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SQL Server 2016 novelties

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Lancement SQL Server 2016

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SQL Server 2016 novelties

  1. 1. SQL Server 2016 CTP3 New innovations
  2. 2. Operational analytics: in-memory tables
  3. 3. Support for index maintenance Operation SQL Server 2014 SQL Server 2016 Removing deleted rows Requires index REBUILD Index REORGANIZE Remove deleted rows from single compressed RG Merge one or more compressed RGs with deleted rows Done ONLINE Smaller RG size resulting from: Smaller BATCHSIZE Memory pressure Index build residual Index REBUILD Index REORGANIZE Ordering rows Create clustered index Create columnstore index by dropping clustered index No changes Query Row group granularity No support for RCSI or SI Recommendation: use read uncommitted Support of SI and RCSI (non-blocking) Insert Lock at row level (trickle insert) Row group level for set of rows No changes Delete Lock at row group level Row-level lock in conjunction with NCI Update Lock at row group level Implemented as Delete/Insert Row-level lock in conjunction with NCI AlwaysOn Failover Clustering (FCI) Fully supported Fully supported AlwaysON Availability Groups Fully supported except readable secondary Fully supported with readable secondary Index create/rebuild Offline Offline Performance
  4. 4. Summary: operational analytics Capability Ability to run analytics queries concurrently with operational workloads using the same schema Data Warehouse queries can be run on In-Memory OLTP workloads with no application changes Benefits Minimal impact on OLTP workloads Best performance and scalability available Offloading analytics workload to readable secondary . IIS Server BI analysts Performance
  5. 5. In-Memory OLTP enhancements
  6. 6. | ADD { <column_definition> | <table_constraint> | <table_index> } [ ,...n ] | DROP { [ CONSTRAINT ] { constraint_name } [ ,...n ] | COLUMN { column_name } [ ,...n ] | INDEX { index_name } [ ,...n ] } [ ,...n ] | ALTER INDEX index_name { REBUILD (WITH <rebuild_index_option>) } } The ALTER TABLE syntax is used for making changes to the table schema, as well as for adding, deleting, and rebuilding indexes Indexes are considered part of the table definition Key advantage is the ability to change the BUCKET_COUNT with an ALTER INDEX statement Altering memory-optimized tables Performance
  7. 7. CREATE PROCEDURE [dbo].[usp_1] WITH NATIVE_COMPILATION, SCHEMABINDING, EXECUTE AS OWNER AS BEGIN ATOMIC WITH ( TRANSACTION ISOLATION LEVEL = SNAPSHOT, LANGUAGE = N'us_english' ) SELECT c1, c2 from dbo.T1 END GO ALTER PROCEDURE [dbo].[usp_1] WITH NATIVE_COMPILATION, SCHEMABINDING, EXECUTE AS OWNER AS BEGIN ATOMIC WITH ( TRANSACTION ISOLATION LEVEL = SNAPSHOT, LANGUAGE = N'us_english' ) SELECT c1 from dbo.T1 END GO You can now perform ALTER operations on natively compiled stored procedures using the ALTER PROCEDURE statement Use sp_recompile to recompile stored procedures on the next execution Altering natively compiled stored procedures Performance
  8. 8. New Transaction Performance Analysis Overview report New report replaces the need to use the Management Data Warehouse to analyze which tables and stored procedures are candidates for in-memory optimization Performance
  9. 9. In SQL Server 2016, the storage for memory- optimized tables will be encrypted as part of enabling TDE on the database Simply follow the same steps as you would for a disk-based database Support for Transparent Data Encryption (TDE) Windows Operating System Level Data Protection SQL Server Instance Level User Database Level Database Encryption Key Service Master Key DPAPI encrypts the Service Master Key Master Database Level Database Encryption Key Service Master Key Encrypts the Database master Key for the master Database Database Master Key of the master Database creates a certificate in the master database The certificate encrypts the database Encryption Key in the user database The entire user database is secured by the Datbase Encryption Key (DEK) of the user database by using transparent database encryption Created at a time of SQL Server setup Statement: CREAT MASTER KEY… Statement: CREATE CERTIFICATE… Statement: CREATE DATABASE ENCRYPTION KEY… Statement: ALTER DATABSE… SET ENCRYPTION Performance
  10. 10. Summary: In-Memory OLTP enhancements Capability ALTER support for memory-optimized tables Greater Transact-SQL coverage Benefits Improved scaling: In-Memory OLTP engine has been enhanced to scale linearly on servers up to 4 sockets Tooling improvements in Management Studio MARS (multiple active result sets) support TDE (Transparent Data Encryption)-enabled: all on-disk data files are now encrypted once TDE is enabled Performance
  11. 11. Query Store Your flight data recorder for your database
  12. 12. Problems with query performance Fixing query plan choice regressions is difficult • Query plan cache is not well-suited for performance troubleshooting Long time to detect the issue (TTD) • Which query is slow? Why is it slow? • What was the previous plan? Long time to mitigate (TTM) • Can I modify the query? • How to use plan guide? Performance Temporary perf issues Website Is down DB upgraded Database is not working Impossible to predict / root cause Regression caused by new bits
  13. 13. Durability latency controlled by DB option DATA_FLUSH_INTERNAL_SECONDS Compile Execute Plan store Runtime stats Query Store schema Query data store Collects query texts (plus all relevant properties) Stores all plan choices and performance metrics Works across restarts / upgrades / recompiles Dramatically lowers the bar for performance troubleshooting New Views Intuitive and easy plan forcing Performance
  14. 14. The Query Store feature provides DBAs with insight on query plan choice and performance Monitoring performance by using the Query Store Performance
  15. 15. Summary: Query Store Capability Query Store helps customers quickly find and fix query performance issues Query Store is a ‘flight data recorder’ for database workloads Benefits Greatly simplifies query performance troubleshooting Provides performance stability across SQL Server upgrades Allows deeper insight into workload performance Performance
  16. 16. JavaScript Object Notation (JSON)
  17. 17. JSON in SQL Server [ { "Number":"SO43659", "Date":"2011-05-31T00:00:00" "AccountNumber":"AW29825", "Price":59.99, "Quantity":1 }, { "Number":"SO43661", "Date":"2011-06-01T00:00:00“ "AccountNumber":"AW73565“, "Price":24.99, "Quantity":3 } ] SO43659 2011-05-31T00:00:00 MSFT 59.99 1 SO43661 2011-06-01T00:00:00 Nokia 24.99 3 Table 2 JSON Formats result set as JSON text. JSON 2 table Migrates JSON text to table Built-in functions ISJSON JSON_VALUE JSON_MODIFY Performance
  18. 18. Main facts No custom type or index Store as NVARCHAR Does JSON work with X? Does NVARCHAR work with X? Yes, with in-memory, row-level security, stretch, compression, encryption, and more Yes, with all client drivers Different from DocumentDB
  19. 19. Table to JSON JSON output: SO43659 2011-05-31T00:00:00 MSFT 59.99 1 SO43661 2011-06-01T00:00:00 Nokia 24.99 3 SELECT Number AS [Order.Number], Date AS [Order.Date], Customer AS Account, Price AS 'Item.UnitPrice', Quantity AS 'Item.Qty' FROM SalesOrder FOR JSON PATH Performance
  20. 20. CREATE TABLE SalesOrderRecord ( Id int PRIMARY KEY IDENTITY, OrderNumber NVARCHAR(25) NOT NULL, OrderDate DATETIME NOT NULL, JSalesOrderDetails NVARCHAR(4000) CONSTRAINT SalesOrderDetails_IS_JSON CHECK ( ISJSON(JSalesOrderDetails)>0 ), Quantity AS CAST(JSON_VALUE(JSalesOrderDetails, '$.Order.Qty') AS int) ) GO CREATE INDEX idxJson ON SalesOrderRecord(Quantity) INCLUDE (Price); Built-in functions JSON is plain text ISJSON guarantees consistency Optimize further with computed column and INDEX
  21. 21. Summary: JSON Capability Greatly enhances developer productivity Benefits Added native JSON support in the core database engine supports schema-free data. Tackle more diverse data types right in SQL Server Support in DocumentDB Performance
  22. 22. Temporal Query back in time
  23. 23. No change in programming model New Insights INSERT / BULK INSERT UPDATE DELETE MERGE DML SELECT * FROM temporal Querying How to start with temporal CREATE temporal TABLE PERIOD FOR SYSTEM_TIME… ALTER regular_table TABLE ADD PERIOD… DDL FOR SYSTEM_TIME AS OF FROM..TO BETWEEN..AND CONTAINED IN Temporal Querying ANSI 2011 compliant Performance
  24. 24. Provides correct information about stored facts at any point in time, or between two points in time There are two orthogonal sets of scenarios with regards to temporal data: System (transaction)-time Application-time SELECT * FROM Person.BusinessEntityContact FOR SYSTEM_TIME BETWEEN @Start AND @End WHERE ContactTypeID = 17 Performance Temporal database support: BETWEEN
  25. 25. Summary: temporal Capability Greatly enhances developer productivity Benefits Added temporal database support so you can record, audit, and query data changes over time
  26. 26. Performance Security Availability Scalability Operational analytics Insights on operational data; Works with in-memory OLTP and disk-based OLTP In-memory OLTP enhancements Greater T-SQL surface area, terabytes of memory supported, and greater number of parallel CPUs Query data store Monitor and optimize query plans Native JSON Expanded support for JSON data Temporal database support Query data as points in time Always encrypted Sensitive data remains encrypted at all times with ability to query Row-level security Apply fine-grained access control to table rows Dynamic data masking Real-time obfuscation of data to prevent unauthorized access Other enhancements Audit success/failure of database operations TDE support for storage of In- Memory OLTP tables Enhanced auditing for OLTP with ability to track history of record changes Enhanced AlwaysOn Three synchronous replicas for auto failover across domains Round robin load balancing of replicas Automatic failover based on database health DTC for transactional integrity across database instances with AlwaysOn Support for SSIS with AlwaysOn Enhanced database caching Cache data with automatic, multiple TempDB files per instance in multi-core environments Mission-critical performance
  27. 27. Always Encrypted
  28. 28. Prevents data disclosure Client-side encryption of sensitive data using keys that are never given to the database system Queries on encrypted data Support for equality comparison, including join, group by, and distinct operators Application transparency Minimal application changes via server and client library enhancements Allows customers to securely store sensitive data outside of their trust boundary. Data remains protected from high-privileged, yet unauthorized users. The need for Always Encrypted Security
  29. 29. How it works Help protect data at rest and in motion, on-premises and in the cloud SQL Server or SQL Database ADO .NET Name Wayne Jefferson Name 0x19ca706fbd9a Result SetResult Set Client Name SSN Country 0x19ca706fbd9a 0x7ff654ae6d USA dbo.Customers ciphertext "SELECT Name FROM Customers WHERE SSN = @SSN", 0x7ff654ae6d ciphertext "SELECT Name FROM Customers WHERE SSN = @SSN", "111-22-3333" Encrypted sensitive data and corresponding keys are never seen in plaintext in SQL Server trust boundary Security
  30. 30. Select columns to be encrypted Analyze schema and application queries to detect conflicts (build time)Set up the keys: master & CEK Static schema analysis tool (SSDT only) UI for selecting columns (no automated data classification) Key setup tool to automate selecting CMK, generating and encrypting CEK, and uploading key metadata to the database Setup (SSMS or SSDT) User experience: SSMS or SSDT (Visual Studio) Security
  31. 31. Data remains encrypted during query Summary: Always Encrypted Protect data at rest and in motion, on-premises and in the cloud Capability ADO.Net client library provides transparent client-side encryption, while SQL Server executes T-SQL queries on encrypted data Benefits Apps TCE-enabled ADO .NET library SQL ServerEncrypted query Columnar key No app changes Master key Security
  32. 32. Row-level security SQL Server 2016 SQL Database
  33. 33. Fine-grained access control over specific rows in a database table Help prevent unauthorized access when multiple users share the same tables, or to implement connection filtering in multitenant applications Administer via SQL Server Management Studio or SQL Server Data Tools Enforcement logic inside the database and schema is bound to the table Protect data privacy by ensuring the right access across rows SQL Database Customer 1 Customer 2 Customer 3 The need for row-level security Security
  34. 34. Fine-grained access control Keeping multitenant databases secure by limiting access by other users who share the same tables Application transparency RLS works transparently at query time, no app changes needed Compatible with RLS in other leading products Centralized security logic Enforcement logic resides inside database and is schema-bound to the table it protects providing greater security. Reduced application maintenance and complexity Store data intended for many consumers in a single database/table while at the same time restricting row-level read and write access based on users’ execution context. Benefits of row-level security (RLS) Security
  35. 35. Security CREATE FUNCTION dbo.fn_securitypredicate(@wing int) RETURNS TABLE WITH SCHEMABINDING AS return SELECT 1 as [fn_securitypredicate_result] FROM StaffDuties d INNER JOIN Employees e ON (d.EmpId = e.EmpId) WHERE e.UserSID = SUSER_SID() AND @wing = d.Wing; CREATE SECURITY POLICY dbo.SecPol ADD FILTER PREDICATE dbo.fn_securitypredicate(Wing) ON Patients WITH (STATE = ON) Fine-grained access control over rows in a table based on one or more pre-defined filtering criteria, such as user’s role or clearance level in organization Concepts: Predicate function Security policy Example
  36. 36. Two App user (e.g., nurse) selects from Patients table Three Security Policy transparently rewrites query to apply filter predicate Database Policy Manager CREATE FUNCTION dbo.fn_securitypredicate(@wing int) RETURNS TABLE WITH SCHEMABINDING AS return SELECT 1 as [fn_securitypredicate_result] FROM StaffDuties d INNER JOIN Employees e ON (d.EmpId = e.EmpId) WHERE e.UserSID = SUSER_SID() AND @wing = d.Wing; CREATE SECURITY POLICY dbo.SecPol ADD FILTER PREDICATE dbo.fn_securitypredicate(Wing) ON Patients WITH (STATE = ON) Filter Predicate: INNER JOIN… Security Policy Application Patients Nurse SELECT * FROM Patients SELECT * FROM Patients SEMIJOIN APPLY dbo.fn_securitypredicate(patients.Wing); SELECT Patients.* FROM Patients, StaffDuties d INNER JOIN Employees e ON (d.EmpId = e.EmpId) WHERE e.UserSID = SUSER_SID() AND Patients.wing = d.Wing; RLS in three steps Security
  37. 37. Summary: RLS Capability Row-level security provides fine-grained access control over rows in a table based on conditions you set up Benefits Store data for many users in the same databases and tables while limiting access by other users who share the same tables Security
  38. 38. Dynamic data masking SQL Server 2016 SQL Database
  39. 39. Configuration made easy in the new Azure portal Policy-driven at the table and column level, for a defined set of users Data masking applied in real-time to query results based on policy Multiple masking functions available (e.g. full, partial) for various sensitive data categories (credit card numbers, SSN, etc.) SQL Database SQL Server 2016 Table.CreditCardNo 4465-6571-7868-5796 4468-7746-3848-1978 4484-5434-6858-6550 Real-time data masking; partial masking Dynamic data masking Prevent the abuse of sensitive data by hiding it from users Security
  40. 40. Regulatory compliance Sensitive data protection Agility and transparency Data is masked on the fly, with underlying data in the database remaining intact. Transparent to the application and applied according to user privilege Limit access to sensitive data by defining policies to obfuscate specific database fields, without affecting the integrity of the database. Benefits of dynamic data masking Security
  41. 41. How it works On-the-fly obfuscation of data in query results Policy-driven on the table and column Multiple masking functions available for various sensitive data categories Flexibility to define a set of privileged logins for un-masked data access By default, database owner is unmasked See: https://msdn.microsoft.com/en-us/library/mt130841.aspx Azure DB Table.CreditCardNo 4465-6571-7868-5796 4468-7746-3848-1978 4484-5434-6858-6550 On-the-fly masking of sensitive data in query results Dynamic masking Limit sensitive data exposure by obfuscating it to non-privileged users Security
  42. 42. Dynamic data masking walkthrough ALTER TABLE [Employee] ALTER COLUMN [SocialSecurityNumber] ADD MASKED WITH (FUNCTION = ‘SSN()’) ALTER TABLE [Employee] ALTER COLUMN [Email] ADD MASKED WITH (FUNCTION = ‘EMAIL()’) ALTER TABLE [Employee] ALTER COLUMN [Salary] ADD MASKED WITH (FUNCTION = ‘RANDOM(1,20000)’) GRANT UNMASK to admin1 1) Security officer defines dynamic data masking policy in T-SQL over sensitive data in Employee table2) Application user selects from Employee table3) Dynamic data masking policy obfuscates the sensitive data in the query results SELECT [Name], [SocialSecurityNumber], [Email], [Salary] FROM [Employee] Security
  43. 43. Summary: dynamic data masking Capability Protects against unauthorized disclosure of sensitive data in the application Benefits Enables you to set up policies at the table and column level that provide multiple masking functions Allows certain privileged logins to see the data unmasked Security
  44. 44. Enhanced AlwaysOn
  45. 45. More than two automatic failover targets Increased solution scale Increased resiliency Now any sync secondary can be a target for automatic failover Total of three automatic failover targets Availability
  46. 46. Log transport performance New hardware pushes boundaries Very high transaction rates have caused problems when coupled with fast hardware (i.e. PCIe flash storage) The entire pipeline has been reworked end to end Result is much lower CPU consumption, and much better performance Performance bar: comparison to the performance of a standalone server Availability
  47. 47. Distributed Transaction Coordinator (DTC) support Currently, any distributed transactions touching a database in an availability group are not allowed Many customers run unsupported, at risk to their data and reputation Many enterprise applications need cross-database transactions Fully supported in SQL Server 2016 Joint effort with Windows Requires specific patch in order to work cleanly Other requirements: • Availability groups must be running on Windows Server 2016 Technical Preview 2 • Availability groups must be created with the CREATE AVAILABILITY GROUP command and the WITH DTC_SUPPORT = PER_DB clause. You cannot currently alter an existing availability group • Learn more: https://msdn.microsoft.com/en-us/library/ms366279.aspx Availability
  48. 48. Database-level failover trigger Currently, Availability Groups only monitor the health of the instance A database can be offline or corrupt, but will not trigger a failover as long as the instance itself is healthy SQL Server 2016: option to also monitor the health of the databases in the Availability Group Databases going offline trigger a change in the health status Availability
  49. 49. Summary: enhanced AlwaysOn Capability For scalability, SQL Server 2016 adds in load balancing of readable secondaries Increases the number of auto-failover targets from two to three Benefits Log transport performance has been improved Support for Distributed Transaction Coordinator (DTC) — enrolled transactions for Availability Group databases Database-level health monitoring gMSA: domain-level accounts that are automatically managed Availability
  50. 50. Performance Security Availability Scalability Operational analytics Insights on operational data; Works with in-memory OLTP and disk-based OLTP In-memory OLTP enhancements Greater T-SQL surface area, terabytes of memory supported, and greater number of parallel CPUs Query data store Monitor and optimize query plans Native JSON Expanded support for JSON data Temporal database support query data as points in time Always encrypted Sensitive data remains encrypted at all times with ability to query Row-level security Apply fine-grained access control to table rows Dynamic Data Masking Real-time obfuscation of data to prevent unauthorized access Other enhancements Audit success/failure of database operations TDE support for storage of in- memory OLTP tables Enhanced auditing for OLTP with ability to track history of record changes Enhanced AlwaysOn Three synchronous replicas for auto failover across domains Round robin load balancing of replicas Automatic failover based on database health DTC for transactional integrity across database instances with AlwaysOn Support for SSIS with AlwaysOn Enhanced database caching Cache data with automatic, multiple TempDB files per instance in multicore environments Mission-critical performance
  51. 51. Scalability improvements
  52. 52. Access any data Scale and manage Powerful insights Advanced analytics PolyBase Insights from data across SQL Server and Hadoop with the simplicity of T-SQL Enhanced SSIS Designer support for previous SSIS versions Enterprise-grade Analysis Services Enhanced performance and scalability for Analysis Services Single SSDT in Visual Studio 2015 (CTP3) Build richer analytics solutions as part of your development projects in Visual Studio Enhanced MDS Excel add-in 15x faster; more granular security roles; archival options for transaction logs; and reuse entities across models Mobile BI Business insights for your on- premises data through rich visualization on mobile devices with native apps for Windows, iOS, and Android Enhanced Reporting Services New modern reports with rich visualizations R integration (CTP3) Bringing predictive analytic capabilities to your relational database Expand your “R” script library with Microsoft Azure Marketplace Deeper insights across data
  53. 53. PolyBase for SQL Server 2016
  54. 54. PolyBase and queries Provides a scalable, T-SQL-compatible query processing framework for combining data from both universes Access any data
  55. 55. PolyBase View in SQL Server 2016 PolyBase View • Execute T-SQL queries against relational data in SQL Server and ‘semi-structured’ data in HDFS and/or Azure • Leverage existing T-SQL skills and BI tools to gain insights from different data stores • Expand the reach of SQL Server to Hadoop(HDFS) Access any data
  56. 56. SSIS improvements
  57. 57. SSIS improvements for SQL Server 2016 AlwaysOn support Incremental deployment of packages Improved project upgrade support CTP3 enhancements Error column name support Custom log level Package template OData V4 support Designer improvements One designer multi-version support AlwaysOn Availability Groups Secondary for SSISDB New York (Primary) New Jersey (Secondary) SSIS DB SSIS DB SQL Server 2012 SSIS Project X SQL Server 2016 SSIS Project X Improved project upgrade Access any data
  58. 58. Package template (CTP3) Reuse code as templates Developer can save part of the package as a template and reuse it in the design of other packages Access any data
  59. 59. Capability DBA can set up AlwaysOn Availability Groups for the SSIS catalog Deploy one or more packages to an existing or new project without deploying the whole project Benefits Customize your own log levels for more flexibility See the error column name in both the data viewer and editor Create reusable code by making a package template OData Source support for V4 Protocol Summary: SSIS improvement Access any data
  60. 60. Enterprise-grade Analysis Services
  61. 61. Analysis Services themes for SQL Server 2016 Improved productivity and performance Increased productivity Scale and manage
  62. 62. Scale your tabular models with support for high-end servers More memory More cores Summary: enterprise-grade Analysis Services Deliver high performance and scalability for your BI solutions High-end server hardware Capability Tabular and MOLAP modeling enhancements High-performance Direct Query for Tabular Additional tools for administrators Performance and data source updates Benefits Scale and manage
  63. 63. Unified Visual Studio Data Tools (CTP3)
  64. 64. Capability SSDT-BI and SSMS for Visual Studio 2015 Rich data modeling enhancements New DAX analytical functions MDX Query Plan tool for performance optimizations and troubleshooting Benefits{JSON} Rich BI application platform Develop and deliver BI solutions faster and at lower costs Scale and manage
  65. 65. Master Data Services improvements
  66. 66. Areas of improvement Scale and manage
  67. 67. Performance and scale Overall performance and scale improvements Target of 15x performance improvement for Excel add-in Increased performance for bulk entity-based staging operations Security improvements New security roles for more granular permissions around read, write, and delete Multiple system administrators Manageability and modeling Archive transaction logs Reuse of entities across models Support for compound keys Name and Code attributes can be renamed Summary: MDS improvements Scale and manage
  68. 68. Enhanced Reporting Services
  69. 69. Modern Report Builder look and feel Visual Studio 2015 support Modern reports Powerful insights
  70. 70. Consume anywhere, anytime Find, view, and manage through modern web portal Support for modern HTML5 browsers Printing without plug-ins PowerPoint rendering and export Powerful insights
  71. 71. Summary: Enhanced Reporting Services Modern design tools Support for latest enterprise data source Expanded browser support Secure access Power BI integration  Report consumption from modern browsers  Improved parameters  Modern themes  New chart types Powerful insights
  72. 72. R integration
  73. 73. R Integration in SQL Server will be Revolutionary
  74. 74. Capability Extensible in-database analytics, integrated with R, exposed through T-SQL Centralize enterprise library for analytic models Benefits SQL Server Analytical engines Full R integration Fully extensible Data Management Layer Relational data T-SQL interface Stream data in-memory Analytics library Share and collaborate Manage and deploy R + Data Scientists Business Analysts Publish algorithms, interact directly with data Analysis through TSQL, tools, and vetted algorithms DBAs Manage storage and analytics together Summary: R integration and advanced analytics Advanced analytics
  75. 75. Stretch Database
  76. 76. Ever-growing data, ever-shrinking IT What to do? Expand server and storage Move data elsewhere Delete Massive tables (hundreds of millions/billions of rows, TBs size) Users want/need to retain data indefinitely Cold data infrequently accessed but must be online Datacenter consolidation Maintenance challenges Business SLAs at risk Hybrid solutions
  77. 77. Capability Stretch large operational tables from on-premises to Azure with the ability to query Benefits Stretch SQL Server into Azure Securely stretch cold tables to Azure with remote query processing SQL SERVER 2016 Azure Hybrid solutions
  78. 78. Stretch Database architecture How it works Creates a secure linked server definition in the on-premises SQL Server Linked server definition has the remote endpoint as the target Provisions remote resources and begins to migrate eligible data, if migration is enabled Queries against tables run against both the local database and the remote endpoint Remote endpoint Remote data Azure InternetBoundary Local database Local data Eligible data Linked Servers Hybrid solutions
  79. 79. Queries continue working • Business applications continue working without disruption • DBA scripts and tools work as before. All controls still held in local SQL Server • Developers continue building or enhancing applications with existing tools and methods Hybrid solutions
  80. 80. Order history Name SSN Date Jane Doe cm61ba906fd 2/28/2005 Jim Gray ox7ff654ae6d 3/18/2005 John Smith i2y36cg776rg 4/10/2005 Bill Brown nx290pldo90l 4/27/2005 Sue Daniels ypo85ba616rj 5/12/2005 Sarah Jones bns51ra806fd 5/22/2005 Jake Marks mci12hh906fj 6/07/2005 Eric Mears utb76b916gi 6/18/2014 Rachel Hogan px61hi9306fj 7/1/2014 Sam Johnson ol43bi506gd 7/12/2014 David Simon tx83hal916fi 7/29/2014 Order history Name SSN Date Jane Doe cm61ba906fd 2/28/2005 Jim Gray ox7ff654ae6d 3/18/2005 John Smith i2y36cg776rg 4/10/2005 Bill Brown nx290pldo90l 4/27/2005 Customer data Product data Order History Stretch to cloud App Query Microsoft Azure  Jim Gray ox7ff654ae6d 3/18/2005 Hybrid solutions
  81. 81. What is IoT?
  82. 82. Internet of Things (IoT)
  83. 83. Azure Data Lake and Data Factory
  84. 84. Get started with U-SQL
  85. 85. Questions

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