A few weeks ago, developers from around the world gathered for the Microsoft Build Conference. It was an amazing display of Microsoft’s products and cloud services to meet the needs of all types of applications. I missed the //build event this year because I found myself in San Francisco at the Red Hat Summit 2018. I can’t even imagine five years ago someone telling me I would represent Microsoft and SQL Server at an open-source based event.

Travis Wright, Jamie Reding, and I travelled to the event to speak and show amazing demos of SQL Server running on Red Hat Enterprise Linux (RHEL) and OpenShift. We were part of the Microsoft team attending the event to continue to show the great partnership we have and are building with Red Hat. Get an overview of the Microsoft presence at the summit from Robin Ginn’s blog post.

This was not the first time for Travis or Jamie attending this event. Microsoft demonstrated SQL Server on Linux at the Red Hat Summit 2016. Travis and Jamie both presented at the 2017 Summit for SQL Server on RHEL and Open Shift. I thought it would be interesting to write about our experiences and what we learned at this year’s Summit.

The presentation Travis and I gave on SQL Server was rich with demos. We worked hard on these demos, so we thought why not show everyone, not just the people at the Summit. We have packaged them all on the brand-new SQL Server YouTube channel. I’ve included links to these videos throughout the rest of this blog post.

It’s no longer “Why are you here?”

As the event ended, I asked Jamie and Travis what was one of the key differences between previous Summit events and this year. Travis summarized it as “Customers at the booth have switched from asking us Why are you here? and What is SQL Server? two years ago to specific questions about running SQL Server on RHEL/OpenShift.”

The most common question we received was “How do you install it?” I think I must have demonstrated at our booth 20 or more times the SQL Server deployment experience, along with the great set of tools we have available for SQL Server on Linux including SQL Server Operations Studio and mssql-cli. Check out this video that shows the complete deployment experience on RHEL as well as a survey of our tools. Cross-platform and open-source tools is how we roll these days!

Watch the video here.

Is it the same as SQL Server on Windows?

Part of the above video shows how to restore a database and connect with SQL Server tools. Linux admins and architects want to be more comfortable with how we integrate with Linux, and I met many folks at the Summit who didn’t know much SQL Server but knew Linux. Many of their colleagues who manage SQL Server in their environment couldn’t attend so they wanted to know if SQL Server is the same as on Windows?

My first answer to this question was “Tell your SQL friends to install SQL Server on Linux, backup their database on Windows, and just restore it on Linux”. Then fire up SQL Server Management Studio (SSMS), connect, and start running queries. When I showed this simple experience, it made the Linux folks incredibly happy. They saw our deployment experience aligned with package managers like yum. But they also could tell their SQL Server colleagues “Hey, it’s pretty much just SQL Server”. Since many of them didn’t use Windows, I then showed them SQL Operations Studio and mssql-cli which runs native on Linux and macOS as well as Windows. I heard comments like “Now that is impressive”.

We used these opportunities to talk beyond just the basics. We showed customers how fast SQL Server can be with technologies like Columnstore Indexes. Check out this demo with PowerBi and Columnstore.

Watch the video here.

We showed new SQL Server 2017 capabilities of intelligence performance like Automatic Tuning powered by Query Store. Check out this demo which includes built in performance telemetry through SQL Server Dynamic Management Views and charting capabilities in SQL Operations Studio.

Watch the video here.

We also talked about security and authentication. SQL Server supports both SQL Server authentication and integration with Active Directory. Watch this demo of SQL Server connecting with Active Directory Authentication.

Watch the video here.

What about new capabilities?

One of the most compelling new features of SQL Server 2016 and 2017 is in database Machine Learning Services. Keeping data associated with data science models and projects together with SQL Server provides security, data freshness, and scalability.
While SQL Server 2017 on Linux supports Native Scoring, support for R and Python did not make it into the release. But we are committed to bringing this type of feature for SQL Server on Linux. Check out this demo to see SQL Server on Linux with Python and Native Scoring with a real-world prediction example.

Watch the video here.

Does it perform?

As we described the architecture of SQL Server on Linux, the first question most people asked us was “Does it perform?”. Sometimes it is always best to answer questions like these with data.

The current top two 1TB TPC-H benchmarks are SQL Server 2017 on Linux 1 2. We talked to customers about how SQL Server can scale from your laptop to the biggest servers in the market. And we especially love showing how fast it can run and scale on enterprise-class machines. Check out this demo where Travis Wright shows how SQL Server in Linux can scan billions of rows and run aggregation functions in seconds on an HPE Superdome computer with 12TB of RAM and 480 CPUs.

Watch the video here.

How does SQL Server support Containers?

I have come to realize over the last few months how popular containers are spreading. It is fast becoming not just an interest, but part of production implementation plans. SQL Server is ready to be a part of this wave. Deploying in a container itself can be an amazingly easy way to get up to speed fast on SQL Server on Linux. In fact, I brought along my MacBook Pro with me and demonstrated to attendees the SQL Server on Mac challenge. For those who know me in the SQL Server community I’m sure you fell off your chair over this.

Running a single container is interesting, but to run containers in a production environment, you need something bigger. This is where Kubernetes comes in. And this is also where Red Hat’s Kubernetes-based system called OpenShift can make a difference. At the Summit, Microsoft announced a new managed OpenShift service in Azure and SQL Server fits right into this offering. Kubernetes and OpenShift have built-in high availability capabilities with shared persistent storage. SQL Server works well within this model. Try it yourself with this tutorial.

Always on Availability Groups (AG) is the flagship feature for SQL Server High Availability. So, we are working on new capabilities for SQL Server to integrate AGs with environments like OpenShift. Watch this demo to see this in action.

Licensing

One last interesting topic from attendees was around licensing and offers. Attendees wanted to know if SQL Server on Linux licensing were the same as with Windows. First, it is important to know the Editions of SQL Server are the same, I found many attendees did not know this:

• SQL Server Evaluation – A full-featured version of SQL Server for evaluation only purposes with a 180-time limit.
• SQL Server Express – A free, entry-level version of SQL Server for learning or building small desktop applications.
• SQL Server Developer – A full-featured version of SQL Server license only for development and testing.
• SQL Server Standard – The basic version of SQL Server for departments and small organizations. Limits exist for this edition compared to Enterprise, but many features previously only available in Enterprise are in Standard today.
• SQL Server Enterprise – The premium version of SQL Server for mission critical and applications that need maximum scalable performance and high-availability.

For a complete breakdown of editions, check out our documentation.

The licensing of these editions is the same as SQL Server on Windows. Licensing for containers was also a big question we saw at the summit and our SQL Server Licensing Guide shows licenses for containers is similar to licensing for virtual machines. There are some unique offers today for customers looking at SQL Server on Linux including Migrating from ORACLE, SQL Server on Linux subscription, and special offer with Red Hat Enterprise Linux.

The experience at the Red Hat Summit for me personally was humbling and rewarding. It is the first time for me to be at a big event where Microsoft was not the central focus. It forced me to work harder explaining to attendees the value of SQL Server on Linux and not assume they already knew SQL Server. I saw a new perspective from customers that want SQL Server to work within the natural ecosystem of Linux, while also not losing the excellent features and tools that have made SQL Server a force in the industry.

We also had an opportunity to meet several engineers from Red Hat. We shared ideas on how to make SQL Server and RHEL a better experience including discussions on performance monitoring and OpenShift.

I look forward to continuing getting the word out and showing off SQL Server on Linux. It was a remarkable achievement to launch last year, but it is the satisfaction of seeing it now in the mainstream of customer conversations that is most rewarding. It is no longer a “what” conversation. It is now a “when” project plan.

Get started with SQL Server on Linux or dive deeper with our free Virtual Academy Training for SQL Server on Linux.

• 1 1TB TPC-H benchmark result as of May 15th, 2018.
• 2 1TB TPC-H benchmark result as of May 15th, 2018

The post SQL Server at the Red Hat Summit 2018 appeared first on Microsoft SQL Server Blog.

Perhaps you saw the keynote at the recent PASS 2017 Summit where Microsoft demonstrated the performance of the world’s first enterprise-class “diskless database”. This demonstration showed how Microsoft, Hewlett Packard Enterprise (HPE), and SUSE Linux Enterprise Server partnered together to deliver > 5x performance on analytic queries directly against storage at up to 50 percent of the cost.

Recently HPE published a new world record 1TB TPC-H benchmark result¹ using this configuration with their DL380 Gen10 Server showing 1,009,065 QphH at an incredible price/performance of $0.47 USD per QphH. Performance and price are achieved by combining the power of SQL Server 2017, HPE’s scalable persistent memory, and SUSE Linux Enterprise 12 SP3 Persistent Memory Support.

HPE’s scalable persistent memory is a new innovation which combines standard memory with the persistence of standard storage. It allows database engines like SQL Server 2017 to retrieve data from its data files in a matter of seconds.

To see this technology in action, check out this video. To learn more about this amazing result and technology, read HPE’s blog post and SUSE’s blog post. To learn more about SQL Server on SUSE Linux Enterprise Server, check out SUSE’s SQL Server on Linux website.

SQL Server 2017 is the world leader in TPC-H performance, price, and value and continues to demonstrate that it is one of the fastest databases on the planet, in your cloud or ours.

References

• ¹ 1TB non-clustered TPC-H result http://www.tpc.org/3331 as of November 21, 2017.

The post SQL Server 2017: The world’s first enterprise-class diskless database appeared first on Microsoft SQL Server Blog.

SQL Server continues to be a proven leader in database performance for both analytic and OLTP workloads. SQL Server 2017 is fast, built-in with capabilities and features such as Columnstore indexes to accelerate analytic performance and Automatic Tuning and Adaptive Query Processing to keep your database application at peak speed.

Recently, Hewlett Packard Enterprise (HPE) announced a new world record TPC-H 10TB benchmark result¹ using SQL Server 2017 and Windows Server 2016 on their new DL580 Gen10 Server. This new amazing result at 1,479,748 Composite Query-per-Hour (QphH) was achieved at price/performance of .95 USD per QphH continuing to show SQL Server’s leadership in price and performance.

HPE also announced the first TPC-H 3TB result² on a 2-socket system using SQL Server 2017 and Windows Server 2016 with their DL380 Gen Server. They achieved a stellar 1,014,374 QphH on only 2-sockets. These results continue to show how powerful SQL Server can be to handle your analytic query workloads including data warehouses.

SQL Server also is a proven leader for OLTP workloads. Lenovo recently announced a new world-record TPC-E benchmark result³ using SQL Server 2017 and Windows Server 2016. This is now the #1 TPC-E result in both performance at 11,357 tpsE and price/performance at 98.83 USD per tpsE for systems with 4 sockets or more. This result was achieved on Lenovo’s ThinkSystem SR950 server using 4 sockets at 112 cores which represents a 25% performance gain from the previous 4 socket result with 16% more cores.

SQL Server 2017 is the world leader in TPC-E and TPC-H performance, price, and value and continues to demonstrate it is one of the fastest databases on the planet, in your cloud or ours.

References

• ¹ 10TB TPC-H non-clustered result as of November 9th, 2017.
• ² 3TB TPC-H non-clustered result as of November 9th, 2017.
• ³ TPC-E benchmark result as of November 9th, 2017.

The post SQL Server 2017: A proven leader in database performance appeared first on Microsoft SQL Server Blog.

“I feel the need, the need for speed”. That is a quote from the character Maverick, played by Tom Cruise, in one of my favorite movies, Top Gun. It makes me think of one of the top reasons why someone chooses a database engine. It must be fast and perform to the needs of all types of workloads accessing data.

SQL Server 2017 sets the standard when it comes to speed and performance. Based on the incredible work of SQL Server 2016 (See the blog series It Just Runs Faster), SQL Server 2017 is fast: built-in, simple, and online. Maybe you caught my presentation at Microsoft Ignite where I demonstrated 1 million transactions per minute on my laptop using the popular tool HammerDB¹ by simply installing SQL Server out of the box with no configuration changes (with the HammerDB client and SQL Server on the same machine!)

Consider for a minute all the built-in capabilities that power the speed of SQL Server. From a SQLOS scheduling engine that minimizes OS context switches to read-ahead scanning to automatic scaling as you add NUMA and CPUs. And we parallelize everything! From queries to indexes to statistics to backups to recovery to background threads like LogWriter. We partition and parallelize our engine to scale from your laptop to the biggest servers in the world.

Like the enhancements we made as described in It Just Runs Faster, in SQL Server 2016, we are always looking to tune our engine for speed, all based on customer experiences. Take, for example, indirect checkpoint, which is designed to provide a more predictable recovery time for a database. We boosted scalability of this feature based on customer feedback. We also made scalability improvements for parallel scanning and consistency check performance. No knobs required. Just built-in for speed.

One of the coolest performance aspects to built-in speed is online operations. We know you need to perform other maintenance tasks than just run queries, but keep your application up and running, so we support online backups, consistency checks, and index rebuilds. SQL Server 2017 enhances this functionality with resumable online index builds allowing you to pause an index build and resume it at any time (even after a failure).

SQL Server 2017 is faster than you think. SQL Server 2017 was designed from the beginning to run fast on popular Linux distributions such as Red Hat Enterprise Linux, SUSE Linux Enterprise, and Ubuntu whether that is on your server or a Docker Container. Don’t believe it? Check out our world record 1TB TPC-H benchmark result (non-clustered) for SQL Server on Red Hat Enterprise Linux. Even though this is our first release on Linux, we know how to configure and run SQL Server on Linux for maximum speed. Read our best practices guide for performance settings on Linux in our documentation. We know it performs well because our customers tell us. Read the amazing story of dv01 and how SQL Server on Linux exceeded their performance expectations as they migrated from PostgreSQL

One of the key technologies to achieve a result like this is columnstore indexes. This is one of the most powerful features of SQL Server for high-speed analytic queries and large databases. Columnstore indexes boost performance by organizing columnar data in a new way than traditional indexes, compressing data to reduce memory and disk footprint, filtering scans automatically through rowgroup elimination and processing queries in batches. SQL Server runs at warp speed for data warehouses and columnstore is the fuel. At Microsoft Ignite, I demonstrated how columnstore indexes can make PowerBI with Direct Query against SQL Server faster handling the self-service, ad-hoc nature of PowerBI queries.

SQL Server also excels at transaction processing, the heart of many top enterprise workloads. Got RAM? Not only does columnstore use in-memory technologies to achieve speed, but our In-Memory OLTP feature focuses on optimized access to memory-optimized tables. This feature is named OLTP, but it can be so much more. ETL staging tables, IoT workloads, table types (no more tempdb!), and “caching” tables. One of our customers was able to get a throughput of 1.2M batch requests/sec using SCHEMA_ONLY memory-optimized tables. To really boost transaction processing, also consider using SQL Server’s support for Persistent Memory (NVDIMM-N) and our optimization for transaction log (get ready for WRITELOG waits = 0!) performance. SQL Server 2017 supports any Persistent Memory technology supported on Windows Server 2016 and later releases.

Many customers I talk to have great performance when they first deploy SQL Server and their application. Keeping SQL Server fast and tuned is more of the challenge. SQL Server 2017 comes with features to keep you fast and tuned automatically and adaptively. Our Query Processing engine has all types of capabilities to create and build query plans to maximize the performance of your queries. We have created a new feature family in SQL Server 2017 to make it smarter, called Adaptive Query Processing. Imagine running a query that is not quite the speed you expect because of insufficient memory grants (which is a thorn in the side of many SQL Server users, as it can lead to a spill to tempdb). With Adaptive Query Processing, future executions of this query will have a corrected calculated memory grant avoiding the spill, all without requiring a recompilation of the query plan. Adaptive Query Processing handles other scenarios such as adaptive joins and interleaved execution of Table Valued Functions.

Another way to keep you tuned is the amazing feature we added in SQL Server 2016 called Query Store. Query Store provides built-in capabilities to track and analyze query performance all stored in your database. For SQL Server 2017, we made tuning adjustments to Query Store to make it more efficient based on learnings in our Azure SQL Database service where Query Store is enabled for millions of databases. We added wait statistics so now you have an end-to-end picture of query performance. Perhaps though the most compelling enhancement in SQL Server 2017 is Automatic Tuning. Parameter Sniffing got you down? Automatic Tuning uses Query Store to detect query plan regressions and automatically forces a previous plan that used to run fast. What I love about this feature is that even if you don’t have it turned on, you can see recommendations it has detected about plan regressions. Then you can either manually force plans that you feel have regressed or turn on the feature to have SQL Server do it for you.

SQL Server 2017 is the fastest database everywhere you need it. Whether it is your laptop, in your private cloud, or in our Azure public cloud infrastructure. Whether it is running on Linux, Windows, or Docker Containers, we have the speed to power any workload your application needs.

As I mentioned above, back in April, we announced our world record TPC-H 1TB data warehousing workload (non-clustered) for SQL Server 2017 running on a HPE ProLiant DL380 Gen9 using RedHat Enterprise Linux².

Perhaps you missed the announcement in June of 2017, of a new world record TPC-E benchmark result³ on SQL Server 2017 on Windows Server 2016 running on a Lenovo ThinkSystem SR650 continuing to demonstrate our leadership in database performance. This benchmark running on a 2 socket system using Intel’s Xeon Scalable Processors has set a new standard for price and performance, becoming the first TPC-E benchmark result ever to be under $100/tpsE.

We continued to show our proven speed for analytics by announcing in July of 2017 a new TPC-H 10TB (non-clustered) world record benchmark result⁴ of 1,336,109 QppH on Windows Server 2016 using a Lenovo ThinkSystem SR950 system with 6TB RAM and 224 logical CPUs.

While benchmarks can show the true speed of SQL Server, we believe it can perform well with your workload and maximize the computing power of your server. Perhaps you caught the session at Ignite where my colleague Travis Wright showed how we can scan a 180 Billion row table (from a 30TB database) in our labs in under 20 seconds powering 480 CPUs to 100% capacity. And if you don’t believe SQL Server is deployed in some of the biggest installations and servers in the world, I recently polled some of our field engineers, SQL Customer Advisor Team, and MVPs asking them for their largest SQL Server deployments. Over 30 people responded, and the average footprint of these installations was 3TB+ RAM on machines with 128 physical cores. Keep in mind that SQL Server on can theoretically scale to 24TB RAM on Windows and 64TB on Linux. And it supports the maximum CPUs of those systems (64 sockets with unlimited cores on Windows and 5120 logical CPUs on Linux). Look for more practical and fun demonstrations of the speed of SQL Server in the future.

It could be that you are consolidating your deployments and want to run SQL Server using Azure Virtual Machine, but not sure if the capacity is there for your performance needs. Consider that Azure Virtual machine has the new M-Series, which supports up to 128 vCPUs, 2TB RAM, and 64 Data Disks with a capacity of 160,000 IOPS. It could be that in your environment you want to scale out your read workload with Availability Group secondary replicas but don’t want to invest in Failover Clustering. SQL Server 2017 introduces the capability of read-scale availability groups without clustering supported both on Windows and Linux. Two other very nice performance features new to SQL Server 2017 are SSIS Scale Out, for those with data loading needs, and native scoring, which integrates machine learning algorithms into the SQL Server engine for maximum performance.

SQL Server 2017 brings to the database market a unique set of features and speed. A database engine that is fast, built-in with the power to scale, and even faster when taking advantage of technologies like columnstore Indexes and In-Memory OLTP. An engine that provides automation and adapts to keep you fast and tuned. And the fastest database everywhere you need it.

Stay tuned for future blog posts providing more details on SQL Server performance both on this blog and the bobsql blog.

Resources

• ¹HammerDB is an open-source performance tool. The demo and results shown are not official TPC benchmark results and all testing was done with a workload derived from the TPC-C benchmark.
• ²1TB TPC-H benchmark result: Hewlett Packard Enterprise; TPC, as of September 27th, 2017.
• ³TPC-E benchmark result: Lenovo Press; TPC as of September 27, 2017.
• ⁴10TB TPC-H benchmark result: Lenovo Press; TPC, as of September 27, 2017.

The post SQL Server 2017: Fast, faster, and the fastest database everywhere you need it appeared first on Microsoft SQL Server Blog.

In our seven community technology previews (CTPs) to date, SQL Server 2017 has delivered:

• Linux support for tier-1, mission-critical workloads – SQL Server 2017 support for Linux includes the same high availability solutions on Linux as Windows Server, including Always On availability groups integrated with Linux native clustering solutions like Pacemaker.
• Graph data processing in SQL Server – With the graph data features available in SQL Server 2017 and Azure SQL Database, customers can create nodes and edges, and discover complex and many-to-many relationships.
• Adaptive query processing – Adaptive query processing is a family of features in SQL Server 2017 that automatically keeps database queries running as efficiently as possible without requiring additional tuning from database administrators. In addition to the capability to adjust batch mode memory grants, the feature set includes batch mode adaptive joins and interleaved execution capabilities.
• Python integration for advanced analytics – Microsoft Machine Learning Services now brings you the ability to run in-database analytics using Python or R in a parallelized and scalable way. The ability to run advanced analytics in your operational store without ETL means faster time to insights for customers while easy deployment and rich extensibility make it fast to get up and running on the right model.

Key enhancements in Release Candidate 1

In SQL Server 2017 RC1, there were several feature enhancements of note:

• SQL Server on Linux Active Directory integration – With RC1, SQL Server on Linux supports Active Directory Authentication, which enables domain-joined clients on either Windows or Linux to authenticate to SQL Server using their domain credentials and the Kerberos protocol. Check out the getting started instructions.
• Transport Layer Security (TLS) to encrypt data – SQL Server on Linux can use TLS to encrypt data that is transmitted across a network between a client application and an instance of SQL Server. SQL Server on Linux supports the following TLS protocols: TLS 1.2, 1.1, and 1.0. Check out the getting started instructions.
• Machine Learning Services enhancements – In RC1, we add more model management capabilities for R Services on Windows Server, including External Library Management. The new release also supports Native Scoring.
• SQL Server Analysis Services (SSAS) – In addition to the enhancements to SSAS from previous CTPs of SQL Server 2017, RC1 adds additional Dynamic Management Views, enabling dependency analysis and reporting. See the Analysis Services blog for more information.
• SQL Server Integration Services (SSIS) on Linux – The preview of SQL Server Integration Services on Linux now adds support for any Unicode ODBC driver, if it follows ODBC specifications. (ANSI ODBC driver is not supported.)
• SQL Server Integration Services (SSIS) on Windows Server – RC1 adds support for SSIS scale out in highly available environments. Customers can now enable Always On for SSIS, setting up Windows Server failover clustering for the scale out master.

SQL Server 2017 for faster performance

SQL Server 2017 has several new benchmarks demonstrating faster performance than competitive databases, and against older versions of SQL Server:

• The world record TPC-H 1TB nonclustered data warehousing benchmark, achieved in April 2017 using SQL Server 2017 on Red Hat Enterprise Linux and HPE Proliant DL380 Gen9 hardware.
• A new, nonclustered TPC-H 10TB data warehousing workload benchmark achieved using SQL Server 2017 on Windows Server 2016 and Lenovo ThinkSystem SR950 hardware. At 1,336,109 QppH, this newly published benchmark is a world record.
• A new TPC-E benchmark achieved with SQL Server 2017 running on Windows Server 2016 on a Lenovo ThinkSystem SR650, a world record for 2 socket TPC-E results.

Streamline your DevOps using SQL Server 2017

In SQL Server 2017, we have introduced support for SQL Server on Linux-based containers, a benefit for customers using containers in development or production. We’re also working to help developers get started developing an app for SQL Server as fast as possible with installation instructions, code snippets, and other handy information.

On our new microsite DevOps using SQL Server, which launched today, developers and development managers can learn how to integrate SQL Server in their DevOps tasks. Find demos, documentation, and blogs, as well as videos and conference presentations. Or, join the DevOps conversation at our Gitter channels.

Customers are already benefitting from SQL Server 2017

In fact, with our Early Adoption Program, customers can develop new applications for SQL Server 2017 or add Linux support to existing applications, and get the support and end-user license agreement that they need to go into production on SQL Server right now. Here are some customers already benefitting from SQL Server 2017 on Linux:

• Convergent Computing – A system integrator and longtime Microsoft partner, Convergent Computing was able to achieve a much faster return on server and storage hardware investments than usual by moving some tier-2 applications to inexpensive, white box servers running SQL Server 2017 on Linux.
• dv01 – Financial technology startup dv01 started out with an open source database on a competitor cloud. But when it ran into performance and scale problems, SQL Server was able to give it 15X faster performance, plus in-database advanced analytics. And by moving to SQL Server 2017, dv01 could standardize its operating systems on Linux—all with an easy migration.

Get started with SQL Server 2017 RC1 today!

Try the release candidate of the SQL Server 2017 today! Get started with our updated developer tutorials that show you how to install and use SQL Server 2017 on macOS, Docker, Windows, and Linux and quickly build an app in a programming language of your choice. For more ways to get started, try the following:

• Watch our recent Security and Monitoring, Client Tools, SQL Server Agent, or Backup engineering town halls on demand
• Install on Red Hat Enterprise Linux
• Install on Ubuntu Linux
• Install on SUSE Linux Enterprise Server
• Pull and run a Linux Docker container on Linux, Windows, or macOS
• Download the preview for Windows
• Active Directory Authentication with SQL Server on Linux
• Sign up for the Early Adoption Program (EAP) – The Early Adoption Program is designed to help customers and partners evaluate new features in SQL Server 2017, and to build and deploy applications for SQL Server 2017 on Windows and Linux.

Have questions? Join the discussion of SQL Server 2017 at MSDN. If you run into an issue or would like to make a suggestion, you can let us know through Connect. We look forward to hearing from you!

The post First release candidate of SQL Server 2017 now available appeared first on Microsoft SQL Server Blog.

Leveraging intelligence out of the ever-increasing amounts of data can make the difference between being the next market disruptor or being relegated to the pages of history. Today at the Microsoft Data Amp online event, we will make several product announcements that can help empower every organization on the planet with data-driven intelligence. We are delivering a comprehensive data platform for developers and businesses to create the next generation of intelligent applications that drive new efficiencies, help create better products, and improve customer experiences.

I encourage you to attend the live broadcast of the Data Amp event, starting at 8 AM Pacific, where Scott Guthrie, executive VP of Cloud and Enterprise, and I will describe product innovations that integrate data and artificial intelligence (AI) to transform your applications and your business. You can stream the keynotes and access additional on-demand technical content to learn more about the announcements of the day.

Today, you’ll see three key innovation themes in our product announcements. The first is the close integration of AI functions into databases, data lakes, and the cloud to simplify the deployment of intelligent applications. The second is the use of AI within our services to enhance performance and data security. The third is flexibility—the flexibility for developers to compose multiple cloud services into various design patterns for AI, and the flexibility to leverage Windows, Linux, Python, R, Spark, Hadoop, and other open source tools in building such systems.

Hosting AI where the data lives

A novel thread of innovation you’ll see in our products is the deep integration of AI with data. In the past, a common application pattern was to create statistical and analytical models outside the database in the application layer or in specialty statistical tools, and deploy these models in custom-built production systems. That results in a lot of developer heavy lifting, and the development and deployment lifecycle can take months. Our approach dramatically simplifies the deployment of AI by bringing intelligence into existing well-engineered data platforms through a new computing model: GPU deep learning. We have taken that approach with the upcoming release of SQL Server, and deeply integrated deep learning and machine learning capabilities to support the next generation of enterprise-grade AI applications.

So today it’s my pleasure to announce the first RDBMS with built-in AI—a production-quality Community Technology Preview (CTP 2.0) of SQL Server 2017. In this preview release, we are introducing in-database support for a rich library of machine learning functions, and now for the first time Python support (in addition to R). SQL Server can also leverage NVIDIA GPU-accelerated computing through the Python/R interface to power even the most intensive deep-learning jobs on images, text, and other unstructured data. Developers can implement NVIDIA GPU-accelerated analytics and very sophisticated AI directly in the database server as stored procedures and gain orders of magnitude higher throughput. In addition, developers can use all the rich features of the database management system for concurrency, high-availability, encryption, security, and compliance to build and deploy robust enterprise-grade AI applications.

We have also released Microsoft R Server 9.1, which takes the concept of bringing intelligence to where your data lives to Hadoop and Spark, as well as SQL Server. In addition to several advanced machine learning algorithms from Microsoft, R Server 9.1 introduces pretrained neural network models for sentiment analysis and image featurization, supports SparklyR, SparkETL, and SparkSQL, and GPU for deep neural networks. We are also making model management easier with many enhancements to production deployment and operationalization. R Tools for Visual Studio provides a state-of-the-art IDE for developers to work with Microsoft R Server. An Azure Microsoft R Server VM image is also available, enabling developers to rapidly provision the server on the cloud.

In the cloud, Microsoft Cognitive Services enable you to infuse your apps with cognitive intelligence. Today I am excited to announce that the Face API, Computer Vision API, and Content Moderator are now generally available in the Azure Portal. Here are some of the different types of intelligence that cognitive services can bring to your application:

• Face API helps detect and compare human faces, organize faces into groups according to visual similarity, and identify previously tagged people in images.
• Computer Vision API gives you the tools to understand the contents of any image: It creates tags that identify objects, beings like celebrities or actions in an image, and crafts coherent sentences to describe it. You can now detect landmarks and handwriting in images. Handwriting detection remains in preview.
• Content Moderator provides machine-assisted moderation of text and images, augmented with human review tools.

Azure Data Lake Analytics (ADLA) is a breakthrough serverless analytics job service where you can easily develop and run massively parallel petabyte-scale data transformation programs that compose U-SQL, R, Python, and .NET. With no infrastructure to manage, you can process data on demand, scale instantly, and pay per job only. Furthermore, we’ve incorporated the technology that sits behind the Cognitive Services inside U-SQL directly as functions. Now you can process massive unstructured data, such as texthttps://www.microsoft.com/images, extract sentiment, age, and other cognitive features using Azure Data Lake, and query/analyze these by content. This enables what I call “Big Cognition—it’s not just extracting one piece of cognitive information at a time, and not just about understanding an emotion or whether there’s an object in an individual image, but rather it’s about integrating all the extracted cognitive data with other types of data, so you can perform powerful joins, analytics, and integrated AI.

Azure Data Lake Store (ADLS) is a no-limit cloud HDFS storage system that works with ADLA and other big data services for petabyte-scale data. We are announcing the general availability of Azure Data Lake Analytics and Azure Data Lake Store in the Azure North Europe region.

Yet another powerful integration of data and AI is the seamless integration of DocumentDB with Spark to enable machine learning and advanced analytics on top of globally distributed data. To recap, DocumentDB is a unique, globally distributed, limitless NoSQL database service in Azure designed for mission-critical applications. Designed as such from the ground up, it allows customers to distribute their data across any number of Azure regions worldwide, guarantees low read and write latencies, and offers comprehensive SLAs for data-loss, latency, availability, consistency, and throughput. You can use it as either your primary operational database or as an automatically indexed, virtually infinite data lake. The Spark connector understands the physical structure of DocumentDB store (indexing and partitioning) and enables computation pushdown for efficient processing. This service can significantly simplify the process of building distributed and intelligent applications at global scale.

I’m also excited to announce the general availability of Azure Analysis Services. Built on the proven business intelligence (BI) engine in Microsoft SQL Server Analysis Services, it delivers enterprise-grade BI semantic modeling capabilities with the scale, flexibility, and management benefits of the cloud. Azure Analysis Services helps you integrate data from a variety of sources—for example, Azure Data Lake, Azure SQL DW, and a variety of databases on-premises and in the cloud—and transform them into actionable insights. It speeds time to delivery of your BI projects by removing the barrier of procuring and managing infrastructure. And by leveraging the BI skills, tools, and data your team has today, you can get more from the investments you’ve already made.

Stepping up performance and security

Performance and security are central to databases. SQL Server continues to lead in database performance benchmarks, and in every release we make significant improvements. SQL Server 2016 on Windows Server 2016 holds a number of records on the Transaction Processing Performance Council (TPC) benchmarks for operational and analytical workload performance, and SQL Server 2017 does even better. I’m also proud to announce that the upcoming version of SQL Server will run just as fast on Linux as on Windows, as you’ll see in the newly published 1TB TPC-H benchmark world record nonclustered data warehouse performance achieved with SQL Server 2017 on Red Hat Enterprise Linux and HPE ProLiant hardware.

SQL Server 2017 will also bring breakthrough performance, scale, and security features to data warehousing. With up to 100x faster analytical queries using in-memory Columnstores, PolyBase for single T-SQL querying across relational and Hadoop systems, capability to scale to hundreds of terabytes of data, modern reporting, plus mobile BI and more, it provides a powerful integrated data platform for all your enterprise analytics needs.

In the cloud, Azure SQL Database is bringing intelligence to securing your data and increasing database performance. Threat Detection in Azure SQL Database works around the clock, using machine learning to detect anomalous database activities indicating unusual and potentially harmful attempts to access or exploit databases. Simply turning on Threat Detection helps customers make databases resilient to the possibility of intrusion. Other features of Azure SQL Database such as auto-performance tuning automatically implement, tune, and validate performance to guarantee the most optimal query performance. Together, our intelligent database management features help make your database more secure and faster automatically, freeing up scarce DBA capacity for more strategic work.

Simple, flexible multiservice AI solutions in the cloud

We are very committed to simplifying the development of AI systems. Cortana Intelligence is a collection of fully managed big data and analytics services that can be composed together to build sophisticated enterprise-grade AI and analytics applications on Azure. Today we are announcing Cortana Intelligence solution templates that make it easy to compose services and implement common design patterns. These solutions templates have been built on best practice designs motivated by real-world customer implementations done by our engineering team, and include Personalized Offers (for example, for retail applications), Quality Assurance (for example, for manufacturing applications), and Demand Forecasting. These templates accelerate your time to value for an intelligent solution, allowing you to deploy a complex architecture within minutes, instead of days. The templates are flexible and scalable by design. You can customize them for your specific needs, and they’re backed by a rich partner ecosystem trained on the architecture and data models. Get started today by going to the Azure gallery for Cortana Intelligence solutions.

Also, AppSource is a single destination to discover and seamlessly try business apps built by partners and verified by Microsoft. Partners like KenSci have already begun to showcase their intelligent solutions targeting business decision-makers in AppSource. Now partners can submit Cortana Intelligence apps at AppSource “List an app” page.

Cross-platform and open source flexibility

Whether on-premises or in the cloud, cross-platform compatibility is increasingly important in our customers’ diverse and rapidly changing data estates. SQL Server 2017 will be the first version of SQL Server compatible with Windows, Linux, and Linux-based container images for Docker. In addition to running on Windows Server, the new version will also run on Red Hat Enterprise Linux, SUSE Enterprise Linux Server, and Ubuntu. It can also run inside Docker containers on Linux or Mac, which can help your developers spend more time developing and less on DevOps.

Getting started

It has never been easier to get started with the latest advances in the intelligent data platform. We invite you to join us to learn more about SQL Server 2017 on Windows, Linux, and in Linux-based container images for Docker; Cognitive Services for smart, flexible APIs for AI; scalable data transformation and intelligence from Azure Data Lake Store and Azure Data Lake Analytics; the Azure SQL Database approach to proactive threat detection and intelligent database tuning; new solution templates from Cortana Intelligence; and precalibrated models for Linux, Hadoop, Spark, and Teradata in R Server 9.1.

Join our Data Amp event to learn more! You can go now to the Microsoft Data Amp online event for live coverage starting at 8 AM Pacific on April 19. You’ll also be able to stream the keynotes and watch additional on-demand technical content after the event ends. I look forward to your participation in this exciting journey of infusing intelligence and AI into every software application.

The post Delivering AI with data: the next generation of the Microsoft data platform appeared first on Microsoft SQL Server Blog.

By modernizing both your SQL Server data platform and your Windows Server OS, you can gain major cost savings and unprecedented performance boosts for workloads such as storage, business intelligence, and analytics. The strength of this combination has been shown in independent benchmark testing. As you can see in Figure 1, SQL Server 2016 on Windows Server 2016 leads the industry in both performance and price/performance ratio.

Figure 1: Best-in-class performance and price/performance ratio [1]

You can get these amazing results from a few key features that are built into the new versions:

• On the data platform side, SQL Server 2016 delivers in-memory performance, which gives you the power to run queries faster than ever before.
• Windows Server 2016 turbocharges this with Persistent Memory (aka Storage Class Memory), which provides 3x latency improvement.
• Storage Spaces Direct in Windows Server 2016 allows use of industry-standard servers with local storage as a highly available, scalable alternative to expensive storage area networks (SANs) — with read speeds that can exceed 25 GB/second.

SQL Server 2016 in-memory OLTP and 24 TB of Windows Server 2016 memory built in

In-memory processing was introduced in SQL Server 2014, and improvements in SQL Server 2016 make it much easier to accelerate your new, and now also your existing, applications. Revving up the speed potential of in-memory, Windows Server 2016 has built-in capability to provide in-memory with 24 terabytes of available server memory. Plus, new CPU maximums have been increased by three times so that you can run up to 640 CPU cores.

Windows Server 2016 Persistent Memory and Storage Spaces Direct

SQL Server professionals know that database transactions can be gated by log write speed. If the log is faster, more database updates are possible. Windows Server 2016 helps solve this with Persistent Memory, again adding direct value when you run SQL Server 2016 on Windows Server 2016.

When you think about significant cost savings over traditional storage, you’ll be interested to know that the new Windows Server 2016 Storage Spaces Direct lets you use local storage to create highly scalable and flexible storage solutions as shown in Figure 2. The ability to aggregate locally attached storage across the nodes in a failover cluster means you can deploy very large and highly available pools of storage from types of devices that you could not use before, such as inexpensive SATA SSD, and cutting-edge solutions like NVMe flash, which can plug directly into the PCIe bus inside the machine to create an NVMe fabric. Now SQL Server can scale to huge memory.

Figure 2: Enormous cost savings and blazing speed with Storage Spaces Direct

Persistent memory is a state-of the-art new technology, and Windows Server 2016 is exposing it for the first time. SQL Server is in lockstep with the innovation so you can exploit it to gain the fastest speed at the lowest cost. If you’re tied to hugely expensive third-party solutions, you can certainly appreciate the enormous value this brings.

Get it all!

SQL Server 2016 features are built in. You get great performance at less than one-tenth of the total cost of using Oracle to run the same transactional, data warehouse, data integration, business intelligence, and advanced analytics workloads. [2] With a Windows Server 2016 license, you also get everything built in: Hyper-V and advanced storage capabilities with no need to buy separate third-party storage solutions or virtualization technologies.

It’s all built right in. By listening to customers and creating new capabilities in both SQL Server 2016 and Windows Server 2016, Microsoft is tuning up your super vehicle to help get top performance and winning efficiency. Let ‘er rip!

Try it out for yourself:

• SQL Server virtual labs
• Windows Server 2016 free evaluation
• Windows Server 2016 virtual labs

Read more

• • Five reasons to run SQL Server 2016 on Windows Server 2016, part 4

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[1] Qualifications and references for TPC-H results: These are for nonclustered TPC-H results and are valid as of March 1, 2017. The first bar is for the Cisco result . The second bar is for the Lenovo result. The third bar is for the HPE result. The price/performance references are valid as of March 1, 2017, and details on the referenced results can be found at:
#1 nonclustered 1TB performance
#1 nonclustered 3TB performance
#1 nonclustered 30TB performance
#1 nonclustered 1TB price/performance
#1 nonclustered 3TB price/performance
#1 nonclustered 10TB price/performance
#1 nonclustered 30TB price/performance
#1 TPC-E price/performance

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