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What’s new in SQL Server 2017

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What’s new in SQL Server 2017

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Covers all the new features in SQL Server 2017, as well as details on upgrading and migrating to SQL Server 2017 or to Azure SQL Database.

Covers all the new features in SQL Server 2017, as well as details on upgrading and migrating to SQL Server 2017 or to Azure SQL Database.

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What’s new in SQL Server 2017

  1. 1. What’s new in SQL Server 2017 James Serra Big Data Evangelist Microsoft JamesSerra3@gmail.com
  2. 2. About Me  Microsoft, Big Data Evangelist  In IT for 30 years, worked on many BI and DW projects  Worked as desktop/web/database developer, DBA, BI and DW architect and developer, MDM architect, PDW/APS developer  Been perm employee, contractor, consultant, business owner  Presenter at PASS Business Analytics Conference, PASS Summit, Enterprise Data World conference  Certifications: MCSE: Data Platform, Business Intelligence; MS: Architecting Microsoft Azure Solutions, Design and Implement Big Data Analytics Solutions, Design and Implement Cloud Data Platform Solutions  Blog at JamesSerra.com  Former SQL Server MVP  Author of book “Reporting with Microsoft SQL Server 2012”
  3. 3. The power of SQL Server Microsoft Tableau Oracle $120 $480 $2,230 Self-service BI per userat massive scale 0 1 4 0 0 3 0 34 29 22 15 5 22 16 6 43 20 69 18 49 74 3 0 10 20 30 40 50 60 70 80 1 2 3 4 5 6 7 NIST comprehensive vulnerability database, June 2016 SQL Server Oracle MySQL2 SAP HANA 1M Predictions per second Any platform Any data 1010 0101 0010 {} Any language T-SQL Java PHP Node.js C/C++ C#/VB.NET Python Ruby #1 TPC-H non-clustered results, August 2017 #1 in 30TB, 10TB, 1TB TPC-H Windows Server 2016
  4. 4. SQL Server 2017 Meeting you where you are It’s the same SQL Server database engine with many features and services available for all your applications—regardless of your operational ecosystem Linux Any data Any application Anywhere Choice of platform T-SQL Java C/C++ C#/VB.NET PHP Node.js Python Ruby 1010 0101 0010 { }
  5. 5. How we develop SQL Cloud-first but not cloud-only Use SQL Database to improve core SQL Server features and cadence Many interesting and compelling on-premises  cloud scenarios SQL Server and APS Azure SQL Virtual Machines Azure SQL Database Azure SQL Data Warehouse
  6. 6. A consistent experience from SQL Server on-premises to Microsoft Azure IaaS and PaaS On-premises, private cloud, and public cloud SQL Server local (Windows and Linux), VMs (Windows and Linux), containers, and SQL Database Common development, management, and identity tools including Active Directory, Visual Studio, Hyper-V, and System Center Scalability, availability, security, identity, backup and restore, and replication Many data sources Reporting, integration, processing, and analytics All supported in the hybrid cloud Consistency and integration
  7. 7. SQL Server 2017 New Features
  8. 8. Database Engine new features Linux/Docker support • RHEL, Ubuntu, SLES, and Docker containers Adaptive Query Processing • Faster queries just by upgrading • Interleaved execution • Batch-mode memory grant feedback • Batch-mode adaptive joins
  9. 9. Database Engine new features Graph • Store relationships using nodes/edges • Analyze interconnected data using node/edge query syntax SELECT r.name FROM Person AS p, likes AS l1, Person AS p2, likes AS l2, Restaurant AS r WHERE MATCH(p-(l1)->p2-(l2)->r) AND p.name = 'Chris' Automatic Tuning • Automatic plan correction – identify, and optionally fix, problematic query execution plans causing query performance problems • Automatic index management – make index recommendations (Azure SQL Database only)
  10. 10. Database Engine new features Enhanced performance for natively complied T-SQL modules • OPENJSON, FOR JSON, JSON • CROSS APPLY operations • Computed columns New string functions • TRIM, CONCAT_WS, TRANSLATE, and STRING_AGG with support for WITHIN GROUP (ORDER BY) BULK IMPORT now supports CSV format and Azure Blob Storage as file source
  11. 11. Database Engine new features Native scoring with T-SQL PREDICT Resumable online index rebuild • Pause/resume online index rebuilds Clusterless read-scale availability groups • Unlimited, geo-distributed, linear read scaling P S1 S2 S3 S4
  12. 12. Integration Services new features Integration Services Scale Out • Distribute SSIS package execution more easily across multiple workers, and manage executions and workers from a single master computer Integration Services on Linux • TRIM, CONCAT_WS, TRANSLATE, and STRING_AGG with support for WITHIN GROUP (ORDER BY) Connectivity improvements • Connect to the OData feeds of Microsoft Dynamics AX Online and Microsoft Dynamics CRM Online with the updated OData components
  13. 13. Analysis Services new features Object level security for tabular models Get Data enhancements • New data sources • Modern experience for tabular models Enhanced ragged hierarchy support • New Hide Members property to hide blank members in ragged hierarchies
  14. 14. Reporting Services new features Comments • Comments are now available for reports, to add perspective and collaborate with others. You can also include attachments with comments Broader DAX support • Report Builder and SQL Server Data Tools, you can create native DAX queries against supported tabular data models by dragging and dropping desired fields in the query designers. Standalone installer • SSRS is no longer distributed through SQL Server setup
  15. 15. Machine Learning Services new features Python support • Python and R scripts are now supported • revoscalepy - Pythonic equivalent of RevoScaleR – parallel algorithms for data processing with a rich API MicrosoftML • Package of machine learning algorithms and transforms (with Python bindings), as well as pre-trained models for image extraction or sentiment analysis
  16. 16. SQL Server 2017 on Linux
  17. 17. Power of the SQL Server Database Engine on the platform of your choice Linux distributions: RedHat Enterprise Linux (RHEL), Ubuntu, and SUSE Linux Enterprise Server (SLES) Docker: Windows & Linux containers Windows Server / Windows 10 Linux Linux/Windows container Windows
  18. 18. Linux-native user experience
  19. 19. Supported platforms Platform Supported version(s) Supported file system(s) Red Hat Enterprise Linux 7.3 XFS or EXT4 SUSE Linux Enterprise Server v12 SP2 EXT4 Ubuntu 16.04 EXT4 Docker Engine (on Windows, Mac, or Linux) 1.8+ N/A System requirements for SQL Server on Linux
  20. 20. Cross-System Architecture SQL Platform Abstraction Layer (SQLPAL) RDBMS AS IS RS Windows Linux Windows Host Ext. Linux Host Extension SQL Platform Abstraction Layer (SQLPAL) Host Extension mapping to OS system calls (IO, Memory, CPU scheduling) Win32-like APIsSQL OS API SQL OS v2 Everything else System Resource & Latency Sensitive Code Paths
  21. 21. Tools and programmability • Windows-based SQL Server tools - like SSMS, SSDT, Profiler – work when connected to SQL Server on Linux • All existing drivers and frameworks supported • Third-party tools continue to work • Native command-line tools – sqlcmd, bcp • Visual Studio Code mssql extension
  22. 22. Client Connectivity Language Platform More Details C# Windows, Linux, macOS Microsoft ADO.NET for SQL Server Java Windows, Linux, macOS Microsoft JDBC Driver for SQL Server PHP Windows, Linux, macOS PHP SQL Driver for SQL Server Node.js Windows, Linux, macOS Node.js Driver for SQL Server Python Windows, Linux, macOS Python SQL Driver Ruby Windows, Linux, macOS Ruby Driver for SQL Server C++ Windows, Linux, macOS Microsoft ODBC Driver for SQL Server SQL Server client drivers are available for many programming languages, including:
  23. 23. What’s available on Linux? Operations Features • Support for RHEL, Ubuntu, SLES, Docker • Package-based installs • Support for Open Shift, Docker Swarm • Failover Clustering via Pacemaker • Backup / Restore • SSMS on Windows connected to Linux • Command line tools: sqlcmd, bcp • Transparent Data Encryption • Backup Encryption • SCOM Management Pack • DMVs • Table partitioning • SQL Server Agent • Full-Text search • Integration Services • Active Directory (integrated) authentication • TLS for encrypted connections
  24. 24. What’s available on Linux? Programming Features • All major language driver compatibility • In-Memory OLTP • Columnstore indexes • Query Store • Compression • Always Encrypted • Row-Level Security, Data Masking • Auditing • Service Broker • CLR • JSON, XML • Third-party tools
  25. 25. Features not currently supported on Linux
  26. 26. In-Memory OLTP
  27. 27. In-Memory Online Transaction Processing (OLTP) In-Memory OLTP is the premier technology available in SQL Server and Azure SQL Database for optimizing performance of transaction processing, data ingestion, data load, and transient data scenarios Memory-optimized tables outperform traditional disk-based tables, leading to more responsive transactional applications Memory-optimized tables also improve throughput and reduce latency for transaction processing, and can help improve performance of transient data scenarios such as temp tables and ETL
  28. 28. In-Memory OLTP enhancements (SQL Server 2017)  sp_spaceused is now supported for memory-optimized tables.  sp_rename is now supported for memory-optimized tables and natively compiled T-SQL modules.  CASE statements are now supported for natively compiled T-SQL modules.  The limitation of 8 indexes on memory-optimized tables has been eliminated.  TOP (N) WITH TIES is now supported in natively compiled T-SQL modules.  ALTER TABLE against memory-optimized tables is now substantially faster in most cases.  Transaction log redo of memory-optimized tables is now done in parallel. This bolsters faster recovery times and significantly increases the sustained throughput of AlwaysOn availability group configuration.  Memory-optimized filegroup files can now be stored on Azure Storage. Backup/Restore of memory-optimized files on Azure Storage is supported.  Support for computed columns in memory-optimized tables, including indexes on computed columns.  Full support for JSON functions in natively compiled modules, and in check constraints.  CROSS APPLY operator in natively compiled modules.  Performance of btree (non-clustered) index rebuild for MEMORY_OPTIMIZED tables during database recovery has been significantly optimized. This improvement substantially reduces the database recovery time when non-clustered indexes are used.
  29. 29. High Availability
  30. 30. Availability Groups + Failover Clustering (Linux) Always On: Failover Cluster Instances and Availability Groups work together to ensure data is accessible despite failures Pacemaker Cluster Network Subnet Network Subnet Storage Node NodeNodeNodeNode SQL Server Instance SQL Server Instance SQL Server Instance Always On SQL Server Failover Cluster Instance Secondary Replica Secondary Replica Secondary Replica Primary Replica Always On Availability Group Instance Network Name Pacemaker Configuration Pacemaker Configuration Pacemaker Configuration Pacemaker Configuration Pacemaker Configuration Instance Network Name Instance Network Name Instance Network Name Pacemaker cluster virtual IP Storage Storage Shared Storage DNS name (manual registration)
  31. 31. Always On cross-platform capabilities Mission critical availability on any platform • Always On availability groups for LinuxNEW* and Windows for HA and DR • Flexibility for HA architecturesNEW* • Ultimate HA with OS-level redundancy and failover • Load balancing of readable secondaries • High Availability • Offload Backups • Scale BI Reporting • Enables Testing • Enables Migrations
  32. 32. Guarantee commits on synchronous secondary replicas Use REQUIRED_COPIES_TO_COMMIT with CREATE AVAILABILITY GROUP or ALTER AVAILABILITY GROUP. When REQUIRED_COPIES_TO_COMMIT is set to a value higher than 0, transactions at the primary replica databases will wait until the transaction is committed on the specified number of synchronous secondary replica database transaction logs. If enough synchronous secondary replicas are not online, write transactions to primary replica will stop until communication with sufficient secondary replicas resume. Enhanced AlwaysOn Availability Groups (SQL Server 2017) AG_Listener New York (Primary) Asynchronous data Movement Synchronous data Movement Unified HA solution Hong Kong (Secondary) New Jersey (Secondary)
  33. 33. CLUSTER_TYPE CLUSTER_TYPE Use with CREATE AVAILABILITY GROUP. Identifies the type of server cluster manager that manages an availability group. Can be one of the following types: WSFC - Windows server failover cluster. On Windows, it is the default value for CLUSTER_TYPE. EXTERNAL - A cluster manager that is not Windows server failover cluster - for example, on Linux with Pacemaker. NONE - No cluster manager. Used for a read- scale availability group. Enhanced AlwaysOn Availability Groups (SQL Server 2017) AG_Listener New York (Primary) Asynchronous data Movement Synchronous data Movement Unified HA solution Hong Kong (Secondary) New Jersey (Secondary)
  34. 34. Automatic Tuning
  35. 35. Automatic Tuning Automatic plan correction identifies problematic plans and fixes SQL plan performance problems. Adapt Verify Learn
  36. 36. Automatic Plan Choice Detection sys.dm_db_tuning_recommendations
  37. 37. Automatic Plan Correction sys.dm_db_tuning_recommendations by enabling the AUTOMATIC_TUNING database property ALTER DATABASE current SET AUTOMATIC_TUNING ( FORCE_LAST_GOOD_PLAN = ON );
  38. 38. Adaptive Query Processing (AQP)
  39. 39. Adaptive Query Processing Three features to improve query performance Enabled when the database is in SQL Server 2017 compatibility mode (140) ALTER DATABASE current SET COMPATIBILITY_LEVEL = 140; Adaptive Query Processing Interleaved Execution Batch Mode Memory Grant Feedback Batch Mode Adaptive Joins
  40. 40. Query Processing and Cardinality Estimation When estimates are accurate (enough), we make informed decisions around order of operations and physical algorithm selection CE uses a combination of statistical techniques and assumptions During optimization, the cardinality estimation (CE) process is responsible for estimating the number of rows processed at each step in an execution plan
  41. 41. Common reasons for incorrect cardinality estimates Missing statistics Stale statistics Inadequate statistics sample rate Bad parameter sniffing scenarios Out-of-model query constructs • E.g. MSTVFs, table variables, XQuery Assumptions not aligned with data being queried • E.g. independence vs. correlation
  42. 42. Cost of incorrect estimates Slow query response time due to inefficient plans Excessive resource utilization (CPU, Memory, IO) Spills to disk Reduced throughput and concurrency T-SQL refactoring to work around off- model statements
  43. 43. Interleaved Execution Pre 2017 2017+ 100 rows guessed for MSTVFs MSTVF identified 500k rows assumed Performance issues if skewed Execute MSTVF Good performance Problem: Multi-statement table valued functions (MSTVFs) are treated as a black box by QP and we use a fixed optimization guess Interleaved Execution will materialize row counts for multi-statement table valued functions (MSTVFs) Downstream operations will benefit from the corrected MSTVF cardinality estimate
  44. 44. Batch Mode Memory Grant Feedback Problem: Queries can spill to disk or take too much memory based on poor cardinality estimates Memory Grant Feedback (MGF) will adjust memory grants based on execution feedback MGF will remove spills and improve concurrency for repeating queries
  45. 45. Batch Mode Adaptive Joins Problem: If cardinality estimates are skewed, we may choose an inappropriate join algorithm Batch Mode Adaptive Joins (AJ) will defer the choice of hash join or nested loop until after the first join input has been scanned AJ uses nested loop for small inputs, hash joins for large inputs Build input Adaptive threshold Hash join Nested loop Yes No
  46. 46. Support for Graph data in SQL Server 2017
  47. 47. A graph is collection of Nodes and Edges Undirected Graph Directed Graph Weighted Graph Property Graph What is a Graph? Node Person Person
  48. 48. Typical Scenarios for Graph Databases Hierarchical or interconnected data, entities with multiple parents. Analyze interconnected data, materialize new information from existing facts. Identify non-obvious connections Complex many-to-many relationships. Organically grow connections as the business evolves. A
  49. 49. Introducing SQL Server Graph A collection of node and edge tables in the database Language Extensions DDL Extensions – create node and edge tables DML Extensions – SELECT - T-SQL MATCH clause to support pattern matching and traversals; DELETE, UPDATE, and INSERT support graph tables Graph support is integrated into the SQL Server ecosystem Database Contains Graph isCollectionOf Node table has Properties Edge table may or may not have Properties Node Table(s) Edges connect Nodes Edge Table(s)
  50. 50. DDL Extensions • Create node and edge tables • Properties associated with nodes and edges CREATE TABLE Product (ID INTEGER PRIMARY KEY, name VARCHAR(100)) AS NODE; CREATE TABLE Supplier (ID INTEGER PRIMARY KEY, name VARCHAR(100)) AS NODE; CREATE TABLE hasInventory AS EDGE; CREATE TABLE located_at(address varchar(100)) AS EDGE;
  51. 51. DML Extensions Multi-hop navigation and join-free pattern matching using the MATCH predicate: SELECT Prod.name as ProductName, Sup.name as SupplierName FROM Product Prod, Supplier Sup, hasInventory hasIn, located_at supp_loc, Customer Cus, located_at cust_loc, orders, location loc WHERE MATCH( cus-(orders)->Prod<-(hasIn)-Sup AND cus-(cust_loc)->location<-(supp_loc)-Sup ) ;
  52. 52. Transact-SQL Extensions in SQL Server 2017
  53. 53. T-SQL TRIM TRIM ( [ characters FROM ] string ) Removes the space character (char(32)) or other specified characters from the start or end of a string. SELECT TRIM (' test ') AS Result ; Result ----------- test Removing the space character from both sides of string (equivalent to LTRIM(RTRIM(string))) SELECT TRIM( '.,! ' FROM '# test .') AS Result; Result --------------- # test Removes specified characters from both sides of string (Trimming multiple characters)
  54. 54. T-SQL CONCAT_WS CONCAT_WS ( separator, argument1, argument2 [, argumentN]…) Concatenates a variable number of arguments with a delimiter specified in the first argument. SELECT CONCAT_WS( ' - ','one','two','three','four') AS Result ; Result ------------------------ one - two - three - four Concatenating with a delimiter SELECT CONCAT_WS( ' - ','one',NULL,'two',NULL,'three',NULL,'four') AS Result ; Result --------------------------------- one - two - three - four Concatenation ignores NULL
  55. 55. T-SQL TRANSLATE TRANSLATE ( inputString, characters, translations) Returns the string provided as a first argument after some characters specified in the second argument are translated into a destination set of characters. SELECT TRANSLATE('2*[3+4]/{7-2}', '[]{}', '()()') AS Result ; Result ------------- 2*(3+4)/(7-2) Replace square and curly braces with regular braces SELECT TRANSLATE('[137.4, 72.3]' , '[,]', '( )') AS Point, TRANSLATE('(137.4 72.3)' , '( )', '[,]') AS Coordinates ; Point Coordinates ------------- ------------- (137.4 72.3) [137.4,72.3] Convert GeoJSON points into WKT
  56. 56. T-SQL STRING_AGG STRING_AGG ( expression, separator ) [ <order_clause> ] <order_clause> ::= WITHIN GROUP ( ORDER BY <order_by_expression_list> [ ASC | DESC ] ) Concatenates the values of string expressions and places separator values between them. The separator is not added at the end of string. SELECT STRING_AGG ( ISNULL(FirstName,'N/A'), ',') AS csv FROM Person.Person ; csv ----------------------------------------------------------------------------------------------------------------------------- Syed,Catherine,Kim,Kim,Kim,Hazem,Sam,Humberto,Gustavo,Pilar,Pilar,Aaron,Adam,Alex,Alexandra,Allison,Amanda,Amber,Andrea,Angel Generate list of names separated with comma (without NULL values) SELECT town, STRING_AGG (email, ';') WITHIN GROUP (ORDER BY email ASC) AS emails FROM dbo.Employee GROUP BY town ; town emails ------- --------------------------------------------------------------------------------- Seattle catherine0@adventure-works.com;kim2@adventure-works.com;syed0@adventure-works.com LA hazem0@adventure-works.com;sam1@adventure-works.com Generate a sorted list of emails per towns
  57. 57. T-SQL BULK INSERT / OPENROWSET(BULK…) [ [ , ] FORMAT = 'CSV' ] [ [ , ] FIELDQUOTE = 'quote_characters'] Additional options added that provide support for CSV format data files Data files and format files can now be loaded from Azure Blob Storage
  58. 58. Columnstore
  59. 59. Data stored as columns SQL Server performance features: Columnstore Columnstore A technology for storing, retrieving, and managing data by using a columnar data format called a columnstore. You can use columnstore indexes for real-time analytics on your operational workload. Key benefits Provides a very high level of data compression, typically 10x, to reduce your data warehouse storage cost significantly. Indexing on a column with repeated values vastly improves performance for analytics. Improved performance: More data fits in memory Batch-mode execution
  60. 60. ColumnStore Index Enhancements (SQL Server 2017)  Clustered Columnstore Indexes now support LOB columns (nvarchar(max), varchar(max), varbinary(max)).  Online non-clustered columnstore index build and rebuild support added
  61. 61. Resumable Online Indexing
  62. 62. Resumable Online Indexing With Resumable Online Index Rebuild you can resume a paused index rebuild operation from where the rebuild operation was paused rather than having to restart the operation at the beginning. In addition, this feature rebuilds indexes using only a small amount of log space. • Resume an index rebuild operation after an index rebuild failure, such as after a database failover or after running out of disk space. There is no need to restart the operation from the beginning. This can save a significant amount of time when rebuilding indexes for large tables. • Pause an ongoing index rebuild operation and resume it later – for example, to temporarily free up system resources to execute a high priority. Instead of aborting the index rebuild process, you can pause the index rebuild operation and resume it later without losing prior progress. • Rebuild large indexes without using a lot of log space and have a long-running transaction that blocks other maintenance activities. This helps log truncation and avoid out-of-log errors that are possible for long-running index rebuild operations.
  63. 63. Using Resumable Online Index Rebuild Start a resumable online index rebuild ALTER INDEX test_idx on test_table REBUILD WITH (ONLINE=ON, RESUMABLE=ON) ; Pause a resumable online index rebuild ALTER INDEX test_idx on test_table PAUSE ; Resume a paused online index rebuild ALTER INDEX test_idx on test_table RESUME ; Abort a resumable online index rebuild (which is running or paused) ALTER INDEX test_idx on test_table ABORT ; View metadata about resumable online index operations SELECT * FROM sys.index_resumable_operations ;
  64. 64. Upgrading and migrating to SQL Server 2017
  65. 65. Upgrade and migration tools Data Migration Assistant (DMA) • Upgrade from previous version of SQL Server (on-premises or SQL Server 2017 in Azure VM) SQL Server Migration Assistant • Migrate from Oracle, MySQL, SAP ASE, DB2, or Access to SQL Server 2017 (on-premises or SQL Server 2017 in Azure VM) Azure Database Migration Service • Migrate from SQL Server, Oracle, or MySQL to Azure SQL Database or SQL Server 2017 in Azure VM
  66. 66. Upgrading to SQL Server 2017 In-place or side-by-side upgrade path from: • SQL Server 2008 • SQL Server 2008 R2 • SQL Server 2012 • SQL Server 2014 • SQL Server 2016 Side-by-side upgrade path from: • SQL Server 2005 Use Data Migration Assistant to prepare for migration
  67. 67. Legacy SQL Server instance DMA: Assess and upgrade schema 1. Assess and identify issues 2. Fix issues 3. Upgrade database Data Migration Assistant SQL Server 2017
  68. 68. Choosing a migration target “What’s the best path for me?”
  69. 69. Migrating to SQL Server 2017 from other platforms Identify apps For migration Use migration tools and partners Deploy to production SQL Server Migration Assistant Global partner ecosystem AND SQL Server 2017 on Windows SQL Server 2017 on Linux OR
  70. 70. Migration Assistant Database and application migration process • Database connectivity • User Login and Permission • Performance Tuning • Database Discovery • Architecture requirements • (HADR, performance, locale, maintenance, dependencies, etc.) • Migration Assessment • Complexity, Effort, Risk • Schema Conversion • Data Migration • Embedded SQL Statements • ETL and Batch • System and DB Interfaces
  71. 71. SQL Server Migration Assistant (SSMA) Automates and simplifies all phases of database migration Assess migration complexityMigration Analyzer Convert schema and business logicSchema Converter Migrate dataData Migrator Supports migration from DB2, Oracle, SAP ASE, MySQL, or Access to SQL Server Validate converted database codeMigration Tester
  72. 72. Using SQL Server Migration Assistant (SSMA) SSMA: Automates components of database migrations to SQL Server DB2, Oracle, Sybase, Access, and MySQL analyzers are available Assess the migration project Migrate schema and business logic Migrate data Convert the application Test, integrate, and deploy SSMA migration analyzer SSMA data migrator SSMA schema converter
  73. 73. Azure solution paths Do not have to manage any VMs, OS or database software, including upgrades, high availability, and backups. Highly customized system to address the application’s specific performance and availability requirements.
  74. 74. Azure migration tools & services Assess Migrate
  75. 75. Legacy SQL Server instance DMA: Assess and migrate schema 1. Assess and identify issues 2. Fix issues 3. Convert and deploy schema DMA
  76. 76. Oracle SQL SQL DB Azure Database Migration Service Accelerating your journey to the cloud Streamline database migration to Azure SQL Database (PaaS) Managed service platform for migrating databases Migrate SQL Server & 3rd party databases to Azure SQL Database
  77. 77. S E A M L E S S C LO U D I N T E G R AT I O N Easy lift-and-shift migration Azure SQL Database Managed Instance private preview facilitates lift and shift migration from on- premises SQL Server to cloud Azure Hybrid Benefit for SQL Server maximizes current on-premises license investments to facilitate migration Database Migration Service (DMS) private preview provides seamless and reliable migration at scale with minimal downtime Most consistent data platform Database Migration Ser vice (DMS) Azure SQL Database Managed Instance Azure Hybrid Benefit (AHB) for SQL Ser ver
  78. 78. Editions, features, and capacity
  79. 79. SQL Server Editions SQL Server edition Definition Enterprise The premium offering, SQL Server Enterprise edition delivers comprehensive high-end datacenter capabilities with blazing-fast performance, unlimited virtualization, and end-to-end business intelligence — enabling high service levels for mission-critical workloads and end user access to data insights. Standard SQL Server Standard edition delivers basic data management and business intelligence database for departments and small organizations to run their applications and supports common development tools for on-premise and cloud — enabling effective database management with minimal IT resources. Web SQL Server Web edition is a low total-cost-of-ownership option for Web hosters and Web VAPs to provide scalability, affordability, and manageability capabilities for small to large scale Web properties. Developer SQL Server Developer edition lets developers build any kind of application on top of SQL Server. It includes all the functionality of Enterprise edition, but is licensed for use as a development and test system, not as a production server. SQL Server Developer is an ideal choice for people who build SQL Server and test applications. Express Express edition is the entry-level, free database and is ideal for learning and building desktop and small server data- driven applications. It is the best choice for independent software vendors, developers, and hobbyists building client applications. If you need more advanced database features, SQL Server Express can be seamlessly upgraded to other higher end versions of SQL Server. SQL Server Express LocalDB, a lightweight version of Express that has all of its programmability features, yet runs in user mode and has a fast, zero-configuration installation and a short list of prerequisites.
  80. 80. Capacity Limits by Edition Feature Enterprise Standard Web Express Maximum compute capacity used by a single instance - SQL Server Database Engine Operating system maximum Limited to lesser of 4 sockets or 24 cores Limited to lesser of 4 sockets or 16 cores Limited to lesser of 1 socket or 4 cores Maximum compute capacity used by a single instance - Analysis Services or Reporting Services Operating system maximum Limited to lesser of 4 sockets or 24 cores Limited to lesser of 4 sockets or 16 cores Limited to lesser of 1 socket or 4 cores Maximum memory for buffer pool per instance of SQL Server Database Engine Operating System Maximum 128 GB 64 GB 1410 MB Maximum memory for Columnstore segment cache per instance of SQL Server Database Engine Unlimited memory 32 GB 16 GB 352 MB Maximum memory-optimized data size per database in SQL Server Database Engine Unlimited memory 32 GB 16 GB 352 MB Maximum relational database size 524 PB 524 PB 524 PB 10 GB
  81. 81. SQL Server Features Server components Description SQL Server Database Engine SQL Server Database Engine includes the Database Engine, the core service for storing, processing, and securing data, replication, full-text search, tools for managing relational and XML data, in database analytics integration, and Polybase integration for access to Hadoop and other heterogeneous data sources, and the Data Quality Services (DQS) server. Analysis Services Analysis Services includes the tools for creating and managing online analytical processing (OLAP) and data mining applications. Reporting Services Reporting Services includes server and client components for creating, managing, and deploying tabular, matrix, graphical, and free-form reports. Reporting Services is also an extensible platform that you can use to develop report applications. Integration Services Integration Services is a set of graphical tools and programmable objects for moving, copying, and transforming data. It also includes the Data Quality Services (DQS) component for Integration Services. Master Data Services Master Data Services (MDS) is the SQL Server solution for master data management. MDS can be configured to manage any domain (products, customers, accounts) and includes hierarchies, granular security, transactions, data versioning, and business rules, as well as an Add-in for Excel that can be used to manage data. Machine Learning Services (In-Database) Machine Learning Services (In-Database) supports distributed, scalable machine learning solutions using enterprise data sources. SQL Server 2017 supports R and Python. Machine Learning Server (Standalone) Machine Learning Server (Standalone) supports deployment of distributed, scalable machine learning solutions on multiple platforms and using multiple enterprise data sources, including Linux, Hadoop, and Teradata. SQL Server 2017 supports R and Python.
  82. 82. Features by Edition Some SQL Server features (and sub-features) are available only to certain editions: • see https://docs.microsoft.com/en-us/sql/sql-server/editions-and- components-of-sql-server-2017 for a complete list
  83. 83. Q & A ? James Serra, Big Data Evangelist Email me at: JamesSerra3@gmail.com Follow me at: @JamesSerra Link to me at: www.linkedin.com/in/JamesSerra Visit my blog at: JamesSerra.com (where this slide deck is posted under the “Presentations” tab)

Editor's Notes

  • Fluff, but point is I bring real work experience to the session
  • Title: SQL Server 2017 – Meeting you where you are

    Any data
    Access diverse data, including video, streaming, documents, relational, both external data and data internal to your org
    Use PolyBase to access Hadoop big data and Azure blog storage with the simplicity of T-SQL
    You can use Azure DocumentDB, a NoSQL document database service, for native JSON support and JavaScript built directly inside the database engine
    Any application
    Leverage the T-SQL skills of your talent base to run advanced analytics through R/Python models, and to access structured and unstructured data
    Take advantage of Microsoft–created database connectivity drivers and open-source drivers that enable developers to build any application using the platforms and tools of their choice, including Python, Ruby, and Node.js
    Anywhere
    Flexible on-premises and cloud
    Easily backup to the cloud
    You can now migrate a SQL Server workload to Azure SQL DB. The parity is there and the notion that SQL Server doesn’t map to Azure SQL DB is no longer the case
    Keep more historical data at your fingertips by dynamically stretching tables to the cloud with Stretch Database.
    Choice of platform
    Aligns to your operating system environment. Today, SQL Server is available on Windows/Windows Server, Linux, and Docker
    Benefit from continued integration with Windows Server for industry-leading performance, scale and virtualization on Windows.

    Note: Tux penguin image created by Larry Ewing



  • Source - https://docs.microsoft.com/en-us/sql/integration-services/what-s-new-in-integration-services-in-sql-server-2017
  • Source: https://docs.microsoft.com/en-us/sql/analysis-services/what-s-new-in-sql-server-analysis-services-2017
  • Source: https://docs.microsoft.com/en-us/sql/reporting-services/what-s-new-in-sql-server-reporting-services-ssrs
  • Source: https://docs.microsoft.com/en-us/sql/advanced-analytics/what-s-new-in-sql-server-machine-learning-services

    Microsoft R Services has been renamed Microsoft Machine Learning Services
  • Customers need flexibility when it comes to the choice of platform, programming languages & data infrastructure to get from the most from their data.
     
    Why? In most IT environments, platforms, technologies and skills are as diverse as they have ever been, the data platform of the future needs to you to build intelligent applications on any data, any platform, any language on premises and in the cloud.
     
    SQL Server manages your data, across platforms, with any skills, on-premises & cloud
    Our goal is to meet you where you are with on any platform, anywhere with the tools and languages of your choice.
    SQL Server Database Engine now has support for Windows, Linux & Docker Containers.
  • Microsoft has focused on providing a Linux-native user experience for SQL Server, starting with the installation process. Installing SQL Server 2017 uses the standard package-based installation method for Linux, using yum for Fedora-based distributions, apt-get for Debian-based distributions, and zypper for SUSE Linux Enterprise Server (SLES).
    Administrators can update SQL Server 2017 instances on Linux using their existing package update/upgrade processes.
    The SQL Server service runs natively using systemd, and performance can be monitored through the file system as for other system daemons.
    Linux file paths are supported in T-SQL statements and scripts such as defining/changing the location of data files or database backup files.
    High-availability clustering can be managed with popular Linux high-availability solutions like Pacemaker and Corosync.
    SQL Server command-line tools are available for Linux (including sqlcmd and bcp). MacOS versions of these tools are available as a preview at time of writing (https://blogs.technet.microsoft.com/dataplatforminsider/2017/05/16/sql-server-command-line-tools-for-macos-released/).
    Existing Windows tools such as SQL Server Management Studio (SSMS), SQL Server Data Tools (SSDT), PowerShell module (sqlps) can be used to manage SQL Server on Linux from a Windows instance.
    The Visual Studio Code extension for SQL Server can run on macOS, Linux, or Windows.
    Microsoft offers tools such as Migration Assistant, also supported on Linux, to assist with moving existing workloads on SQL Server.
  • The SQL Server Docker container is built with Ubuntu 16.04 and SQL Server for Linux.
  • The Platform Abstraction Layer (“PAL”) is what enables SQL Server to run on Linux and Docker. The PAL is used to consolidate OS/platform specific code to enable SQL Server code to become OS agnostic.
    The SQL Server team set strict requirements to ensure that functionality, performance, and scale were not compromised when deployed to Linux.
    Part of what makes this possible is the integration of certain parts of MSR’s project Drawbridge. Drawbridge provided an abstraction between the underlying operating system and the application for the purposes of secure containers. Drawbridge was combined with SQL Server OS, which provided memory management, thread scheduling, and IO services, to create SQLPAL.

    In short, the creation of the PAL allows the same, time-proven core code base for SQL Server to run on new environments such as Docker and Linux – as opposed to porting the Windows code base into multiple operating environments. SQL Server 2017 is not a re-write or a port – it is the same performant, scalable product Microsoft customers have relied upon for years.

    For more detail, see https://blogs.technet.microsoft.com/dataplatforminsider/2016/12/16/sql-server-on-linux-how-introduction/
  • Because the Linux and Windows versions of SQL Server use the same code base, existing applications, drivers, frameworks, and tools will connect to and operate with SQL Server on Linux without modification.

    (The screenshot shows the Visual Studio Code mssql extension in operation)
  • https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-develop-connectivity-libraries

    The list of languages and drivers given in the slide is not exhaustive.
    Any language which supports ODBC data sources should be able to use the ODBC drivers.
    Any languages based on the JVM should be able to use the JDBC or ODBC drivers.
    The Microsoft ODBC driver for SQL Server is available in native versions for Windows, Linux and macOS
  • SQL Server on Linux aims to support the core relational database engine capabilities.

    In general, our goal is “it’s just SQL Server.”
    95% of features just work – anything app or coding related
    Some features have partial support – e.g. SQL Server Agent isn’t going to launch a windows command prompt
    Some features are in progress
    Some features we’ll never support – e.g. FileTable, where you have a win32 share to place files that show up in the engine – the next slide contains more details

    And while it’s the same SQL Server that you may (or may not!) be used to, we’re putting in a lot of effort to be a good Linux citizen.

    Callouts:
    Package-based installs – no “crappy installers” like predicted on Twitter; if SQL is coming to Linux, we’re going to do it right
    Failover Clustering – resilience against OS/SQL failures; automatic failover within seconds
    Log Shipping – warm standbys for DR
    Xplat CLI – sqlcmd lets you connect/query from any OS; bcp lets you bulk copy data;
    In-Memory – 30-100x performance increases by keeping tables in-memory and using natively compiled queries
    ColumnStore – why SQL Server is the leader in Gartner’s Magic Quadrant for Data Warehousing, holds the top 3 slots in performance benchmark
    Always Encrypted – protect your most sensitive data even from high-privileged database administrators
    AD authentication – no need to manage separate credentials for SQL Server on Linux (https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-active-directory-authentication)



  • SQL Server on Linux aims to support the core relational database engine capabilities.

    In general, our goal is “it’s just SQL Server.”
    95% of features just work – anything app or coding related
    Some features have partial support – e.g. SQL Server Agent isn’t going to launch a windows command prompt
    Some features are in progress
    Some features we’ll never support – e.g. FileTable, where you have a win32 share to place files that show up in the engine – the next slide contains more details

    And while it’s the same SQL Server that you may (or may not!) be used to, we’re putting in a lot of effort to be a good Linux citizen.

    Callouts:
    Package-based installs – no “crappy installers” like predicted on Twitter; if SQL is coming to Linux, we’re going to do it right
    Failover Clustering – resilience against OS/SQL failures; automatic failover within seconds
    Log Shipping – warm standbys for DR
    Xplat CLI – sqlcmd lets you connect/query from any OS; bcp lets you bulk copy data;
    In-Memory – 30-100x performance increases by keeping tables in-memory and using natively compiled queries
    ColumnStore – why SQL Server is the leader in Gartner’s Magic Quadrant for Data Warehousing, holds the top 3 slots in performance benchmark
    Always Encrypted – protect your most sensitive data even from high-privileged database administrators



  • NB: This slide is correct as at SQL Server 2017 RC2. The list of unsupported features might change in later releases - see https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-release-notes
  • In-Memory OLTP is available in all editions of SQL Server 2017 (including SQL Server 2017 Express Edition). This is a change introduced in SQL Server 2016 Service Pack 1, prior to which In-Memory OLTP was restricted to Enterprise Edition.
  • Source - https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-availability-group-overview

    NB - At this point, SQL Server's integration with Pacemaker on Linux is not as coupled as with WSFC on Windows. From within SQL Server, there is no knowledge about the presence of the cluster, all orchestration is outside in and the service is controlled as a standalone instance by Pacemaker. Also, virtual network name is specific to WSFC, there is no equivalent of the same in Pacemaker. Always On dynamic management views that query cluster information will return empty rows. You can still create a listener to use it for transparent reconnection after failover, but you will have to manually register the listener name in the DNS server with the IP used to create the virtual IP resource

    NB – The configuration shown in this side is not one customers typically use (one primary in FCI, one local secondary replica and 2 remote secondary replicas). The most common layout is primary replica in FCI in the primary Data Center for HA and then secondary replica in the remote Data Center (different subnet) for DR.
    In this case, the multiple secondary replicas might be used for read scale-out.
  • [this slide has animation]
    Mission critical availability on any platform
    In SQL Server 2017, we are enabling the same High Availability (HA) and Disaster Recovery (DR) solutions on all platforms supported by SQL Server, including Windows and Linux. Always On Availability Groups is SQL Server’s flagship solution for HA
    [click]
    and DR.
    [click]

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

    New flexibility to do HA without Windows Server fail over clustering
    Fail-over clustering with Pacemaker and more through integration scripts and guides
    Always On availability groups with automatic fail-over, listener, synchronous replication, read-only secondaries
    Shared disk failover clusters
    Backup and restore: .bak, .bacpac, and .dacpac
    Log shipping

  • Distributed transactions for databases in availability groups: https://docs.microsoft.com/en-us/sql/database-engine/availability-groups/windows/transactions-always-on-availability-and-database-mirroring
  • Source: https://docs.microsoft.com/en-us/sql/relational-databases/automatic-tuning/automatic-tuning
    Automatic tuning is a continuous monitoring and analysis process that constantly learns about the characteristic of your workload and identify potential issues and improvements.

    Automatic tuning in SQL Server 2017 notifies you whenever a potential performance issue is detected, and lets you apply corrective actions, or lets the Database Engine automatically fix performance problems. Automatic tuning in SQL Server 2017 enables you to identify and fix performance issues caused by SQL plan choice regressions.
    Automatic tuning in Azure SQL Database creates necessary indexes and drops unused indexes.

    The Database Engine monitors the queries that are executed on the database and automatically improves performance of the workload. Database Engine has a built-in intelligence mechanism that can automatically tune and improve performance of your queries by dynamically adapting the database to your workload. There are two automatic tuning features that are available:
    Automatic plan correction (available in SQL Server 2017) that identifies problematic plans and fixes SQL plan performance problems.
    Automatic index management (available in Azure SQL Database) that identifies indexes that should be added in your database, and indexes that should be removed.

    Constantly monitoring performance can be a hard and tedious task, especially when dealing with many databases. Managing a huge number of databases might be impossible to do efficiently. Instead of monitoring and tuning your database manually, you might consider delegating some of the monitoring and tuning actions to Database Engine using automatic tuning feature.
  • Automatic plan monitoring is enabled by default in SQL Server 2017.

    For more information about sys.dm_db_tuning_recommendations, see https://docs.microsoft.com/en-us/sql/relational-databases/system-dynamic-management-views/sys-dm-db-tuning-recommendations-transact-sql
  • Source - https://docs.microsoft.com/en-us/sql/relational-databases/automatic-tuning/automatic-tuning#automatic-plan-correction

    In addition to detection, the Database Engine can automatically switch to the last known good plan whenever the regression is detected.
    When the Database Engine applies a recommendation, it automatically monitors the performance of the forced plan. The forced plan will be retained until a recompile (for example, on next statistics or schema change) if it is better than the regressed plan. If the forced plan is not better than the regressed plan, the new plan will be unforced and the Database Engine will compile a new plan.

    Enabling automatic plan choice correction
    The user can enable automatic tuning per database and specify that last good plan should be forced whenever some plan change regression is detected. Automatic tuning is enabled using the following command:
    ALTER DATABASE current SET AUTOMATIC_TUNING ( FORCE_LAST_GOOD_PLAN = ON ); Once you turn-on this option, Database Engine will automatically force any recommendation where the estimated CPU gain is higher than 10 seconds, or the number of errors in the new plan is higher than the number of errors in the recommended plan, and verify that the forced plan is better than the current one.
  • Source - https://docs.microsoft.com/en-us/sql/relational-databases/performance/adaptive-query-processing

    Adaptive query processing provides three techniques to improve execution plan selection.
    Batch mode memory grant feedback
    Batch mode adaptive joins
    Interleaved execution for multi-statement table valued functions

  • [this slide has animation – bullets appear in sequence]


  • [this slide has animation]
    There are many reasons that cardinality estimates might be inaccurate
    [click]
    When statistics do not exist
    [click]
    When statistics exist but are out of date, because the profile of the data has changed but the statistics have not
    [click]
    When statistics exist, but are not based on a representative sample of the data
    [click]
    When a cached query plan is optimized for a non-representative parameter value (parameter sniffing)
    [click]
    When the query uses constructs for which cardinality estimates cannot be directly inferred
    [click]
    Or when assumptions inferred from the statistics, such as correlation, are not correct
  • [this slide has animation – bullets appear in sequence]
  • Source - https://docs.microsoft.com/en-us/sql/relational-databases/performance/adaptive-query-processing#interleaved-execution-for-multi-statement-table-valued-functions

    Interleaved execution changes the unidirectional boundary between the optimization and execution phases for a single-query execution and enables plans to adapt based on the revised cardinality estimates. During optimization if we encounter a candidate for interleaved execution, which is currently multi-statement table valued functions (MSTVFs), we will pause optimization, execute the applicable subtree, capture accurate cardinality estimates, and then resume optimization for downstream operations. MSTVFs have a fixed cardinality guess of “100” in SQL Server 2014 and SQL Server 2016, and “1” for earlier versions. Interleaved execution helps workload performance issues that are due to these fixed cardinality estimates associated with multi-statement table valued functions.
  • Source - https://docs.microsoft.com/en-us/sql/relational-databases/performance/adaptive-query-processing#batch-mode-memory-grant-feedback
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  • https://docs.microsoft.com/en-us/sql/t-sql/queries/match-sql-graph

    The node names inside MATCH can be repeated. In other words, a node can be traversed an arbitrary number of times in the same query.
    An edge name cannot be repeated inside MATCH.
    An edge can point in either direction, but it must have an explicit direction.
    OR and NOT operators are not supported in the MATCH pattern. MATCH can be combined with other expressions using AND in the WHERE clause. However, combining it with other expressions using OR or NOT is not supported.
  • Source - https://docs.microsoft.com/en-us/sql/t-sql/functions/trim-transact-sql
  • Source - https://docs.microsoft.com/en-us/sql/t-sql/functions/concat-ws-transact-sql

    CONCAT_WS indicates concatenate with separator.
  • Source - https://docs.microsoft.com/en-us/sql/t-sql/functions/translate-transact-sql

    The first example is equivalent to (but much simpler than) the following statement using REPLACE: SELECT REPLACE(REPLACE(REPLACE(REPLACE('2*[3+4]/{7-2}','[','('), ']', ')'), '{', '('), '}', ')');
  • Source - https://docs.microsoft.com/en-us/sql/t-sql/functions/string-agg-transact-sql
  • Source - https://docs.microsoft.com/en-us/sql/t-sql/statements/bulk-insert-transact-sql
    Source - https://docs.microsoft.com/en-us/sql/t-sql/functions/openrowset-transact-sql
  • Source - https://blogs.technet.microsoft.com/dataplatforminsider/2017/04/20/resumable-online-index-rebuild-is-in-public-preview-for-sql-server-2017-ctp-2-0/
    See also https://docs.microsoft.com/en-us/sql/t-sql/statements/alter-index-transact-sql#online-index-operations
  • Source - https://docs.microsoft.com/en-us/sql/t-sql/statements/alter-index-transact-sql#online-index-operations
  • Source - https://docs.microsoft.com/en-us/sql/database-engine/install-windows/upgrade-sql-server
    Detailed upgrade path guide - https://docs.microsoft.com/en-us/sql/database-engine/install-windows/choose-a-database-engine-upgrade-method
    Data Migration Assistant replaces the SQL Server Upgrade Advisor - https://docs.microsoft.com/en-us/sql/database-engine/install-windows/prepare-for-upgrade-by-running-data-migration-assistant
  • [this slide contains animations]
    In assessments, Data Migration Assistant (DMA) automates the potentially overwhelming process of checking database schema, static objects for potential breaking changes from prior versions and also recommends performance and reliability recommendations on target server.
    [click]
    The first phase is to use DMA assess the legacy database and identify issues
    [click]
    In the second phase, issues are fixed. The first and second phases are repeated until all issues are addressed
    [click]
    Finally, the database is upgraded to SQL Server 2017

    For more information, see https://blogs.msdn.microsoft.com/datamigration/2016/08/26/data-migration-assistant-how-to-assess-your-on-premises-sql-server-instance/
  • Intent
    Visualize decision point: migrate to Azure SQL Database or Azure VM. Answer the question “what’s the best path for me?”

    Two options for cloud migration:
    - Infrastructure as a Service (IaaS) : SQL Server in Azure Virtual Machine (VM) allows you to run SQL Server inside a virtual machine in the cloud.
    - Platform as a Service (PaaS). Microsoft Azure SQL Database is a relational database-as-a-service.
    Both of these different cloud offerings can provide enterprise level database support, but their characteristics, capabilities and costs are quite different.

  • https://docs.microsoft.com/en-us/sql/ssma/sql-server-migration-assistant

    SAP ASE was formerly known as SAP Sybase ASE / Sybase.
  • Our customers have given feedback and we wanted to acknowledge and act on it. We are addressing the migration concerns by releasing
    Data Migration Assistant (DMA)—Built by SQL Engineering team with latest and greatest knowledge base of all SQL versions. This helps with assessing and planning.
    Database Migration Service (DMS)—Newest offering, Azure service that helps you move your on-premises DBs to Azure at scale.
  • [this slide contains animations]
    In assessments, Data Migration Assistant (DMA) automates the potentially overwhelming process of checking database schema, static objects for potential breaking changes from prior versions and also recommends performance and reliability recommendations on target server.
    [click]
    The first phase is to use DMA assess the legacy database and identify issues
    [click]
    In the second phase, issues are fixed. The first and second phases are repeated until all issues are addressed
    [click]
    Finally, the database is converted and deployed to Azure
  • Source : https://azure.microsoft.com/en-gb/campaigns/database-migration/

    As organizations look to optimize their IT infrastructure so they have more time and resources to focus on business transformation, Microsoft is committed to accelerating these initiatives. Microsoft announced that a new migration service is coming to Azure to streamline customers’ journey to the cloud. This service will streamline the tasks required to move existing competitive and SQL Server databases to Azure. Deployment options will include Azure SQL Database and SQL Server in Azure VM.

    Managed service platform for migrating databases
    Azure SQL DB and Managed Instance as targets
    Competitive DBs – Oracle and more
    Meets enterprise non-functional requirements (NFRs) – Compliance, Security, Costs, etc.

    TALK ACOUT THE TECHNICAL DETAILS:
    Source ->Target.
    Secure.
    Feature Parity with competitors.
    Zero data loss and near zero downtime migration Azure platform service.


  • Source - https://docs.microsoft.com/en-us/sql/sql-server/editions-and-components-of-sql-server-2017
  • Source - https://docs.microsoft.com/en-us/sql/sql-server/editions-and-components-of-sql-server-2017
  • Source - https://docs.microsoft.com/en-us/sql/sql-server/editions-and-components-of-sql-server-2017
  • Source - https://docs.microsoft.com/en-us/sql/sql-server/editions-and-components-of-sql-server-2017
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