTP cache tables with Columnstore Indexes for serving high-frequency queries. The cache tables can be refreshed on-demand or on a schedule using a SQL Agent job. Using big data clusters for SQL Server 2019 public preview provides this capability.
• Intelligent Data – SQL Server Machine Learning Services can be used for executing R and Python code on SQL Server to train and operationalize machine learning models. The ability to reference an external table in SQL Server with Machine Learning Services allows you to train predictive models over a wider variety and volume of data through the use of data marts for querying the external tables.

Leveraging SQL Server’s PolyBase connectors allows you to build a modern data warehouse using SQL Server which spans beyond just the SQL Server database and opens a number of scenarios that weren’t previously possible. Use SQL Server 2019 Public Preview to try out our new PolyBase feature for Oracle, Teradata, SQL Server, and MongoDB.

Learn more

• Overview of SQL Server PolyBase
• PolyBase Installation
• Configure PolyBase to access external data in Oracle

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