Server SQL 2019 preview available.
SQL Server – Insights over all your data
SQL Server continues to embrace open source, from SQL Server 2017 support for Linux and containers to SQL Server 2019 now embracing Spark and HDFS to bring you a unified data platform. With SQL Server 2019, all the components needed to perform analytics over your data are built into a managed cluster, which is easy to deploy and it can scale as per your business needs. HDFS, Spark, Knox, Ranger, Livy, all come packaged together with SQL Server and are quickly and easily deployed as Linux containers on Kubernetes. SQL Server simplifies the management of all your enterprise data by removing any barriers that currently exist between structured and unstructured data.
Here’s how we make it easy for you to break down barriers to realized insights across all your data, providing one view of your data across the organization:
- Simplify big data analytics for SQL Server users. Server SQL 2019 makes it easier to manage big data environments. It comes with everything you need to create a data lake, including HDFS and Spark provided by Microsoft and analytics tools, all deeply integrated with SQL Server and fully supported by Microsoft. Now, you can run apps, analytics, and AI over structured and unstructured data – using familiar T-SQL queries or people familiar with Spark can use Python, R, Scala, or Java to run Spark jobs for data preparation or analytics – all in the same, integrated cluster.
- Give developers, data analysts, and data engineers a single source for all your data – structured and unstructured – using their favorite tools. With SQL Server 2019, data scientists can easily analyze data in SQL Server and HDFS through Spark jobs. Analysts can run advanced analytics over big data using SQL Server Machine Learning Services: train over large datasets in Hadoop and operationalize in SQL Server. Data scientists can use a brand new notebook experience running on the Jupyter notebooks engine in a new extension of Azure Data Studio to interactively perform advanced analysis of data and easily share the analysis with their colleagues.
- Break down data silos and deliver one view across all of your data using data virtualization. Starting in SQL Server 2016, PolyBase has enabled you to run a T-SQL query inside SQL Server to pull data from your data lake and return it in a structured format—all without moving or copying the data. Now in SQL Server 2019, we’re expanding that concept of data virtualization to additional data sources, including Oracle, Teradata, MongoDB, PostgreSQL, and others. Using the new PolyBase, you can break down data silos and easily combine data from many sources using virtualization to avoid the time, effort, security risks and duplicate data created by data movement and replication. New elastically scalable “data pools” and “compute pools” make querying virtualized data lighting fast by caching data and distributing query execution across many instances of SQL Server.
Enhanced performance, security, and availability
The SQL Server 2019 relational engine will deliver new and enhanced features in the areas of mission-critical performance, security and compliance, and database availability, as well as additional features for developers, SQL Server on Linux and containers, and general engine enhancements.
Industry-leading performance – The Intelligent Database
- The Intelligent Query Processing family of features builds on hands-free performance tuning features of Adaptive Query Processing in SQL Server 2017 including Row mode memory grant feedback, approximate COUNT DISTINCT, Batch mode on rowstore, and table variable deferred compilation.
- Persistent memory support is improved in this release with a new, optimized I/O path available for interacting with persistent memory storage.
- The Lightweight query profiling infrastructure is now enabled by default to provide per query operator statistics anytime and anywhere you need it.
Advanced security – Confidential Computing
- Always Encrypted with secure enclaves extends the client-side encryption technology introduced in SQL Server 2016. Secure enclaves protect sensitive data in a hardware or software-created enclave inside the database, securing it from malware and privileged users while enabling advanced operations on encrypted data.
- SQL Data Discovery and Classification is now built into the SQL Server engine with new metadata and auditing support to help with GDPR and other compliance needs.
- Certification Management is now easier using SQL Server Configuration Manager.
Mission-critical availability – High uptime
- Always On Availability Groups have been enhanced to include automatic redirection of connections to the primary based on read/write intent.
- High availability configurations for SQL Server running in containers can be enabled with Always On Availability Groups using Kubernetes.
- Resumable online indexes now support create operations and include database scoped defaults.
Developer experience
- Enhancements to SQL Graph include match support with T-SQL MERGE and edge constraints.
- New UTF-8 support gives customers the ability to reduce SQL Server’s storage footprint for character data.
- The new Java language extension will allow you to call a pre-compiled Java program and securely execute Java code on the same server with SQL Server. This reduces the need to move data and improves application performance by bringing your workloads closer to your data.
- Machine Learning Services has several enhancements including Windows Failover cluster support, partitioned models, and support for SQL Server on Linux.
Platform of choice
- Additional capabilities for SQL Server on Linux include distributed transactions, replication, Polybase, Machine Learning Services, memory notifications, and OpenLDAP support.
- Containers have new enhancements including use of the new Microsoft Container Registry with support for RedHat Enterprise Linux images and Always On Availability Groups for Kubernetes.
Server SQL 2019 support in Azure Data Studio
Expanded support for more data workloads in SQL Server requires expanded tooling. As Microsoft has worked with users of its data platform we have seen the coming together of previously disparate personas: database administrators, data scientists, data developers, data analysts, and new roles still being defined. These users increasingly want to use the same tools to work together, seamlessly, across on-premises and cloud, using relational and unstructured data, working with OLTP, ETL, analytics, and streaming workloads.
Azure Data Studio offers a modern editor experience with lightning fast IntelliSense, code snippets, source control integration, and an integrated terminal. It is engineered with the data platform user in mind, with built-in charting of query result sets, an integrated notebook, and customizable dashboards. Azure Data Studio currently offers built-in support for SQL Server on-premises and Azure SQL Database, along with preview support for Azure SQL Managed Instance and Azure SQL Data Warehouse.
Azure Data Studio is today shipping a new SQL Server 2019 Preview Extension to add support for select SQL Server 2019 features. The extension offers connectivity and tooling for SQL Server big data clusters, including a preview of the first ever notebook experience in the SQL Server toolset, and a new PolyBase Create External Table wizard that makes accessing data from remote SQL Server and Oracle instances easy and fast.
