Sounds like there should be a punchline right? Well, there is. Microsoft will be in full force at PASS Summit 2018, and this is the third blog post in a series describing the involvement of the Microsoft SQL Server engineering team there. You can read more about our keynotes and our Modernizing pre-conference seminar from this series. In this post, Ill talk more about the SQL Server engineering team sessions and participation in the famous Data Clinic (aka the SQL Clinic) covering everything from SQL Server 2019 to the practical and to the modern.

SQL Server 2019

You may have seen the buzz about the launch of SQL Server 2019 preview a few weeks ago. Our team will immerse you in the strategy and the details of SQL Server 2019 with the following sessions:

• The Roadmap of SQL Server This is your one stop shop for SQL Server 2019 all-up.
• The Future of SQL Server and Big Data Spark, SQL, and HDFS in the box. Is this even possible? Come find out how.
• Whats New in SQL Server on Linux and Containers People still dont believe SQL Server runs on Linux. See whats new in SQL Server on Linux and in containers.
• Deep Dive on SQL Server and Big Data Part 2 of big data clusters. See how we built this release and get a deep dive into how big data clusters work in this 2.5-hour, in-depth session.
• The Future of SQL Server Tooling Why is it called Azure Data Studio? Our team explains why and demonstrates the latest tools and features.

The Tiger team

Many people have asked me whether the Tiger team is some special ops unit of SQL Server. Well in a way, yes: they are all members of the SQL Server engineering team but with very special skills and customer experience, and the team always puts on amazing presentations. Check out the lineup:

• SQL Server Migrations Done the Right Way Can database compatibility change the way you think about migrations? Everything you need to know to modernize seamlessly.
• Architecting a Highly Available Database Platform with SQL Server See actual reference architectures for HADR.
• What’s New – Query Performance Insights Learn the latest and greatest for query tuning and diagnostics, along with the latest SQL Server 2019 Intelligent QP improvements.
• How it Works Availability Groups with SQL Server on Linux AGs can work on Linux. See the nuts and bolts.
• TempDB: The Good, The Bad, and The Ugly Managing tempdb doesnt have to be painful. Get the tips and advice you need.

Don’t miss these

SQL Server is no longer just a relational database engine, it is a modern data platform. And innovative, new parts of that platform are technologies like graph, Machine Learning Services, Java language support, in-memory columnstore, and containers. I promise you will walk away from these sessions and learn something you didnt know.

• Meet the Modern SQL Server: Graph and Machine Learning Services Graph and machine learning in SQL Server. Yes, it is true!
• Inside Columnstore Indexes The only 500 level talk at the conference. Let the brain-melting begin!
• Inside SQL Server with Containers See the internals of how containers work with SQL Server.
• SQL Server in Containers for Application Development and Deployment See the innovation of SQL containers in action.

The Data Clinic

In 2003, Ken Henderson approached me with the idea that we should not just speak at the PASS Summit, but also do something more interactive. That year we started something called the Service Center with just the two of us in a room answering questions. The first day, almost no one came. The next day it was announced at the keynote we would both be in a room answering questions. Later that day there were so many people in line to talk that the fire marshal had to kick people out of the room. Thus was born a unique opportunity to interact with Microsoft experts with no special add-on fees. Years later in 2009, Mark Souza approached me and asked if we could combine forces with the CAT team to create something even bigger. We called it the SQL Clinic. It grew into something legendary. Each year we had to find a bigger space to accommodate the crowds. CSS and CAT together would offer troubleshooting and architecture advice, ad-hoc with no special prep. We would help customers solve real-world problems or questions live, on laptop or whiteboards. Later, the rest of the SQL Engineering team joined the clinic to make it something special. Now at any time during the day, you can walk into the clinic area (a room is too small) and interact with SQL Engineering developers and program managers, architects, the Tiger Team, and the CAT team. And as the expansion of the SQL Server product grew into Azure, we renamed it the Data Clinic to account for BI and Azure services such as Azure SQL Database and CosmosDB. So whether you have a scratch, a broken leg, or need some serious surgery (ok Im just kidding, it is not a real clinic), come find us right across from the Expo entrance and get the answers you need or meet folks you have always wanted to meet from the engineering team. Or, drop by to chat during Data Clinic Happy Hour, 4-6 PM on Wednesday, November 7. Finally, you can read the history behind the clinic in the PASS v20 stories.

We will also staff an expo booth with demos and other opportunities to answer your questions. And we will also have short, 20-minute theater sessions in the booth, where will demonstrate all the technologies of SQL Server 2019 and our tools and youll get a chance to take home some fun giveaways.

Finally, I want to extend an invitation to anyone attending to the PASS Summit to come and meet our team. Whether you find us at the clinic, after a session, at the booth, or just in the hallways. Please dont hesitate to stop and meet us and ask us a question. SQL Server is only successful because of the incredible community that uses and supports this product. I can speak for everyone in our engineering team that we get it. We know this is your conference. And since this conference is for you, we dont want any question unanswered. This is my 16th PASS Summit and it is an event I look forward to every year. Our team hopes you walk away with the resources you need and enjoy all the content from Microsoft and our great community. See you in Seattle!

The post An Engineer and a tiger walk into a Clinic at the PASS Summit 2018 appeared first on SQL Server Blog.

Sounds like there should be a punchline right? Well, there is. Microsoft will be in full force at PASS Summit 2018, and this is the third blog post in a series describing the involvement of the Microsoft SQL Server engineering team there. You can read more about our keynotes and our Modernizing pre-conference seminar from this series. In this post, Ill talk more about the SQL Server engineering team sessions and participation in the famous Data Clinic (aka the SQL Clinic) covering everything from SQL Server 2019 to the practical and to the modern.

SQL Server 2019

You may have seen the buzz about the launch of SQL Server 2019 preview a few weeks ago. Our team will immerse you in the strategy and the details of SQL Server 2019 with the following sessions:

• The Roadmap of SQL Server This is your one stop shop for SQL Server 2019 all-up.
• The Future of SQL Server and Big Data Spark, SQL, and HDFS in the box. Is this even possible? Come find out how.
• Whats New in SQL Server on Linux and Containers People still dont believe SQL Server runs on Linux. See whats new in SQL Server on Linux and in containers.
• Deep Dive on SQL Server and Big Data Part 2 of big data clusters. See how we built this release and get a deep dive into how big data clusters work in this 2.5-hour, in-depth session.
• The Future of SQL Server Tooling Why is it called Azure Data Studio? Our team explains why and demonstrates the latest tools and features.

The Tiger team

Many people have asked me whether the Tiger team is some special ops unit of SQL Server. Well in a way, yes: they are all members of the SQL Server engineering team but with very special skills and customer experience, and the team always puts on amazing presentations. Check out the lineup:

• SQL Server Migrations Done the Right Way Can database compatibility change the way you think about migrations? Everything you need to know to modernize seamlessly.
• Architecting a Highly Available Database Platform with SQL Server See actual reference architectures for HADR.
• What’s New – Query Performance Insights Learn the latest and greatest for query tuning and diagnostics, along with the latest SQL Server 2019 Intelligent QP improvements.
• How it Works Availability Groups with SQL Server on Linux AGs can work on Linux. See the nuts and bolts.
• TempDB: The Good, The Bad, and The Ugly Managing tempdb doesnt have to be painful. Get the tips and advice you need.

Don’t miss these

SQL Server is no longer just a relational database engine, it is a modern data platform. And innovative, new parts of that platform are technologies like graph, Machine Learning Services, Java language support, in-memory columnstore, and containers. I promise you will walk away from these sessions and learn something you didnt know.

• Meet the Modern SQL Server: Graph and Machine Learning Services Graph and machine learning in SQL Server. Yes, it is true!
• Inside Columnstore Indexes The only 500 level talk at the conference. Let the brain-melting begin!
• Inside SQL Server with Containers See the internals of how containers work with SQL Server.
• SQL Server in Containers for Application Development and Deployment See the innovation of SQL containers in action.

The Data Clinic

In 2003, Ken Henderson approached me with the idea that we should not just speak at the PASS Summit, but also do something more interactive. That year we started something called the Service Center with just the two of us in a room answering questions. The first day, almost no one came. The next day it was announced at the keynote we would both be in a room answering questions. Later that day there were so many people in line to talk that the fire marshal had to kick people out of the room. Thus was born a unique opportunity to interact with Microsoft experts with no special add-on fees. Years later in 2009, Mark Souza approached me and asked if we could combine forces with the CAT team to create something even bigger. We called it the SQL Clinic. It grew into something legendary. Each year we had to find a bigger space to accommodate the crowds. CSS and CAT together would offer troubleshooting and architecture advice, ad-hoc with no special prep. We would help customers solve real-world problems or questions live, on laptop or whiteboards. Later, the rest of the SQL Engineering team joined the clinic to make it something special. Now at any time during the day, you can walk into the clinic area (a room is too small) and interact with SQL Engineering developers and program managers, architects, the Tiger Team, and the CAT team. And as the expansion of the SQL Server product grew into Azure, we renamed it the Data Clinic to account for BI and Azure services such as Azure SQL Database and CosmosDB. So whether you have a scratch, a broken leg, or need some serious surgery (ok Im just kidding, it is not a real clinic), come find us right across from the Expo entrance and get the answers you need or meet folks you have always wanted to meet from the engineering team. Or, drop by to chat during Data Clinic Happy Hour, 4-6 PM on Wednesday, November 7. Finally, you can read the history behind the clinic in the PASS v20 stories.

We will also staff an expo booth with demos and other opportunities to answer your questions. And we will also have short, 20-minute theater sessions in the booth, where will demonstrate all the technologies of SQL Server 2019 and our tools and youll get a chance to take home some fun giveaways.

Finally, I want to extend an invitation to anyone attending to the PASS Summit to come and meet our team. Whether you find us at the clinic, after a session, at the booth, or just in the hallways. Please dont hesitate to stop and meet us and ask us a question. SQL Server is only successful because of the incredible community that uses and supports this product. I can speak for everyone in our engineering team that we get it. We know this is your conference. And since this conference is for you, we dont want any question unanswered. This is my 16th PASS Summit and it is an event I look forward to every year. Our team hopes you walk away with the resources you need and enjoy all the content from Microsoft and our great community. See you in Seattle!

The post An Engineer and a tiger walk into a Clinic at the PASS Summit 2018 appeared first on SQL Server Blog.

We are excited to announce the October release of Azure Data Studio (formerly known as SQL Operations Studio) is now available.

Download Azure Data Studio and review the Release Notes to get started.

Note: If you are currently using the preview version, SQL Operations Studio, and would like to retain your settings when you upgrade to the latest version, follow these instructions. When you download Azure Data Studio, remember to enable preview features by default on first launch, and then you can disable in settings if you dont need it otherwise you will be missing preview experiences like Query Plans, certain extension support, and more.

Azure Data Studio is a new cross-platform desktop environment for data professionals using the family of on-premises and cloud data platforms on Windows, MacOS, and Linux. To learn more, visit our GitHub.

Azure Data Studio was announced Generally Available last month at Microsoft Ignite. If you missed the GA announcement, you can see “Azure Data Studio for SQL Server” on the SQL Server blog. You wont want to miss the great orthogonality matrix included comparing SSMS and Azure Data Studio and answers to many of your questions.

Check out this video for a general overview of Azure Data Studio.

In the Octobers version of the monthly release blog, we will be covering features shipped in the September GA release as well as what is new in the October release.

This includes:

• Announcing the SQL Server 2019 Preview extension
  • Support for SQL Server 2019 features including big data cluster support
  • Azure Data Studio Notebooks
  • SQL Server Polybase Create External Table Wizard
• Azure Resource Explorer
  • The Azure Resource Explorer view lets you browse data-related endpoints for your Azure accounts and create connections to them in Object Explorer. In this release Azure SQL Databases and servers are supported.
• SQL Server Agent Extension improvements
• Improve Object Explorer and Query Editor connectivity robustness
• Added Connection name option to provide an alternative display name in the Servers view-let
• VS Code source code refresh from 1.23 to 1.26.1 with Grid Layout and Improved Settings Editor (Preview)
• Thank you to contributors

For complete updates, refer to the Release Notes.

SQL Server 2019 Preview extension

As announced at Microsoft Ignite, one of the most exciting extensions to share in our September GA release was the release of the SQL Server 2019 Preview extension. If you were following the blog announcements, starting with SQL Server 2019 preview, SQL Server big data clusters allow you to deploy scalable clusters of SQL Server, Spark, and HDFS Docker containers running on Kubernetes.

These components are running side by side to enable you to read, write, and process big data from Transact-SQL or Spark. SQL Server big data clusters allow you to easily combine and analyze your high-value relational data with high-volume big data. To learn about all the excitement of SQL Server Big Data Clusters, follow the documentation here.

These experiences are built as an extension to Azure Data Studio. We can go into full depth about all the great capabilities this extension includes, but deep-diving into any one of these features can be a full blog post itself. Here is a high-level summary of these features, and then you can see a full demo of the features below.

• Support for SQL Server 2019 features including big data cluster support
  • Connect to the HDFS/Spark Gateway shipped with SQL Server 2019
  • Browse HDFS, upload files, save files, and launch useful actions such as Analyze in Notebook for CSV files
  • Submit Spark jobs from the dashboard or right-click on a HDFS/Spark connection in Object Explorer
• Azure Data Studio Notebooks
  • Create or open Notebooks using an integrated Notebook viewer. In this release the Notebook viewer supports connecting to local kernels and the SQL Server 2019 big data cluster only.
  • Use the PROSE Code Accelerator libraries in your Notebook to learn file format and data types for fast data preparation.
• SQL Server Polybase Create External Table Wizard
  • Create an external table and its supporting metadata structures with an easy to use wizard. In this release, remote SQL Server and Oracle servers are supported.

Demo of SQL Server 2019 preview extension capabilities:

To download the extension, follow the instructions here.

Introducing the Azure Resource Explorer

As part of our goal to unify data management experiences, we have made it easier to manage your Azure subscriptions through the Azure Resource Explorer. Originally shipped as an extension, this feature is now built into the core product of Azure Data Studio.

After downloading the latest version, you will now see an Azure icon on the left bar, which you can click on to navigate to the Azure Resource Explorer.

With this feature, you can now manage your Azure SQL Server, Azure SQL Database, and the recently GAed Azure SQL Managed Instance resources easily. By clicking the filter icon to the right of the explorer, you can select which subscriptions you want to have displayed.

After drilling down to your target SQL instance through the explorer, you can then click on the plug icon next to each SQL instance to open up the connection dialog to directly connect to that resource and instantly start querying.

To learn more about the Azure Resource Explorer, check out our documentation.

SQL Server Agent extension improvements

One of our engineering focuses is to improve our first party extensions, which include SQL Server Agent, SQL Server Profiler, and SQL Server Import. As one of the first steps, we have brought a lot of UI and functionality fixes in SQL Server Agent, particularly in the Edit Job experience.

Now you can edit your Job steps, schedules, alerts, and notifications within the dialog.

If you are an avid user of SQL Server Agent, this is your chance to have a say in the new Agent experience in Azure Data Studio. You can report an issue directly on GitHub or go to Help->Report an issue to report directly from the product. Let us know your daily scenarios and how we can help empower you to use SQL Agent on Azure Data Studio daily.

To learn more about SQL Agent or how to acquire the extension, check out our documentation.

Improve Object Explorer and Query Editor connectivity robustness

As part of addressing customer reported issues, we put an emphasis on improving connectivity robustness across Object Explorer and Query Editor. In particular, queries that lose a connection will automatically attempt to reconnect.

To see a full list of the connection investments, see below:

• Load properties used for OE node status by group instead of individually
• Clear OE node error message when populating children
• Reopen connections prior to creating query execution data readers
• Fix OE and Binding Queue reliability bugs

Custom connection name option to provide alternative name

As requested on our GitHub issues page, you can now provide friendly connection names for your connections. This is particularly useful if your connection instance is an ip address, very long, or want to hide the name of the server in a public facing demo or docs.

This shows up as the last input box in the connection dialog as you can see in the screenshot below:

This will then appear in your Servers pane.

VS Code refresh from 1.23 to 1.26.1

Since Azure Data Studio forks from Visual Studio Code, our team continues to periodically refresh Azure Data Studio with stable and mature VS Code releases. This directly benefits users especially in editor and configuration experience.

The latest refresh picks up the latest changes from the July release of Visual Studio Code. This was implemented in the September release, but is still good to highlight for those coming from SQL Operations Studio.

A summary of changes:

• Preview support of improved settings editor
• Small UI changes in grid editor

To see the full list of changes, you can view the updates at the Visual Studio Code updates page. Be sure to view the changes in also the May and June changes.

Thank you to contributors

If you would like to help make Azure Data Studio a great product, share any feedback or report issues through our Issues page. Our engineering team is regularly going through the untriaged issues and assigning issues into different monthly milestones so that you can know we are working on it. Your votes on issues helps us prioritize.

In addition to submitting issues, users can also contribute by submitting pull requests for potential quick fixes, and we welcome those submissions. Here is a shout-out to some of the customers who have submitted PRs that have been included in the product:

• AlexFsmnFeature: Ability to add connection name (#2332)
• AlexFsmnDisabled connection name input when connecting to a server. (#2566)
• philoushka forcenter the icon(#2760)
• anthonypants forTypo(#2775)
• kstolte forFix Invalid Configuration in Launch.json(#2789)
• kstolte forFixing a reference to SQL Ops Studio(#2788)

Contact us

If you have any feature requests or issues, please submit to our Github issues page. For any questions, feel free to comment below, message us on Gitter, or tweet us @AzureDataStudio.

The post The October release of Azure Data Studio is now available appeared first on SQL Server Blog.

Whether youre an experienced DBA with multiple certifications or just starting on your SQL Server DBA journey, you face challenges constantly for putting your organizations data to work. To turn the next-to-impossible into the practical, you need a comprehensive resource at your fingertips: SQL Server 2017 Administration Inside Out, by William Assaf, Randolph West, Sven Aelterman, and Mindy Curnutt. With this guide, you can get to know new and expanded SQL Server features like Columnstore, memory-optimized indexes, and Query Store, plus understand how to administer Microsoft Azure SQL Database or SQL Server 2017 virtual machines running on Azure.

Heres just some of what youll find in the ultimate guide to SQL Server 2017 (spoiler alertget free access to these chapters below):

Database server components

Even if you have a firm foundation in SQL Server configuration and administration, you want to keep the basics at-hand so you can easily refresh your memory. SQL Server 2017 Administration Inside Out provides a solid foundation in the infrastructure that makes up a database and how it works. The guide also offers helpful tips and best practices every DBA should know, like:

• Keep in mind that In-Memory OLTP requires RAM overhead thats two times the size of a memory-optimized object.
• Disable power saving everywhere. Power-saving settings in your operating system (OS) can result in poor query performance. Turn on High Performance at the OS and BIOS levels.
• Realize that Redundant Array of Independent Disks (RAID) shouldnt take the place of performing backups. It doesnt provide 100 percent protection from data loss.
• Ensure all TCP/IP traffic to and from SQL Server is encrypted. If youre using the Shared Memory Protocol with applications that are all on the same server, this isnt required.

Securing the server and data

Recognizing that more frequent and complex cyberattacks are targeting your environment, SQL Server 2017 Administration Inside Out covers the security capabilities of SQL Server 2017 comprehensively. The guide helps you play defense to protect your data from attack and minimize the damage should you suffer a data breach. For example, youll learn (or be reminded) that you can:

• Prevent common SQL injection attacks by making sure that all data is escaped, sanitized, and validated before input and that all SQL Server queries are parameterized.
• Use newer capabilities like Row-Level Security to restrict access through security policies based on group membership or execution context.
• Start SQL Server in single-user mode or with minimal configuration and remove the offending audit if that audit shuts down your SQL Server instance.

Performance tuning SQL Server

Ensuring your database performs at top speed is one of your highest priorities. You want to have an accurate and complete understanding of performance tuning concepts, the objects typically associated with tuning SQL Server database performance, and best practices that help your databases run at top speed. In SQL Server 2017 Administration Inside Out, youll gain this critical knowledge, including:

• The NOLOCK hint (or READ UNCOMMITTED isolation level) can return invalid data. Not only can uncommitted data be read, but committed data can be read twice or skipped. You also run the risk of returning corrupt data or finding the query has failed.
• You can use the Query Store feature to help you analyze execution plan performance by looking at live Query Store data as it happens as well as at the history of statement execution. Query Store is one of the features that first appeared in Azure SQL Database and then moved to SQL Server on-premises.

All this infoall in one place

This is only a small sample of the in-depth information you can find in SQL Server 2017 Administration Inside Out. Take the book for a test drive by downloading a free custom excerpt, including the three full chapters previewed above.

With this excerpt, youll also get an exclusive 50-percent discount code for the full 14-chapter e-book.

Download the free excerpt from SQL Server 2017 Administration Inside Out today.

The post DBA essentials—SQL Server 2017 security, performance tuning, and more—inside out appeared first on SQL Server Blog.

Each year thousands of data professionals make the annual visit to Seattle, Washington for the Professional Association for SQL Server Summit. This is a community driven organization and event but Microsoft has been a major contributor, sponsor, and part of this summit since its inception.This is the second post in a series leading up to the PASS Summit 2018, to learn more about the keynotes you can reference the most recent post Join us at PASS Summit 2018 Nov 6-9, 2018.

This year myself and other members of the Microsoft SQL Server Engineering and support teams will be at the PASS Summit again in a major way with keynotes, a pre-conference session, conference sessions, theater sessions, a great booth, and the always popular Data Clinic.

We have already received great interested in our pre-conference session called Modernize Your SQL Server with Bob Ward, the Tiger Team, and CSS Escalation Engineers on Tuesday, November 6th. You can read more about this session at the PASS Summit Session webpage.

Whether you have registered or not, it is not too late to register for this pre-conference seminar. It has been awhile since I have participated in a pre-conference seminar at the PASS Summit. Im excited what this one is about. We know that many of our customers are still using legacy releases of SQL Server like 2005, 2008, and 2008R2. If you are in this situation and wondering should I make the move to something new, something more modern, then this session is for you.

This session is not just an upgrade session! Sure we will show you how to migrate but our intention is to provide you with everything you need on how to modernize. Now you can learn the capabilities of SQL Server 2016, 2017, 2019, SQL Server on Linux, containers, Azure Virtual Machine, and Azure SQL Database in a way to know what features make a migration compelling. Our day also includes pre and post migration advice to maximize your investments and architectural guidance for key areas like performance, security, and HADR (the meat and potatoes of SQL Server).

The team of folks I have lined up to train you this day is nothing short of amazing. Every person that will be there to train you all have extensive customer experience and practical knowledge. This includes members of the Tiger Team such as Amit Banerjee, Pam Lahoud, Pedro Lopes, Argenis Fernandez, Sourabh Agarwal, and Vin Yu. In addition, we want you to be able to diagnose and troubleshoot problems with new technologies in a modern way so Im bringing in some of the top talent of CSS including Suresh Kandoth and Sean Gallardy. It will be a packed day but also an opportunity when you can talk to us 1:1 about your SQL Server installation base and what and how modernization will help you.

You will walk away from this session with the resources you need including all of our content and demo scripts and examples. We look forward to everyone coming to this session and I would love to meet each and every one of you personally to talk about your modernization plans for SQL Server.

Stay tuned for future blog posts where Ill show you the other great session and content the Microsoft SQL Server team has available at the PASS 2018 Summit.

The post Modernize your SQL Server at the PASS Summit 2018 appeared first on SQL Server Blog.

Get inspired by our industry-leading keynote speakers on the Microsoft data platform at PASS Summit 2018! There has never been a more exciting time for data professionals and developers as more organizations turn to data-driven insights to stay ahead and prepare for the future. But staying up to speed in this rapidly changing data landscape is a challenge. At PASS Summit you have the unique combination of community leaders sharing their real-world experience and Microsoft product teams providing deep insights into Microsoft data solutions.

For those attending this year, weve put together a quick guide below to all the relevant Data + AI sessions and clinics that you can look forward to. Cant wait until PASS Summit? Check out the Conference Sessions to get a taste of what you wont want to miss at PASS Summit 2018.

Register today and receive a $200 off discount with code: MSLOVESPASS

Pre-Conference activities – Tuesday, November 6

Modernize your SQL Server with Bob Ward, the Tiger Team, and CSS Escalation Engineers
Speaker: Bob Ward
This session will walk you through practical options for modernizing older releases of SQL Server. There are lots of choices and the engineers at Microsoft are here to guide you and find the right solutions to help you modernize your systems. In this full day session, Bob and the Microsoft engineering team will go over the benefits of newest releases, such as SQL Server 2016 and 2017, that provide built-in features for performance, intelligent diagnostics, security, HADR, and Machine Learning now accessible on Linux and Docker containers.

Wednesday, November 7

KEYNOTE: SQL Server and Azure Data Services: Harness the future with the ultimate hybrid platform for data and AI
Speaker: Rohan Kumar, Corporate Vice President of Azure Data, Microsoft
In todays world, the forces of Cloud, Data and AI are driving innovation and ushering in the era of the intelligent cloud and intelligent edge. Microsofts goal is to bring you the best products and tools to tackle these new opportunitieshelping you build a data infrastructure that supports your organization now and into the future. Join Rohan Kumar, Corporate Vice President of Azure Data, as he demonstrates the latest advances from Microsoft across SQL Server, Azure Data Services, Business Analytics and AI. Preview new products and features and see the innovations that make Microsoft the best data partner of any provider on-premises or at cloud scale.

Highlighted Session:The Roadmap for SQL Server
Speakers: Amit Banerjee, Bob Ward, Asad Khan
SQL Server 2017 has brought to market a new modern data platform including support for Linux, Docker Containers and rich features in intelligent performance, HADR, machine learning, and graph database. Come learn about the roadmap and new functionality planned for SQL Server including intelligent query processing, data virtualization, new features for mission critical security and HADR, and new scenarios for Linux and Docker Containers.

Thursday, November 8

KEYNOTE: Two Decades of Data Innovation: Celebrate the Evolution of the Data Platform and See into the Future with Extreme Cloud-Based Data Solutions
Speaker: Raghu Ramakrishnan, CTO for Data, Microsoft
Twenty years of PASS Summit and twenty-five years of SQL Server; together weve come a very long way. Join SQL Server team past and present as they take a journey through the evolution of the Microsoft data platform into the broad ecosystem you see today. You will hear from many of your familiar friends: Connor Cunningham, Bob Ward, Lara Rubbelke, Mark Souza and a few other surprises. Then buckle-up for a deep dive with Microsoft Data Platform CTO Raghu Ramakrishnan on the internals of our next evolution in engine architecture which will form the foundation for the next 25 years of the Microsoft data platform. However you interact with data be the first to look under the hood and see the future of data straight from the Azure Data engineering team.

Over 30 sessions are from the Microsoft data platform speakers; we invite you to come hear from Microsoft and community experts.

The post Join us at PASS Summit 2018 – Nov 6-9, 2018! appeared first on SQL Server Blog.

We are very excited to announce the public preview of SQL Server Management Studio 18.0. SSMS has been ported to the VS 2017 Isolated Shell, which brings with it many improvements to look and feel and accessibility. This release expands platform support to keep up with the announcements at Microsoft Ignite last week and includes bug fixes and feature updates across the board.

Download your copy of SSMS 18 public preview today.

New platform and feature support

• Support for SQL 2019
• Support for Azure SQL Managed Instance
• Azure SQL Database support
  • SLO/Edition
  • Support for updated Azure SQL SKUs
• Support for Always Encrypted with secure enclaves
• UTF-8 support on collation dialog

Shell improvements

• SSMS is based on the new VS 2017 Isolated Shell. This means a modern shell that unlocks all the accessibility features from both SSMS and VS 2017.
• Smaller download size (~400 MB). This is less than half of what SSMS 17.x is.
• SSMS can be installed in a custom folder. Currently, this is only available on the command line setup. Pass the extra argument to SSMS-Setup-ENU.exe, SSMSInstallRoot = C:MyFolder
• High DPI enabled by default.
• Better support for multiple monitors to ensure dialogs and windows pop up on the expected monitor.
• Isolation from SQL Engine. SSMS does not share components with SQL engine anymore. More isolation from SQL engine allows for more frequent updates.
• Package Ids no longer needed to develop SSMS Extensions.

Existing feature improvements

• Changed authentication mode from Storage Account Key to Azure AD.
• AUTOGROW_ALL_FILES config option for Filegroups.
• Removed lightweight pooling and priority boost options from GUI as these have been in the not recommended list for a long time.
• Switched to Windows Credential Manager for connection dialog MRU passwords.
• Exposed the backup checksum default property in the Default Settings Page under Server Properties dialog.
• Added actual time elapsed, actual vs estimated rows under Show Plan operator node, if they are available. This will allow actual plan to be consistent with Live Query Stats plan.
• Modified tooltip and added comment for Show Plan. Edit Query Button to indicate query text may be truncated if its over 4000 characters.
• Added logic to display the Materializer Operator (External Select).
• Added new show plan attribute BatchModeOnRowStoreUSed to easily identify queries that are using the batch-mode scan on rowstores feature.
• Rehash RTO (Estimated Recovery Time) and RPO (Estimated data loss) in SSMS AlwaysOn dashboard. Additional details here, Monitor performance for Always On Availability Groups.
• SMO
  • Scripting performance improvements
  • Extend SMO support for Resumable Index Creation
  • Added new event on SMO objects (Property Missing) to help application authors to detect SMO performance issues sooner
  • Exposed new property DefaultBackupCheckSum on the Configuration object which maps to backup checksum default in server configuration
• Several bug fixes in the following areas in addition to crashes/hangs:
  • XEvents
  • SSMS Options
  • SSMS Editor
  • Object Explorer
  • Backup/Restore/Attach/Detach database
  • General Azure SQL DB Support

For a full list of changes, see Release Notes.

• We have also deprecated some features, including:
• Database Diagram
• TSQL Debugger
• OSQL.exe
• Dreplay Admin UI
• Configuration Manager tools, SQL Server Configuration and Reporting Server Configuration Manager

The product may be installed side-by-side with SSMS 17.x for testing purposes. As a reminder, the use of pre-GA software in production environments is not supported.

SSMS will continue to be the flagship tool for managing and administering SQL Server on Windows environments. We will continue to make investments to fix bugs, optimize and modernize SSMS as new features get lighted up in SQL Server. Wed love to hear from you with any questions, comments or feature suggestions.

The post SSMS 18.0 public preview released appeared first on SQL Server Blog.

This post is authored by Amit Banerjee, Principal PM Manager, SQL Server andBob Ward,Principal Architect, Microsoft SQL Server Data Services.

SQL Server has provided enterprises the capability to manage all facets of their relational data. Over the years, we have increasingly seen a convergence for the need of combining heterogenous sets of relational and non-relational data to meet the needs of business scenarios. This requires setting up a unified data platform that transcends the boundaries of all types of data. Incidentally, we are also celebrating 25 years since SQL Server first shipped on Windows NT in 1993. The heart of SQL Server is mission critical performance, security, and availability and the use of our database platform in mission-critical environments is a testament to that fact. The SQL Server 2019 preview 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.

Earlier at Ignite, Microsoft announced the first public Community Technology Preview (CTP 2.0) of SQL Server 2019. For the first time, SQL Server 2019 comes with big data capabilities built-in, with Apache Spark and Hadoop Distributed File System (HDFS) in the boxextending SQL Server beyond a traditional relational database. This blog post covers the database engine features that are available in first public Community Technology Preview (CTP 2.0) of SQL Server 2019.

An Intelligent database providing Industry-leading performance

The Intelligent Query Processing suite builds on hands-free performance tuning features of Adaptive Query Processing in SQL Server 2017 like row mode memory grant feedback, batch mode on rowstore, table variable deferred compilation. We have identified common classes of query performance problems which could benefit from automatic corrective approaches during runtime based on changes in cardinality or through leveraging a feedback loop based on statistics from past executions. These are features that we have already started leveraging in Azure SQL Database and remain a top investment area for SQL Server 2019.

These are new changes to our query processor which are available with database compatibility level = 150 keeping in line with our database compatibility based upgrade promise. Database compatibility level provides an easy certification path for an existing application which helps with future upgrades to new releases where the database compatibility mode remains the same. This allows our customers to reduce the effort require to leverage capabilities in latest releases for availability, performance and security without having to worry about re-certifying the entire application on a newer release.

Persistent memory support is improved in this release with a new, optimized I/O path available for interacting with persistent memory storage. Any SQL Server file that is placed on a persistent memory device allows SQL Server to directly accesses the device, bypassing the storage stack of the operating system. This mode improves performance by significantly improving low latency input/output without any change to your application or database design. The ability for an existing database schema to leverage significant throughput gains allows existing applications with I/O bound bottlenecks.

The lightweight query profiling infrastructure is now enabled by default to provide per query operator statistics anytime and anywhere you need it. This provides the ability to look back in time and investigate query performance issues. We also decided to extend this capability to queries that are running on the server. This allows SQL Server administrators the ability to leverage Management Studios Live Query Statistics or the new DMF, sys.dm_exec_query_statistics_xml, to perform live troubleshooting of a current performance problem without needing to turn on any diagnostic data collection.

Enhanced security enabling Confidential Computing

Earlier this year, we announced Confidential Computing with Always Encrypted using Enclaves for Azure SQL Database. Now we have Always Encrypted with secure enclaves for SQL Server 2019 preview which 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 during advanced operations on encrypted data.

SQL Data Discovery and Classification is now built into the SQL Server engine with new metadata and auditing support which allows you to create solutions for key compliance requirements. We now have the ability for SQL Server catalog metadata to persist information about user-defined data classification labels.

Certificate management is now integrated into the SQL Server Configuration Manager, simplifying common tasks like deploying certificates across machines participating in a failover cluster instance or availability group. This removes the overhead of managing certificates separately on each node of the SQL Server failover cluster or availability group instance.

Mission-critical availability to keep your SQL Server running

Always On Availability Groups have been enhanced to include automatic redirection of connections based on read/write intent. This capability allows applications to be redirected to the primary replica without requiring a listener for handling scenarios where creation of a listener is not possible. This gives an opportunity for legacy applications which depend on a hard-coded server/host name but can still leverage Availability Groups on upgrade by redirection to the original replica after a failover.

High availability configurations for SQL Server running in containers can be enabled with Always On Availability Groups using Kubernetes as an orchestration layer. A Kubernetes operator deploys a Stateful Set including a container with mssql-server container and a health monitor. This introduces a tighter integration between SQL Server availability groups and Kubernetes. The operator will be available in the Microsoft Container Registry for SQL Server 2019 preview.

SQL Server Always On availability groups will support up to 5 synchronous replicas (1 primary and 4 synchronous secondary) with automatic failover support. This increases your ability to sustain simultaneous failures within or across data centers using SQL Servers high availability and disaster recovery features.

We are enhancing the capability of resumable online index DDL by allowing users to restart from the last point the rowstore index create was paused or failed. This allows you the ability to continue an online index build after an outage, database failover or even stopping the operation to free up resources on the SQL Server instance.

Clustered Columnstore indexes can now be created and rebuilt online to help improve uptime for hybrid transaction analytical processing (HTAP) environments.

SQL Server Machine Learning Services will now support clustering which allows you to have a highly available intelligent database for both OLTP and Machine Learning scenarios.

Enhancing the developer experience

We are introducing UTF-8 support, a widely used character encoding format, which can provide significant storage savings up to 50 percent for your character data. This allows you to compress your existing character data without the need to write additional routines and leverage external software to compress existing data. The ability to convert existing data to UTF-8 collations will allow existing databases to leverage this new capability for storage savings.

Enhancements to SQL Graph include match support with T-SQL MERGE and edge constraints.

We are extending the ability for SQL Server to leverage common programming languages by adding Java. We already have the ability for customers to leverage CLR, R and Python in earlier releases of SQL Server. 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. This extension is installed when you add the feature ‘Machine Learning Services (in-database)’ to your SQL Server instance. And since SQL Server on Linux uses the same database engine code, you can execute the same compiled Java classes on both SQL Server on Linux and Windows.

Machine Learning Services has several enhancements for partitioned models, and support for SQL Server on Linux. We now have the ability to process external scripts per partition which supports training many small models (one model per partition of data) instead of one large model and there by providing the ability to leverage SQL Server machine learning services across your partitions. This allows you to create a partitioned training strategy across archived data sets without having to incur the performance cost of training over all your data in a single monolithic operation.

Azure Data Studio, previously SQL Operations Studio, is now generally available. Azure Data Studio is a free tool that runs on Windows, macOS, and Linux, for managing SQL Server, Azure SQL Database, and Azure SQL Data Warehouse; wherever they’re running. SQL Server Management Studio 18.0 Preview will also be available for customers to continue managing SQL Servers with the support for SQL Server 2019 Public Preview.

Platform of choice

The preview container images of SQL Server will be available on the Microsoft Container Registry along with the new certified RHEL-based SQL Server container image available on the Red Hat Container Catalog. This allows users to leverage well known commands to setup a RHEL image with SQL Server running on it in a matter of seconds improving the ability to deploy and manage their environment where SQL Server running on Red Hat is a requirement.

We are introducing new connectors for PolyBase to external data for SQL Server, Oracle, Teradata, and MongoDB which allows you to create a unified data platform using the SQL Server database engine. We have redesigned PolyBase to allow you to connect to ODBC sources, other relational databases, NoSQL and Big Data environments which enables scenarios like building new application capabilities using SQL Server as a data hub without duplicating data and system of records.

Additional capabilities for SQL Server on Linux include distributed transactions, replication, Machine Learning Services, and OpenLDAP support. These features are driven by customer demand from customer running or evaluating SQL Server on Linux for production use.

We continue to listen to customer feedback and provide features, enhancements and innovation which help our customers run mission and business critical environments on SQL Server. Our new capabilities on SQL Server on Linux along with engine enhancements in SQL Server 2019 Preview features like columnstore statistics support for DBCC CLONEDATABASE, compression estimates for columnstore indexes, and new T-SQL built-in functions to discover details for page resource waits are examples of such customer driven engineering

We also wanted to point out that SQL Server 2008 and SQL Server 2008 R2 will be approaching end of support during July 2019. Microsoft is making options available for you to successfully modernize your data platform while staying secure on your existing environment. Please read about SQL Server 2008 and 2008 R2 End of Extended Support for more information.

Get started now

• Preview SQL Server 2019 for Windows, Linux, or Docker.
• Sign up for the Early Adoption Program to get advice and support for your project from SQL Server engineering, or to try SQL Server 2019 big data clusters.
• Download Azure Data Studio to get started with new SQL Server big data features like data virtualizations and notebooks.

The post SQL Server 2019: Celebrating 25 years of SQL Server Database Engine and the path forward appeared first on SQL Server Blog.

Starting in SQL Server 2017 with support for Linux and containers, Microsoft has been on a journey of platform and operating system choice. With SQL Server 2019 preview, we are making it easier to adopt SQL Server in containers by enabling new HA scenarios and adding supported Red Hat Enterprise Linux container images. Today we are happy to announce the availability of SQL Server 2019 preview Linux-based container images on Microsoft Container Registry, Red Hat-Certified Container Images, and the SQL Server operator for Kubernetes, which makes it easy to deploy an Availability Group.

Customers are adopting SQL Server containers for many different purposes from local development to testing in DevOps pipelines to deployment with container orchestrators such as Kubernetes. SQL Server in containers are great because of their consistent, isolated and reliable behavior across environments, ease of use, and ease of starting and stopping. Customized content can be built on top of SQL Server containers, and run without being affected by the rest of the environment. This isolation makes SQL Server in containers ideal for test deployment scenarios as well as DevOps processes.

SQL Server 2019 on Linux and container images

SQL Server 2019 is now available on Red Hat Enterprise Linux as a Red Hat Certified Container Images and Ubuntu-based container images enabling you to take advantage of the latest SQL Server engine innovations such as new SQL Graph features, and Data Discovery and Classification. We are also making it possible to adopt SQL Server in containers with existing scenarios such as Replication and Distributed Transaction which are now part of SQL Server 2019 on Linux.

Red Hat Certified Container images

With SQL Server 2019 we continue to provide more platform choices with the addition of official SQL Server on Red Hat Enterprise Linux as a Red Hat Certified Container Image. Previously, it was possible to build and run SQL Server in these containers, and now we are making it easier to get started by making it available on Microsoft Container Registry. You can run SQL Server container on Red Hat Enterprise Linux with the all the existing SQL Server on Linux container functionality, or extend it by using this image as a base image. Either way, you can now use this image for a variety of use cases from development on local environment to deployment on OpenShift with support from Red Hat and Microsoft when 2019 is generally available.

Said Ashesh Badani, vice president and general manager, Cloud Platforms, Red Hat, Were pleased to extend our collaboration with Microsoft to bring SQL Server to the worlds leading enterprise Linux platform as a production-ready certified container. With this addition to the Red Hat Container Catalog, our customers now have ready access to a mission-critical database on a trusted, reliable, and more secure operating system thats optimized for container workloads.

For more details on how to deploy SQL Server 2019 Red Hat Certified Container Images are available here. For access to the full list of Red Hat Certified Container images, visit the Red Hat Container Catalog.

SQL Server High Availability operator

SQL Server 2019 introduces the ability to deploy SQL Server containers with Always On Availability Groups on a Kubernetes cluster. The key functionalities for creation, management and health detection of the Availability Groups is encapsulated in the SQL Server HA container image (only Ubuntu images for CTP2.0). Red Hat Enterprise Linux-based images for Kubernetes or Red Hat OpenShift clusters will be made available in a subsequent CTP release of SQL Server 2019.

For more details on deployment, management and connecting to Availability Groups on an AKS cluster in Azure refer to the documentation.

Getting started with SQL Server in containers

• SQL Server 2019 CTP2.0 and 2017 images can be pulled from Microsoft Container Registry (MCR), our home for Microsoft-certified container images.
• For a complete list of SQL Server 2019 CTP2.0 improvements read What’s new in SQL Server documentation.
• For more details on how to deploy SQL Server 2019 in containers readQuickstart: Run SQL Server container images with Docker documentation.
• For more information on Red Hat Certified Container images, visit the Red Hat container catalog.

The post SQL Server 2019 preview containers now available appeared first on SQL Server Blog.

Yesterday at the Microsoft Ignite conference, we announced that SQL Server 2019 is now in preview and that SQL Server 2019 will include Apache Spark and Hadoop Distributed File System (HDFS) for scalable compute and storage. This new architecture that combines together the SQL Server database engine, Spark, and HDFS into a unified data platform is called a big data cluster.

For 25 years, Microsoft SQL Server has been powering data-driven organizations. As the variety of types of data and the volume of that data has risen, the number of types of databases has risen dramatically. Over the years, SQL Server has kept pace by adding support for XML, JSON, in-memory, and graph data in the database. It has become a flexible database engine that enterprises can count on for industry-leading performance, high availability, and security. However, a single instance of SQL Server was never designed or built to be a database engine for analytics on the scale of petabytes or exabytes. It also was not designed for scale-out compute for data processing or machine learning, nor for storing and analyzing data in unstructured formats, such as media files.

SQL Server 2019 preview extends its unified data platform to embrace big and unstructured data by deploying multiple instances of SQL Server together with Spark and HDFS as a big data cluster.

When Microsoft added support for Linux in SQL Server 2017, it opened the possibility of deeply integrating SQL Server with Spark, the HDFS, and other big data components that are primarily Linux-based. SQL Server 2019 big data clusters take that to the next step by fully embracing the modern architecture of deploying applications even stateful ones like a database as containers on Kubernetes. Deploying SQL Server 2019 big data clusters on Kubernetes ensures a predictable, fast, and elastically scalable deployment, regardless of where it is deployed. Big data clusters can be deployed in any cloud where there is a managed Kubernetes service, such as Azure Kubernetes Service (AKS), or in on-premises Kubernetes clusters, such as AKS on Azure Stack. Built-in management services in a big data cluster provide log analytics, monitoring, backup, and high availability through an administrator portal, ensuring a consistent management experience wherever a big data cluster is deployed.

The SQL Server 2019 relational database engine in a big data cluster leverages an elastically scalable storage layer that integrates SQL Server and HDFS to scale to petabytes of data storage. The Spark engine that is now part of SQL Server enables data engineers and data scientists to harness the power of open source data preparation and query programming libraries to process and analyze high-volume data in a scalable, distributed, in-memory compute layer.

Figure 1: SQL Server and Spark are deployed together with HDFS creating a shared data lake

Data integration through data virtualization

While extract, transform, load (ETL) has its use cases, an alternative to ETL is data virtualization, which integrates data from disparate sources, locations, and formats, without replicating or moving the data, to create a single “virtual” data layer. Data virtualization enables unified data services to support multiple applications and users. The virtual data layersometimes referred to as a data huballows users to query data from many sources through a single, unified interface. Access to sensitive data sets can be controlled from a single location. The delays inherent to ETL need not apply; data can always be up to date. Storage costs and data governance complexity are minimized.

SQL Server 2019 big data clusters with enhancements to PolyBase act as a data hub to integrate structured and unstructured data from across the entire data estateSQL Server, Azure SQL Database, Azure SQL Data Warehouse, Azure Cosmos DB, MySQL, PostgreSQL, MongoDB, Oracle, Teradata, , HDFS, and more using familiar programming frameworks and data analysis tools.

Figure 2: Data sources that can be integrated by PolyBase in SQL Server 2019

In SQL Server 2019 big data clusters, the SQL Server engine has gained the ability to natively read HDFS files, such as CSV and parquet files, by using SQL Server instances collocated on each of the HDFS data nodes to filter and aggregate data locally in parallel across all of the HDFS data nodes.

Performance of PolyBase queries in SQL Server 2019 big data clusters can be boosted further by distributing the cross-partition aggregation and shuffling of the filtered query results to compute pools comprised of multiple SQL Server instances that work together.

When you combine the enhanced PolyBase connectors with SQL Server 2019 big data clusters data pools, data from external data sources can be partitioned and cached across all the SQL Server instances in a data pool, creating a scale-out data mart. There can be more than one scale-out data mart in a given data pool, and a data mart can combine data from multiple external data sources and tables, making it easy to integrate and cache combined data sets from multiple external sources.

Figure 3: Using a scale-out data pool to cache data from external data sources for better performance

A complete AI platform built on a shared data lake with SQL Server, Spark, and HDFS

SQL Server 2019 big data clusters make it easier for big data sets to be joined to the dimensional data typically stored in the enterprise relational database, enabling people and apps that use SQL Server to query big data more easily. The value of the big data greatly increases when it is not just in the hands of the data scientists and big data engineers but is also included in reports, dashboards, and applications. At the same time, the data scientists can continue to use big data ecosystem tools while also utilizing easy, real-time access to the high-value data in SQL Server because it is all part of one integrated, complete system.

Figure 4: A scalable compute and storage architecture in SQL Server 2019 big data cluster

SQL Server 2019 big data clusters provide a complete AI platform. Data can be easily ingested via Spark Streaming or traditional SQL inserts and stored in HDFS, relational tables, graph, or JSON/XML. Data can be prepared by using either Spark jobs or Transact-SQL (T-SQL) queries and fed into machine learning model training routines in either Spark or the SQL Server master instance using a variety of programming languages, including Java, Python, R, and Scala. The resulting models can then be operationalized in batch scoring jobs in Spark, in T-SQL stored procedures for real-time scoring, or encapsulated in REST API containers hosted in the big data cluster.

SQL Server big data clusters provide all the tools and systems to ingest, store, and prepare data for analysis as well as to train the machine learning models, store the models, and operationalize them.
Data can be ingested using Spark Streaming, by inserting data directly to HDFS through the HDFS API, or by inserting data into SQL Server through standard T-SQL insert queries. The data can be stored in files in HDFS, or partitioned and stored in data pools, or stored in the SQL Server master instance in tables, graph, or JSON/XML. Either T-SQL or Spark can be used to prepare data by running batch jobs to transform the data, aggregate it, or perform other data wrangling tasks.

Data scientists can choose either to use SQL Server Machine Learning Services in the master instance to run R, Python, or Java model training scripts or to use Spark. In either case, the full library of open-source machine learning libraries, such as TensorFlow or Caffe, can be used to train models.

Lastly, once the models are trained, they can be operationalized in the SQL Server master instance using real-time, native scoring via the PREDICT function in a stored procedure in the SQL Server master instance; or you can use batch scoring over the data in HDFS with Spark. Alternatively, using tools provided with the big data cluster, data engineers can easily wrap the model in a REST API and provision the API + model as a container on the big data cluster as a scoring microservice for easy integration into any application.

Importantly, this entire pipeline all happens in the context of a SQL Server big data cluster. The data never leaves the security and compliance boundary to go to an external machine learning server or a data scientists laptop. The full power of the hardware underlying the big data cluster is available to process the data, and the compute resources can be elastically scaled up and down as needed.

Figure 5: A complete AI platform: SQL Server 2019 big data cluster

Azure Data Studio is an open-source, multi-purpose data management and analytics tool for DBAs, data scientists, and data engineers. New extensions for Azure Data Studio integrate the user experience for working with relational data in SQL Server with big data. The new HDFS browser lets analysts, data scientists, and data engineers easily view the HDFS files and directories in the big data cluster, upload/download files, open them, and delete them if needed. The new built-in notebooks in Azure Data Studio are built on Jupyter, enabling data scientists and engineers to write Python, R, or Scala code with Intellisense and syntax highlighting before submitting the code as Spark jobs and viewing the results inline. Notebooks facilitate collaboration between teammates working on a data analysis project together. Lastly, the External Table Wizard simplifies the process of creating external data sources and tables, including column mappings.

Conclusion

SQL Server 2019 big data clusters are a compelling new way to utilize SQL Server to bring high-value relational data and high-volume big data together on a unified, scalable data platform. Enterprises can leverage the power of PolyBase to virtualize their data stores, create data lakes, and create scalable data marts in a unified, secure environment without needing to implement slow, costly ETL pipelines. This makes data-driven applications and analysis more responsive and productive. SQL Server 2019 big data clusters provide a complete AI platform to deliver the intelligent applications that help make any organization more successful.

Figure 6: SQL Server 2019 big data cluster summary

Getting started

• Sign up for the Early Adoption Program if you would like to try out the new SQL Server 2019 big data clusters!
• Read more about the details of big data clusters in the SQL Server 2019 Big Data Clusters white paper.

The post Introducing Microsoft SQL Server 2019 Big Data Clusters appeared first on SQL Server Blog.