Microsoft Ignite 2015 4/16/2017 3:29 PM © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

SQL Server Evolution SQL 2016 new innovations – Part 2 Lindsey Allen Principal Group Program Manager Borko Novakovic Program Manager

What’s in this session SQL 2016 highlights Scaling up to new heights – 16 sockets In-memory Engine Advances Query flight recorder - Query Store Time travel and auditing with Temporal database Bring Advanced Analytics to your data Call to action

Mission critical platform Performance Security Availability Scalability Operational analytics Minimize performance impact running real-time analytics on transaction data Avoid data sprawl In-memory OLTP for more applications Query Store Always Encrypted Row level security Dynamic Data Masking Enhanced AlwaysOn 3 synchronous replicas for auto failover across domains Round robin load balancing of replicas DTC for transactional integrity across database instances with AlwaysOn Enhanced online operations Support for Windows Server 2016 12TB 16 Sockets

Demo SQL scalability on HP superdomeX Lindsey Allen

In-memory engine Faster Transactions Faster Queries IN-MEMORY OLTP 4/16/2017 In-memory engine Faster Transactions IN-MEMORY OLTP Faster Queries IN-MEMORY DW Up to 30x faster transaction processing with In-Memory OLTP Over 100x query speed and significant data compression with In-Memory ColumnStore © 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

In-memory OLTP Hardware trends Business Customer Benefits High performance data operations Efficient business-logic processing Frictionless scale-up Hybrid engine and integrated experience Architectural Pillars Main-Memory Optimized T-SQL Compiled to Machine Code High Concurrency SQL Server Integration Optimized for in-memory data Indexes (hash and range) exist only in memory No buffer pool, B-trees Stream-based storage T-SQL compiled to machine code via C code generator Invoking a procedure is just a DLL entry-point Aggressive optimizations @ compile-time Multi-version optimistic concurrency control with full ACID support Core engine uses lock-free algorithms No lock manager, latches or spinlocks Same manageability, administration & development experience Integrated queries & transactions Integrated HA and backup/restore Drivers Hardware trends Business Steadily declining memory price, NVRAM Stalling CPU clock rate Many-core processors TCO

Columnstore (index) Data stored as rows Data stored as columns Benefits: Improved compression: Data from same domain compress better Reduced I/O: Fetch only columns needed Improved Performance: More data fits in memory C1 C2 C3 C5 C4 …

In-memory column store 2016 Updatable NCCI In-Memory OLTP + Column-store Faster batch mode scans using CPU vector instructions Dynamic Aggregate pushdown PK/FK enforcement Offload Reporting to AlwaysOn Secondary Replica

Demo Column store performance improvement Lindsey Allen LINEITEM: 600M rows ORDERS: 150M rows CUSTOMER: 15M rows CUSTOMER_ZIP: 315M rows

Deeper insights across data & Hyperscale Cloud Access any data Scale and manage Hybrid solutions PolyBase Native JSON Temporal database support Power Query for analytics and reporting Built-in Advanced Analytics Business insights through rich visualizations on any mobile device Enterprise-grade Analysis Services New single SSDT in Visual Studio 2015 Enhanced MDS Enhanced SSIS Enhanced Reporting Services Stretch tables into Azure Power BI with on-premises data Hybrid scenarios with SSIS Azure Data Factory integration with SSIS Package Lineage and impact analysis Connect SSIS to cloud data sources Enhanced backup to Azure X faster restore and 50% reduction in storage Easy migration of on-premises SQL Server

Query Store Flight data recorder for your database

When performance is not good… Web site is down Database is not working Temporary perf. issues Impossible to predict / root cause DB upgraded Regression caused by new bits Plan choice change can cause these problems

With Query Store you CAN… Long-term/strategic Find and fix plan regressions Identify top resource consumers De-risk SQL Server upgrade Deeply analyze workload patterns Short-term/tactical

Demo Fixing performance regression using Query Store Borko Novakovic

Temporal Database

Why temporal? Real data sources are dynamic Workarounds are… 4/16/2017 3:29 PM Why temporal? Real data sources are dynamic Historical data may be critical to business success Traditional databases fail to provide required insights Workarounds are… Complex, expensive, limited, inflexible, inefficient SQL Server 2016 makes life easy No change in programming model New Insights © 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

history as a stretch table: PeriodEnd < “Now - 6 months” Microsoft Ignite 2015 4/16/2017 3:29 PM Facts: History is much bigger than actual data Retained between 3 and 10 years “Warm”: up to a few weeks/months “Cold”: rarely queried SELECT * FROM Department FOR SYSTEM_TIME AS OF '2010.01.01' Azure SQL Database Solution: history as a stretch table: PeriodEnd < “Now - 6 months” © 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

Demo Auditing with Temporal database Borko Novakovic

Server & Tools Business 4/16/2017 Built-in advanced analytics In-database analytics Example Solutions Fraud detection Sales forecasting Warehouse efficiency Predictive maintenance Extensibility ? R R Integration Microsoft Azure Machine Learning Marketplace New R scripts 010010 100100 010101 010010 100100 010101 Data Scientist Analytic Library Interact directly with data 010010 100100 010101 010010 100100 010101 Data Developer/DBA Manage data and analytics together T-SQL Interface Relational Data Built-in to SQL Server © 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

Demo Build-in Advanced analytics Lindsey Allen

Fisher’s Iris flower dataset a typical test case in machine learning Iris species can be identified based on their sepal and petal length/width Plotting these attributes shows well differentiated classes with few overlaps AML Gallery ML Studio SSMS / R SSRS / CR Excel / PV Power BI.com

CTA [TAE8DD] Azure SQL Data Warehouse Overview [T55A62] Microsoft Azure SQL Database: Overview [TC530B] Stretching on-prem databases to cloud [T4D1C9]In-Memory Technologies Overview Polybase in SQL Server Futures - A sneak Peek [TB63B1] In-Memory OLTP Futures [T9F2FD] Overview of Microsoft SQL Server Security Futures Temporal, Query Store and JSON Support in SQL Server Futures [TCFAC2] ColumnStore Index: Microsoft SQL Server 2014 and Beyond [TD4D79] Best Practices for Designing Your Cloud-Based, Data-Tier Strategy [TBD345] APS and Data Warehousing in the cloud - Technical drilldown [TB01EC] Elastic Scale for Microsoft Azure SQL Database

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