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- Herausgeber: Packt Publishing
- Kategorie: Fachliteratur
- Sprache: Englisch
Microsoft SQL Server 2016 is considered the biggest leap in the data platform history of the Microsoft, in the ongoing era of Big Data and data science.
This book introduces you to the new features of SQL Server 2016 that will open a completely new set of possibilities for you as a developer. It prepares you for the more advanced topics by starting with a quick introduction to SQL Server 2016's new features and a recapitulation of the possibilities you may have already explored with previous versions of SQL Server. The next part introduces you to small delights in the Transact-SQL language and then switches to a completely new technology inside SQL Server - JSON support. We also take a look at the Stretch database, security enhancements, and temporal tables.
The last chapters concentrate on implementing advanced topics, including Query Store, column store indexes, and In-Memory OLTP. You will finally be introduced to R and learn how to use the R language with Transact-SQL for data exploration and analysis.
By the end of this book, you will have the required information to design efficient, high-performance database applications without any hassle.
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Veröffentlichungsjahr: 2017
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Table of Contents
SQL Server 2016 Developer's Guide
SQL Server 2016 Developer's Guide
Copyright © 2017 Packt Publishing
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First published: March 2017
Production reference: 1150317
Published by Packt Publishing Ltd.
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Credits
Authors
Dejan Sarka
Miloš Radivojević
William Durkin
Copy Editor
Vikrant Phadke
Reviewer
Tomaž Kaštrun
Project Coordinator
Nidhi Joshi
Commissioning Editor
Amey Varangaonkar
Proofreader
Safis Editing
Acquisition Editor
Vinay Argekar
Indexer
Tejal Daruwale Soni
Content Development Editor
Aishwarya Pandere
Production Coordinator
Shraddha Falebhai
Technical Editor
Vivek Arora
About the Authors
Dejan Sarka, MCT and SQL Server MVP, is an independent trainer and consultant who focuses on the development of database and business intelligence applications, located in Ljubljana, Slovenia. Besides his projects, he spends around half of his time on training and mentoring. He is the founder of the Slovenian SQL Server and .NET Users Group. Dejan is the main author and co-author of many books and courses about databases and SQL Server. He is a frequent speaker at many worldwide events.
I would like to thank everybody involved in this book, especially to my co-authors, Miloš and William, to the content development editor, Aishwarya, and to the technical editor, Vivek.
Miloš Radivojević is a database consultant in Vienna, Austria. He is a Data Platform MVP and specializes in SQL Server for application developers and performance and query tuning. Currently, he works as a principal database consultant in bwin (GVC Holdings)—the largest regulated online gaming company in the world. Miloš is a co-founder of PASS Austria. He is also a speaker at international conferences and speaks regularly at SQL Saturday events and PASS Austria meetings.
I would like to thank my co-authors, Dejan Sarka and William Durkin. It has been a pleasure and privilege working with you guys! It was also a pleasure to work with editors, Aishwarya Pandere and Vivek Arora, in the production of this book. I'd also like to thank, Tomaž Kaštrun, for his prompt and helpful review. Finally, I would like to thank my wife, Nataša, my daughter, Mila, and my son, Vasilije, for all their sacrifice, patience, and understanding while I worked on this book.
William Durkin is a DBA and data platform architect for CloudDBA. He uses his decade of experience with SQL Server to help multinational corporations achieve their data management goals. Born in the UK and now based in Germany, he has worked as a database developer and DBA on projects ranging from single-server installations, up to environments spanning five continents using a range of high-availability solutions. William is a regular speaker at conferences around the globe, a Data Platform MVP and is the chapter leader of a German PASS chapter.
I would like to thank, Dejan and Miloš, for involving me in this book, it has been a challenge but a lot of fun! I would also like to thank Aishwarya and Vivek, for their editorial support. Last but certainly not least, thanks to my wife, Birgit, and son Liam, for your support and patience.
About the Reviewer
Tomaž Kaštrun is a SQL Server developer and data analyst. He has more than 15 years of experience in business warehousing, development, ETL, database administration and query tuning. He also has more than 15 years of experience in the fields of data analysis, data mining, statistical research, and machine learning.
He is a Microsoft SQL Server MVP for data platforms and has been working with Microsoft SQL Server since version 2000.
Tomaž is a blogger, author of many articles, co-author of statistical analysis books, speaker at community and Microsoft events, and avid coffee drinker.
Thanks to the people who inspire me, the community, and the SQL family. Thank you, dear reader, for reading this. For endless inspiration, thank you Rubi.
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Preface
Microsoft SQL Server is developing faster than ever in its nearly 30 years history. The newest version, SQL Server 2016, brings many important new features. Some of these new features just extend or improve features that were introduced in the previous versions of SQL Server, and some of them open a completely new set of possibilities for a database developer.
This book prepares the readers for more advanced topics by starting with a quick introduction of SQL Server 2016's new features and a recapitulation of the possibilities database developers had already in the previous versions of SQL Server. Then, the new tools are introduced. The next part introduces small delights in the Transact-SQL language, then the book switches to a completely new technology inside SQL Server—JSON support. This is where the basic chapters finish, and the more complex chapters start. Stretch database, security enhancements, and temporal tables are medium-level topics. The last chapters of the book cover advanced topics, including Query Store, columnstore indexes, and In-Memory OLTP. The final two chapters introduce R and R support in SQL Server and show how to use the R language for data exploration and analysis beyond that which a developer can achieve with Transact-SQL.
By reading this book, you will explore all of the new features added to SQL Server 2016. You will be capable of identifying opportunities for using the In-Memory OLTP technology. You will learn how to use columnstore indexes to get significant storage and performance improvements for analytical applications. You will be able to extend database design by using temporal tables. You will exchange JSON data between applications and SQL Server in a more efficient way. For vary large tables with some historical data, you will be able to migrate the historical data transparently and securely to Microsoft Azure by using Stretch Database. You will tighten security by using the new security features to encrypt data or to get more granular control over access to rows in a table. You will be able to tune workload performance more efficiently than ever with Query Store. Finally, you will discover the potential of R integration with SQL Server.
What this book covers
Chapter 1, Introduction to SQL Server 2016, very covers briefly the most important features and enhancements, not only those for developers. We want to show the whole picture and point where things are moving on.
Chapter 2, Review of SQL Server Features for Developers, is a brief recapitulation of the features available for developers in previous versions of SQL Server and serves as a foundation for an explanation of the many new features in SQL Server 2016. Some best practices are covered as well.
Chapter 3, SQL Server Tools, helps you understand the changes in the release management of SQL Server tools and explores small and handy enhancements in SQL Server Management Studio (SSMS). It also introduces RStudio IDE, a very popular tool for developing R code, and briefly covers SQL Server Data Tools (SSDT), including the new R Tools for Visual Studio (RTVS), a plugin for Visual Studio, which enables you to develop R code in an IDE that is common and well-known among developers that use Microsoft products and languages.
Chapter 4, Transact-SQL Enhancements, explores small Transact-SQL enhancements: new functions and syntax extensions, ALTER TABLE improvements for online operations, and new query hints for query tuning.
Chapter 5, JSON Support in SQL Server, explores the JSON support built into SQL Server. This support should make it easier for applications to exchange JSON data with SQL Server.
Chapter 6, Stretch Database, helps you understand how to migrate historical or less accessed data transparently and securely to Microsoft Azure by using the Stretch Database (Stretch DB) feature.
Chapter 7, Temporal Tables, introduces support for system-versioned temporal tables based on the SQL:2011 standard. We’ll explain how this implemented in SQL Server is and demonstrates some use cases for it (for example, a time-travel application).
Chapter 8, Tightening the Security, introduces three new security features. With Always Encrypted, SQL Server finally enables full data encryption. Row-level security on the other side restricts which data in a table can be seen by specific user. Dynamic data masking is a soft feature that limits sensitive data exposure by masking it to non-privileged users.
Chapter 9, Query Store, guides you through Query Store, and helps you to troubleshoot and fix performance problems that are related to execution plan changes.
Chapter 10, Columnstore Indexes, revises the columnar storage and then explores the huge improvements for columnstore indexes in SQL Server 2016: updateable nonclustered columnstore indexes, columnstore indexes on in-memory tables, and many other new features for operational analytics.
Chapter 11, Introducing SQL Server In-Memory OLTP, describes a feature introduced in SQL Server 2014 that is still underused: the In-Memory database engine, which provides significant performance gains for OLTP workloads.
Chapter 12, In-Memory OLTP Improvements in SQL Server 2016, describes all of the improvements of the In-Memory OLTP technology in SQL Server 2016, which extend the number of potential use cases and allow implementation with less development effort and risk.
Chapter 13, Supporting R in SQL Server, introduces R Services and the R language. It explains how SQL Server R Services combine the power and flexibility of the open source R language with enterprise-level tools for data storage and management, workflow development, and reporting and visualization.
Chapter 14, Data Exploration and Predictive Modeling with R in SQL Server, shows how you can use R for advanced data exploration and manipulation, and for statistical analysis and predictive modeling that is way beyond what is possible when using T-SQL language.
What you need for this book
In order to run all of the demo code in this book, you will need SQL Server 2016 Developer or Enterprise Edition. In addition, you will extensively use SQL Server Management Studio. You will also need the RStudio IDE and/or SQL Server Data Tools with R Tools for Visual Studio plug-in.
Who this book is for
This book is aimed at database developers and solution architects who plan to use new SQL Server 2016 features or simply want to know what is now available and which limitations from previous versions have been removed. An ideal book reader is an experienced SQL Server developer, familiar with features of SQL Server 2014, but this book can be read by anyone who has an interest in SQL Server 2016 and wants to understand its development capabilities.
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Chapter 1. Introduction to SQL Server 2016
SQL Server is the main relational database management product from Microsoft. It has been around in one form or another since the late 1980s (developed in partnership with Sybase), but as a standalone Microsoft product since the early 1990s. During the last 20 years, SQL Server has changed and evolved, gaining newer features and functionality along the way.
The SQL Server we know today is based on what was arguably the most significant (r)evolutionary step in its history, with the release of SQL Server 2005. The changes that were introduced have allowed the versions that followed the 2005 release to take advantage of newer hardware and software improvements such as: 64-bit memory architecture, better multi-CPU and multi-core support, as concludes the overview of programming better alignment with the .NET framework, and many more modernizations in general system architecture.
The incremental changes introduced in each subsequent version of SQL Server have continued to improve upon this solid foundation. Fortunately, Microsoft have changed their release cycle for multiple products, including SQL Server, resulting in shorter timeframes between releases. This has, in part, been due to Microsoft's focus on their much reported "Mobile First, Cloud First" strategy. This strategy, together with the development of the cloud version of SQL Server "Azure SQL Database", has forced Microsoft into a drastically shorter release cycle. The advantage of this strategy is that we are no longer required to wait three to five years for a new release (and new features). There have been releases every two years since SQL Server 2012 was introduced, with multiple releases of Azure SQL Database in between the real versions. While we can be pleased that we no longer need to wait for new releases, we are also at a distinct disadvantage. The rapid release of new versions and features leaves us developers with ever decreasing periods of time to get to grips with the shiny new features. Previously, versions had many years between releases, allowing us to build up a deeper knowledge and understanding of the available features before having to consume new information.
In this chapter, we will introduce what's new inside SQL Server 2016. We will outline features that are brand new in this release of the product and look at features that have been extended or improved upon.
We will be outlining new features in the following areas:
Security
The last few years have provided frequent demonstrations of the importance of security in IT. Whether we consider the repercussions of recent, high profile data leaks, or the multiple cases of data theft by hacking. While no system is completely impenetrable, we should always consider how we can improve security in the systems we build. These considerations are wide-ranging and sometimes even dictated by rules, regulations, and laws. Microsoft has responded to the increased focus on security by delivering new features to assist developers and DBAs in their search for more secure systems. The security features in SQL Server 2016 have been designed to make improving the security of SQL Server based solutions even easier to implement.
Row Level Security
The first technology that has been introduced in SQL Server 2016 to address the need for increased and improved security is Row Level Security (RLS). RLS provides the ability to control access to the rows in a table based on the user executing a query. With RLS it is possible to implement a filtering mechanism on any table in a database completely transparently to any external application or direct T-SQL access. The ability to implement such filtering without having to redesign a data access layer allows system administrators to control access to data at an even more granular level than before.
The fact that this control can be achieved without any application logic redesign makes this feature potentially even more attractive to certain use cases. RLS also makes it possible, in conjunction with the necessary auditing features, to lock down a SQL Server database so that even the traditional "god-mode" sysadmin cannot access the underlying data.
Note
Further details for Row Level Security can be found in Chapter 8, Tightening the Security.
Dynamic Data Masking
The second security feature that we will be covering is Dynamic Data Masking (DDM). DDM allows the system administrator to define column level data masking algorithms that prevent users from reading the sensitive content of columns, while still being able to query the rows themselves. This feature seems to have been initially aimed at allowing developers to work with a copy of production data without having the ability to actually see the underlying data. This can be particularly useful in environments where data protection laws are enforced (for example, credit card processing systems, medical record storage). The data masking occurs for unauthorized users at query runtime and does not affect the stored data of a table. This means that it is possible to mask a multi-terabyte database through a simple DDL statement, rather than resorting to the previous solution of physically masking the underlying data in the table we want to mask. The current implementation of DDM provides the ability to define a fixed set of functions to columns of a table, which will mask data when a masked table is queried. If a user has permission to view the masked data, then the masking function(s) are not run, whereas a user without those permissions will be provided with the data as seen through the defined masking functions.
Note
Further details for Dynamic Data Masking can be found in Chapter 8, Tightening the Security.
Always Encrypted
The third major security feature to be introduced in SQL Server 2016 is Always Encrypted. Encryption with SQL Server was previously a (mainly) server-based solution. Databases were either protected with encryption at the database level (the entire database was encrypted) or at the column level (single columns had an encryption algorithm defined). While this encryption was and is fully functional and safe, crucial portions of the encryption process (for example, encryption certificates) are stored inside SQL Server. This effectively gave the owner of a SQL Server instance the potential ability to gain access to this encrypted data; if not directly, there was at least an increased surface area for a potential malicious access attempt. As more and more companies moved into hosted service and cloud solutions (for example, Microsoft Azure), the old encryption solutions no longer provided the required level of control and security. Always Encrypted was designed to bridge this security gap by removing the ability of an instance owner to gain access to the encryption components. The entirety of the encryption process was moved outside SQL Server and resides on the client-side. Previously, you could achieve a similar effect using a homebrew solution, but Always Encrypted provides a fully integrated encryption suite into both the .NET Framework and SQL Server. Whenever data is defined as requiring encryption, the data is encrypted within the .NET Framework and only sent to SQL Server after encryption has occurred. This means that a malicious user (or even system administrator) will only ever be able to access encrypted information should they attempt to query data stored via Always Encrypted.
Note
Further details for Always Encrypted can be found in Chapter 8, Tightening the Security.
This concludes the overview of the three main security enhancements inside SQL Server 2016. Microsoft has made some positive progress in this area. While no system is completely safe, and no single feature can provide an all-encompassing solution, each of these three features provide a further option in building up, or improving upon, any system's current security level. As mentioned for each feature, consult the dedicated chapter to explore how each feature functions and how they may be used in your environments.
Engine features
The engine features section is traditionally the most important, or interesting, for most DBAs or system administrators when a new version of SQL Server is released. However, there are also numerous engine feature improvements that have tangential meaning for developers too. So, if you are a developer, don't skip this section or you may miss some improvements that could save you some trouble later on!
Query Store
The Query Store is possibly the biggest new engine feature to come with the release of SQL Server 2016. DBAs and developers should be more than familiar with the situation of a query behaving reliably for a long period, which suddenly changed into a slow-running, resource-killing monster query. Some readers may identify the cause of the issue being the phenomenon "parameter sniffing" or similarly "stale statistics". Either way, when troubleshooting why an unchanging query suddenly becomes slow, knowing the query execution plan(s) that SQL Server has created and used can be very helpful. A major issue when investigating these types of problems is the transient nature of query plans and their execution statistics. This is where Query Store comes into play; SQL Server collects and permanently stores statistics on query compilation and execution on a per database basis. This information is then persisted inside each database that has Query Store enabled, allowing a DBA or developer to investigate performance issues after the fact. It is even possible to perform query regression analysis, providing an insight into how query execution plans change over a longer timeframe. This sort of insight was previously only possible via hand-written solutions or third-party monitoring solutions, which may still not allow the same insights as the Query Store does.
Note
Further details on Query Store can be found in Chapter 9, Query Store.
Live Query Statistics
When we are developing inside SQL Server, each developer creates a mental model of how data flows inside SQL Server. Microsoft has provided a multitude of ways to display this concept when working with query execution. The most obvious visual aid is the graphical execution plan. There are endless explanations in books, articles and training seminars which attempt to make reading these graphical representations easier. Depending upon how your mind works, these descriptions can help or hinder your ability to understand the data flow concepts: fully blocking iterators, pipeline iterators, semi-blocking iterators, nested loop joins, the list goes on. When we look at an actual graphical execution plan, we are seeing a representation of how SQL Server processed a query: which data retrieval methods were used, which join types were chosen to join multiple data sets, what sorting was required, and so on. However, this is a representation after the query has completed execution. Live Query Statistics offers us the ability to observe during query execution and identify how, when, and where data moves through the query plan. This live representation is a huge improvement in making the concepts behind query execution clearer and is a great tool to allow developers to better design their query and indexing strategies to improve query performance.
Note
Further details for Live Query Statistics can be found in Chapter 3, SQL Server Tools.
Stretch Database
Microsoft has worked on their "Mobile First, Cloud First" strategy a lot in the past few years. We have seen a huge investment in Azure, their cloud offering, with the line between on-premises IT and cloud-based IT being continually blurred. The features being released in the newest products from Microsoft continue this approach and SQL Server is taking steps to bridge the divide between running SQL Server as a fully on-premises solution and storing/processing relational data in the cloud. One big step in achieving this approach is the new Stretch Database feature with SQL Server 2016. Stretch Database allows a DBA to categorize the data inside a database, defining which data is "hot" (frequently accessed data) and which is "cold" (infrequently accessed data). This categorization allows Stretch Database to then move the "cold" data out of the on-premises database and into Azure cloud storage. The segmentation of data remains transparent to any user/application that queries the data which now resides in two different locations. The idea behind this technology is to reduce storage requirements for the on-premises system by offloading large amounts of archive data onto cheaper, slower storage in the cloud. This reduction should then allow the smaller "hot" data to be placed on smaller capacity, higher performance storage. The benefit of Stretch Database is the fact that this separation of data requires no changes at the application or database query level. Stretch Database has been implemented to allow each company to also decide for themselves how data is defined as "hot" or "cold", providing maximum flexibility with minimal implementation overhead. This is a purely storage level change, which means the potential ROI of segmenting a database is quite large.
Note
Further details on Stretch Database can be found in Chapter 6, Stretch Database.
Database scoped configuration
Many DBAs who support multiple third-party applications running on SQL Server experience the difficulty of setting up their SQL Server instances according to the application requirements or best practices. Many third-party applications have prerequisites that dictate how the actual instance of SQL Server must be configured. A common occurrence is a requirement of configuring the "Max Degree of Parallelism" to force only one CPU to be used for query execution. As this is an instance-wide setting, this can affect all other databases/applications in a multi-tenant SQL Server instance (which is generally the case). With Database Scoped Configuration in SQL Server 2016 several previously instance level settings have been moved to a database level configuration option. This greatly improves multi-tenant SQL Server instances, as the decision, for example, how many CPUs can be used for query execution can be made at the database level, rather than for the entire instance. This allows DBAs to host databases with differing CPU usage requirements on the same instance, rather than having to either impact the entire instance with a setting or be forced to run multiple instances of SQL Server and possibly incur higher licensing costs.
Temporal Tables
There are many instances where DBAs or developers are required to implement a change tracking solution, allowing future analysis or assessment of data changes for certain business entities. A readily accessible example is the change history on a customer account in a CRM system. The options for implementing such a change tracking system are varied and each option has strengths and weaknesses. One such implementation that has been widely adopted is the use of triggers to capture data changes and store historical values in an archive table. Regardless of the implementation chosen, it was often cumbersome to develop and maintain these solutions. One of the challenges was incorporating table structure changes in the table being tracked. It was equally challenging creating solutions to allow for querying both the base table and the archive table belonging to it. The intelligence of deciding whether to query the live and/or archive data can require some complex query logic.
With the advent of Temporal Tables, this entire process has been simplified for both developers and DBAs. It is now possible to activate this "change tracking" on a table and push changes into an archive table with a simple change to a table's structure. Querying the base table and including a temporal attribute to the query is also a simple T-SQL syntax addition. As such, it is now possible for a developer to submit temporal analysis queries and SQL Server takes care of splitting the query between the live and archive data and returning the data in a single result set.
Note
Further details for Temporal Tables can be found in Chapter 7, Temporal Tables.
Columnstore indexes
Traditional data storage inside SQL Server has used the row-storage format, where the data for an entire row is stored together on the data pages inside the database. SQL Server 2012 introduced a new storage format: Columnstore. This format stores the data as columns rather than rows, combining the data from a single column and storing the data together on the data pages. This storage format provides the ability for massive compression of data, orders of magnitude better than traditional row-storage. Initially, only non-clustered columnstore indexes were possible. With SQL Server 2014 clustered columnstore indexes were introduced, expanding the usability of the feature greatly. Finally, with SQL Server 2016 updateable columnstore indexes and support for In-Memory columnstore indexes have been introduced. The potential performance improvements through these improvements are huge.
Note
Further details on Columnstore Indexes can be found in Chapter 10, Columnstore Indexes.
This concludes the section outlining the engine features implemented in SQL Server 2016. Through Microsoft's heavy move into cloud computing and their Azure offerings, they have increased the need to improve their internal systems for themselves. Microsoft is famous for their "dogfooding" approach to using their own software to run their own business, and Azure is arguably their largest foray into this area. The main improvements in the database engine have been fueled by the need to improve their own ability to continue offering Azure database solutions and provide features to allow databases of differing sizes and loads to be hosted together.
Programming
The programming landscape of SQL Server has continued to improve in order to adopt newer technologies over the years. SQL Server 2016 is no exception to this: there have been some long awaited general improvements and also some rather revolutionary additions to the product that change the way SQL Server may be used in future projects. This section will outline what programming improvements have been included in SQL Server 2016.
Transact SQL enhancements
The last major improvements in the T-SQL language allowed for better processing of running totals and other similar window functions. This was already a boon and allowed developers to replace arcane cursors with high performance T-SQL. These improvements are never enough for the most performance-conscious developers among us, and as such there were still voices requesting further incorporation of the ANSI SQL standards into the T-SQL implementation.
Notable additions to the T-SQL syntax include the ability to finally split comma separated strings via a single function call STRING_SPLIT() instead of the previous "hacky" implementations using loops, functions, XML conversions or even the CLR.
The sensible opposing syntax for splitting strings is a function to aggregate values together: STRING_AGG() returns a set of values in a comma separated string. This replaces similarly "hacky" solutions using the XML data type or one of a multitude of looping solutions. Each improvement in the T-SQL language further extends the toolbox that we as developers possess in order to manipulate data inside SQL Server.
Note
Further details on T-SQL Enhancements can be found in Chapter 4, Transact-SQL Enhancements.
JSON
It is quite common to meet developers outside the Microsoft stack who look down on products released from Redmond. Web developers in particular have been critical of the access to the latest data exchange structures, or rather lack of it. JSON has become the de facto data exchange method for the application development world. It is similar in structure to the previous "cool-kid" XML, but for reasons beyond the scope of this book, JSON has overtaken XML in general programming projects and is the expected payload for application and database communications. Microsoft has included JSON as a possible data exchange data type in SQL Server 2016 and provided a set of functions to accompany the data type.
Note
Further details on JSON can be found in Chapter 5, JSON Support in SQL Server.
In-Memory OLTP
In-Memory OLTP (codename Hekaton) was introduced in SQL Server 2014. The promise of ultra-high performance data processing inside SQL Server was a major feature when SQL Server 2014 was released. As expected with a newly implemented feature, there were a wide range of limitations in the initial release and this prevented many customers from being able to adopt the technology. With SQL Server 2016 a great number of these limitations have been either raised to a higher threshold or completely removed. In-Memory OLTP has received the required maturity and extension in its feature set to make it viable for prime production deployment. Chapter 11, Introducing SQL Server In-Memory OLTP, of this book will show an introduction to In-Memory OLTP, explaining how the technology works under the hood and how the initial release of the feature works in SQL Server 2014. Chapter 12, In-Memory OLTP Improvements in SQL Server 2016, will build on the introduction and explain how the feature has matured and improved with the release of SQL Server 2016.
Note
Further details on In-Memory OLTP can be found in Chapter 11, Introducing SQL Server In-Memory OLTP and Chapter 12, In-Memory OLTP Improvements in SQL Server 2016.
SQL Server tools
Accessing or managing data inside SQL Server and developing data solutions are two separate disciplines, each with their own specific focus on SQL Server. As such, Microsoft has created two different tools, each tailored towards the processes and facets of these disciplines.
SQL Server Management Studio (SSMS), as the name suggests, is the main management interface between DBAs/Developers and SQL Server. The studio was originally released with SQL Server 2005 as a replacement and consolidation of the old Query Analyzer and Enterprise Manager tools. As with any non-revenue generating software, SSMS received less attention over the years than the database engine, with limitations and missing tooling for many of the newer features in SQL Server. With SQL Server 2016 the focus inside Microsoft has been shifted and SSMS has been de-coupled from the release cycle of SQL Server itself. This decoupling allows both SSMS and SQL Server to be developed without having to wait for each other or for release windows. New releases of SSMS are created on top of more recent versions of Visual Studio and have seen almost monthly update releases since SQL Server 2016 was released to the market.
SQL Server Data Tools (SSDT) is also an application based on the Visual Studio framework. SSDT is focused on the application/data development discipline. SSDT is much more closely aligned with Visual Studio in its structure and the features offered. This focus includes the ability to create entire database projects and solution files, an easier integration into source control systems, the ability to connect projects into automated build processes, and generally offering a developer-centric development environment with a familiarity with Visual Studio. It is possible to design and create solutions in SSDT for SQL Server using the Relational Engine, Analysis Services, Integration Services, Reporting Services, and of course, for Azure SQL Database.
Note
Further details for SQL Server Tools can be found in Chapter 3, SQL Server Tools.
This concludes the overview of programming enhancements inside SQL Server 2016. The improvements outlined are all solid evolutionary steps in their respective areas. New features are very welcome and allow us to achieve more, while requiring less effort on our side. The In-Memory OLTP enhancements are especially positive, as they now expand on the groundwork laid down in the release of SQL Server 2014. Read the respective chapters to gain a deeper insight into how these enhancements can help you.
Business intelligence
Business intelligence is a huge area of IT and has been a cornerstone of the SQL Server product since at least SQL Server 2005. As the market and technologies in the Business intelligence space improve, so must SQL Server. The advent of cloud-based data analysis systems as well as the recent buzz around "big data" are driving forces for all data platform providers and Microsoft is no exception here. While there are many enhancements in the Business intelligence portion of SQL Server 2016, we will be concentrating on the feature that has a wider audience than just data analysts: The R language in SQL Server.
R in SQL Server
Data analytics has been the hottest topic in IT for the past few years, with new niches being crowned as the pinnacle of information science almost as fast as technology can progress. However, IT does have a few resolute classics that have stood the test of time and are still in wide use. SQL (in its many permutations) is a language we are well aware of in the SQL Server world. Another such language is the succinctly titled R. The R language is a data mining, machine learning and statistical analysis language that has existed since 1993. Many professionals with titles such as data scientists, data analyst, or statistician have been using the R language and tools that belong in that domain ever since. Microsoft has identified that, although they may want all the world's data inside SQL Server, this is just not feasible or sensible. External data sources and languages such as R exist and need to be accessible in an integrated manner.
For this to work, Microsoft made the decision to purchase Revolution Analytics (a commercial entity producing the forked Revolution R) in 2015, which made it possible for them to integrate the language and server process into SQL Server 2016. This integration allows a normal T-SQL developer to interact with the extremely powerful R service in a native manner and allow more advanced data analysis to be performed on their data.
Note
Further details on R in SQL Server can be found in Chapter 13, Supporting R in SQL Server and Chapter 14, Data Exploration and Predictive Modeling with R in SQL Server.
Release cycles
Microsoft has made a few major public-facing changes in the past five years. These changes include a departure from longer release cycles for their main products and a transition towards subscription-based services, for example, Office 365 and Azure services. The ideas surrounding continuous delivery and agile software development have also shaped the way that Microsoft has been delivering their flagship integrated development environment, Visual Studio, with new releases approximately every six months. This change in philosophy is now flowing into the development cycle of SQL Server. Due to the similarly constant release cycle of the cloud-version of SQL Server (Azure SQL Database), Microsoft wants to keep both the cloud and on-premises versions of the product as close to each other as possible. As such, it is not a surprise to see that the previous release cycle of every three to five years is being replaced by much shorter intervals. A clear example of this was that SQL Server 2016 released to the market in June of 2016, with a Community Technology Preview (CTP) of the next version of SQL Server being released in November of 2016. The wave of technology progress stops for no one. This is clearly true in the case of SQL Server!
Summary
In this introductory chapter, we have given you a brief outline of what lies ahead in this book. Each version of SQL Server has hundreds of improvements and enhancements, both through new features and through extensions of previous versions. The outlines for each chapter provide an insight into the main topics covered in this book and allow you to identify which areas you may like to dive in to and where to find them.
As we've already hinted, we need to get to work and learn about SQL Server 2016 before it's too late!
