27 April 2017

Microsoft touts SQL Server 2017 as 'first RDBMS with built-in AI'

The 2017 Microsoft Product Roadmap

Many key Microsoft products reached significant milestones in 2016, with next-gen versions of SharePoint Server, SQL Server and Windows Server all being rolled out alongside major updates to the Dynamics portfolio and, of course, Windows. This year's product roadmap looks to be a bit less crowded, though major changes are on tap for Microsoft's productivity solutions, while Windows 10 is poised for another landmark update. Here's what to watch for in the coming months 

With a constantly changing, and increasingly diversifying IT landscape– particularly in terms of heterogeneous operating systems (Linux, Windows, etc.) - IT organizations must contend with multiple data types, different development languages, and a mix of on-premises/cloud/hybrid environments, and somehow simultaneously reduce operational costs. To enable you to choose the best platform for your data and applications, SQL Server is bringing its world-class RDBMS to Linux and Windows with SQL Server v.Next.

You will learn more about the SQL Server on Linux offering and how it provides a broader range of choice for all organizations, not just those who want to run SQL on Windows. It enables SQL Server to run in more private, public, and hybrid cloud ecosystems, to be used by developers regardless of programming languages, frameworks or tools, and further empowers ‘every person and every organization on the planet to achieve more.’

Bootcamp 2017 - SQL Server on Linux

Learn More about:

  • What’s next for SQL Server on Linux
  • The Evolution and Power of SQL Server 2016
  • Enabling DevOps practices such as Dev/Test and CI/CD  with containers
  • What is new with SQL Server 2016 SP1: Enterprise class features in every edition
  • How to determine which SQL Server edition to deploy based on operation need, not feature set

SQL Server on Linux: High Availability and security on Linux

Why Microsoft for your operational database management system?

When it comes to the systems you choose for managing your data, you want performance and security that won't get in the way of running your business. As an industry leader in operational database management systems (ODBMS), Microsoft continuously improves its offerings to help you get the most out of your ever-expanding data world.

Read Gartner’s assessment of the ODBMS landscape and learn about the Microsoft "cloud first" strategy. In its latest Magic Quadrant report for ODBMS, Gartner positioned the Microsoft DBMS furthest in completeness of vision and highest for ability to execute. Gartner Reprint of SQL Server 2017

Top Features Coming to SQL Server 2017
From Python to adaptive query optimization to the many cloud-focused changes (not to mention Linux!), Joey D'Antoni takes you through the major changes coming to SQL Server 2017.

Top three capabilities to get excited about in the next version of SQL Server

Microsoft announced the first public preview of SQL Server v.Next in November 2016, and since then we’ve had lots of customer interest, but a few key scenarios are generating the most discussion.

If you’d like to learn more about SQL Server v.Next on Linux and Windows, please join us for the upcoming Microsoft Data Amp online event on April 19 at 8 AM Pacific. It will showcase how data is the nexus between application innovation and intelligence—how data and analytics powered by the most trusted and intelligent cloud can help companies differentiate and out-innovate their competition.

In this blog, we discuss three top things that customers are excited to do with the next version of SQL Server.

1. Scenario 1: Give applications the power of SQL Server on the platform of your choice

With the upcoming availability of SQL Server v.Next on Linux, Windows, and Docker, customers will have the added flexibility to build and deploy more of their applications on SQL Server. In addition to Windows Server and Windows 10, SQL Server v.Next supports Red Hat Enterprise Linux (RHEL), Ubuntu, and SUSE Linux Enterprise Server (SLES). SQL Server v.Next also runs on Linux and Windows Docker containers opening up even more possibilities to run on public and private cloud application platforms like Kubernetes, OpenShift, Docker Swarm, Mesosphere DC/OS, Azure Stack, and Open Stack. Customers will be able to continue to leverage existing tools, talents, and resources for more of their applications.

Some of the things customers are planning for SQL Server v.Next on Windows, Linux, and Docker include migrating existing applications from other databases on Linux to SQL Server; implementing new DevOps processes using Docker containers; developing locally on the dev machine of choice, including Windows, Linux, and macOS; and building new applications on SQL Server that can run anywhere—on Windows, Linux, or Docker containers, on-premises, and in the cloud.

SQL Server on Linux - march 2017

2. Scenario 2: Faster performance with minimal effort

SQL Server v.Next further expands the use cases supported by SQL Server’s in-memory capabilities, In-Memory OLTP and In-Memory ColumnStore. These capabilities can be combined on a single table delivering the best Hybrid Transactional and Analytical Processing (HTAP) performance available in any database system. Both in-memory capabilities can yield performance improvements of more than 30x, enabling the possibility to perform analytics in real time on operational data.

In v.Next natively compiled stored procedures (In-memory OLTP) now support JSON data as well as new query capabilities. For the column store both building and rebuilding a nonclustered column store can now be done online. Another critical addition to the column store is support for LOBs (Large Objects).

SQL Server on Linux 2017

With these additions, the parts of an application that can benefit from the extreme performance of SQL Server’s in-memory capabilities have been greatly expanded! We also introduced a new set of features that learn and adapt from an application’s query patterns over time without requiring actions from your DBA.

3. Scenario 3: Scale out your analytics

In preparation for the release of SQL Server v.Next, we are enabling the same High Availability (HA) and Disaster Recovery (DR) solutions on all platforms supported by SQL Server, including Windows and Linux. Always On Availability Groups is SQL Server’s flagship solution for HA and DR. Microsoft has released a preview of Always On Availability Groups for Linux in SQL Server v.Next Community Technology Preview (CTP) 1.3.

SQL Server Always On availability groups can have up to eight readable secondary replicas. Each of these secondary replicas can have their own replicas as well. When daisy chained together, these readable replicas can create massive scale-out for analytics workloads. This scale-out scenario enables you to replicate around the globe, keeping read replicas close to your Business Analytics users. It’s of particularly big interest to users with large data warehouse implementations. And, it’s also easy to set up.

In fact, you can now create availability groups that span Windows and Linux nodes, and scale out your analytics workloads across multiple operating systems.

In addition, a cross-platform availability group can be used to migrate a database from SQL Server on Windows to Linux or vice versa with minimal downtime. You can learn more about SQL Server HA and DR on Linux by reading the blog SQL Server on Linux: Mission-critical HADR with Always On Availability Groups Mission Critical HADR .

To find out more, you can watch our SQL Server on Linux webcast Linux Webinars . Find instructions for acquiring and installing SQL Server v.Next on the operating system of your choice at www.microsoft.com/sqlserveronlinux http://www.microsoft.com/sqlserveronlinux . To get your SQL Server app on Linux faster, you can nominate your app for the SQL Server on Linux Early Adopter Program, or EAP. Sign up now to see if your application qualifies for technical support, workload validation, and help moving your application to production on Linux before general availability.

To find out more about SQL Server v.Next and get all the latest announcements, register now to attend Microsoft Data Amp—where Data Amp—- where Data data gets to work.

Microsoft announced the name and many of the new features in the next release of SQL Server at its Data Amp Virtual Event on Wednesday. While SQL Server 2017 may not have as comprehensive of a feature set as SQL Server 2016, there is still some big news and very interesting new features. The reason for this is simple -- the development cycle for SQL Server 2017 is much shorter than the SQL Server 2016 development cycle. The big news at Wednesday's event is the release of SQL Server 2017 later this year on both Windows and Linux operating systems.

Microsoft Data Platform Airlift 2017 Rui Quintino Machine Learning with SQL Server 2016 and R Services

I was able to quickly download the latest Linux release on Docker and have it up and running on my Mac during today's briefing. (I have previously written about the Linux release here.) That speed to development is one of the major benefits of Docker that Microsoft hopes developers will leverage when building new applications. Docker is just one of many open source trends we have seen Microsoft adopt in recent years with SQL Server. Wednesday's soft launch not only introduced SQL on Linux, but also includes Python support, a new graph engine and a myriad of other features.

First R, Now Python
One of the major features of SQL Server 2016 was the integration of R, an open source statistical analysis language, into the SQL Server database engine. Users can use the sp_execute_external_script stored procedure to run R code that takes advantage of parallelism in the database engine. Savvy users of this procedure might notice the first parameter of this stored procedure is @language. Microsoft designed this stored procedure to be open-ended, and now adds Python as the second language that it supports. Python combines powerful scripting with eminent readability and is broadly used by IT admins, developers, data scientists, and data analysts. Additionally, Python can leverage external statistical packages to perform data manipulation and statistical analysis. When you combine this capability with Transact-SQL (T-SQL), the result is powerful.

SQL Server 2017: Advanced Analytics with Python
In this session you will learn how SQL Server 2017 takes in-database analytics to the next level with support for both Python and R; delivering unparalleled scalability and speed with new deep learning algorithms built in. Download SQL Server 2017: https://aka.ms/sqlserver17linuxyt

Big Changes to the Cloud
It is rare for a Microsoft launch event to omit news about cloud services, and Wednesday's event was no exception. Microsoft Azure SQL Database (formerly known as SQL Azure), which is the company's Database as a Service offering, has always lacked complete compatibility with the on-premises (or in an Azure VM) version of SQL Server. Over time, compatibility has gotten much better, but there are still gaps such as unsupported features like SQL CLR and cross-database query.

SQL Server 2017: Security on Linux

The new solution to this problem is a hybrid Platform as a Server (PaaS)/Infrastructure as a Service (IaaS) solution that is currently called Azure Managed Instances. Just as with Azure SQL Database, the Managed Instances administrator is not responsible for OS and patching operations. However, the Managed Instances solution supports many features and functions that are not currently supported in SQL Database. One such new feature is the cross-database query capability. In an on-premises environment, multiple databases commonly exist on the same instance, and a single query can reference separate databases by using database.schema.table notation. In SQL Database, it is not possible to reference multiple databases in one query which has limited many migrations to the platform due to the amount of code that must be rewritten. Support for cross-database queries in Managed Instances simplifies the process of migrating applications to Azure PaaS offerings, and should thereby increase the number of independent software vendor (ISV) applications that can run in PaaS.

SQL Server 2017: HA and DR on Linux

SQL Server 2017: Adaptive Query Processing

Microsoft also showcased some of the data protection features in Azure SQL Database that are now generally available. Azure SQL Database Threat Detection detects SQL Injection, potential SQL Injection vulnerabilities, and anomalous login monitoring. This can simply be turned on at the SQL Database level by enabling auditing and configuring notifications. The administrator is then notified when the threat detection engine detects any anomalous behavior.

Graph Database
One of things I was happiest to see in SQL Server 2017 was the introduction of a graph database within the core database engine. Despite the name, relational databases struggle in managing relationships between data objects. The simplest example of this struggle is hierarchy management . In a classic relational structure, an organizational chart can be a challenge to model -- who does the CEO report to? With graph database support in SQL Server, the concept of nodes and edges is introduced. Nodes represent entities, edges represent relationships between any two given nodes, and both nodes and edges can be associated with data properties . SQL Server 2017 also uses extensions in the T-SQL language to support join-less queries that use matching to return related values.

SQL Server 2017: Building applications using graph data
Graph extensions in SQL Server 2017 will facilitate users in linking different pieces of connected data to help gather powerful insights and increase operational agility. Graphs are well suited for applications where relationships are important, such as fraud detection, risk management, social networks, recommendation engines, predictive analysis, dependence analysis, and IoT applications. In this session we will demonstrate how you can use SQL Graph extensions to build your application using graph data. Download SQL Server 2017: Now on Windows, Linux, and Docker https://www.microsoft.com/en-us/sql-server/sql-server-vnext-including-Linux

Graph databases are especially useful in Internet of Things (IoT), social network, recommendation engine, and predictive analytics applications. It should be noted that many vendors have been investing in graph solutions in recent years. Besides Microsoft, IBM and SAP have also released graph database features in recent years.

Adaptive Query Plans
One the biggest challenges of a DBA is managing system performance over time. As data changes, the query optimizer generates new execution plans which at times might be less than optimal . With Adaptive Query Optimization in SQL Server 2017, SQL Server can evaluate the runtime of a query and compare the current execution to the query's history, building on some of the technology that was introduced in the Query Store feature in SQL Server 2016 . For the next run of the same query, Adaptive Query Optimization can then improve the execution plan .

Because a change to an execution plan  that is based on one slow execution can have a dramatically damaging effect on system performance, the changes made by Adaptive Query Optimization are incremental and conservative. Over time, this feature handles the tuning a busy DBA may not have time to perform. This feature also benefits from Microsoft's management of Azure SQL Database because the development team monitors the execution data and the improvements that adaptive execution plans make in the cloud. They can then optimize the process and flow for adaptive execution plans in future versions of the on-premises product.

Are You a Business Intelligence Pro?
SQL Server includes much more than the database engine. Tools like Reporting Services (SSRS) and Analysis Services (SSAS) have long been a core part of the value proposition of SQL Server. Reporting Services benefited from a big overhaul in SQL Server 2016, and more improvements are coming in SQL Server 2017 with on-premises support for storage of Power BI reports in a SSRS instance. This capability is big news to organizations who are cloud-averse for various reasons. In addition, SQL Server 2017 adds support for the Power Query data sources in SSAS tabular models to expand. This capability means tabular models can store data from a broader range of data sources than it currently supports, such as Azure Blob Storage and Web page data.

2017 OWASP SanFran March Meetup - Hacking SQL Server on Scale with PowerShell

And More...
Although it is only an incremental release, Microsoft has packed a lot of functionality into SQL Server 2017. I barely mentioned Linux in this article for a reason: From a database perspective SQL Server on Linux is simply SQL Server. Certainly, there are some changes in infrastructure, but your development experience in SQL Server, whether on Linux, Windows or Docker, is exactly the same.

Keep your environment always on with sql server 2016 sql bits 2017

From my perspective, the exciting news is not just the new features that are in this version, but also the groundwork for feature enhancements down the road. Adaptive query optimization will get better over time, as will the graph database feature which you can query by using standard SQL syntax. Furthermore, the enhancements to Azure SQL Database with managed instances should allow more organizations to consider adoption of the database as a service option. In general, I am impressed with Microsoft's ability to push the envelope on database technology so shortly after releasing SQL Server 2016.

Nordic infrastructure Conference 2017 - SQL Server on Linux Overview

You can get started with the CTP by downloading the package for Docker, https://hub.docker.com/r/microsoft/mssql-server-windows/ or the Linux, https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-setup-red-hat platforms, or you can download the Windows release here https://www.microsoft.com/evalcenter/evaluate-sql-server-vnext-ctp .

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