J1 - Keynote Data Platform - Rohan Kumar
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- 7. Toutes les infos ? Suivez @MSCloudSummit #MSCloudSummit
- 8. Rohan Kumar General Manager Database Systems Microsoft Corp., Redmond Data Platform Keynote
- 12. IntelligenceCloudBig Data & IoT Three major trends are converging
- 13. How successful companies are staying ahead Exploring new business opportunities with data-driven services Getting the right products to the right places with inventory management Offering customers exactly what they want, when they want it, with personalization Fixing problems proactively before they start with predictive maintenance
- 14. Database + Big Data Information Management Machine Learning + Analytics Intelligence Dashboards + Visualizations
- 15. Database + Big Data Information Management Machine Learning + Analytics Intelligence Dashboards + Visualizations 1010 0101 0010 {} Across any data any platform any language T-SQL Java PHP Node.js C/C++ C#/VB.NET Python Ruby
- 16. Customer priorities Optimize IT Business Transformation
- 17. Microsoft solutions Optimize IT Business Transformation Intelligent platform for SaaS apps Self-managed services for reduced OPEX Modern data platform for high ROI Integrated intelligence for breakthrough experiences
- 18. SaaS Why cloud, why now? Organizations adopting business SaaS apps to reduce CAPEX + OPEX For software providers, big opportunity for growth—30% of enterprise app revenue by 2018 Ship innovations to customers faster Preliminary IDC Hosted Dedicated Private Cloud Services Forecast (4Q2014) Enterprise apps revenue share 2013 2014 2015 2016 2017 2018 SaaS On-premise $50B SaaS revenue worldwide
- 19. Why Azure for building high-growth SaaS apps Drive operational efficiency Scale costs with performance and optimize your apps Protect your customer’s data Secure your data and apps with built-in tools Create engaging experiences Engage users with rich visualizations and analytics Fuel growth Scale apps globally on hybrid platforms or in the cloud
- 20. Azure DocumentDB Blazing fast, planet-scale NoSQL Lower TCO Less expensive than running NoSQL databases Infinite, global scale Scale from 100 to 10s of millions of requests/sec Guaranteed performance Guaranteed low latency for reads and writes Build and ship code faster Support for SQL, Java, MongoDB
- 21. Azure SQL Database Intelligent, scalable database service Lower TCO 53% less expensive than on-prem/hosted Faster time to market 75% faster app deployment cycles Manage thousands of DBs as one 47% staff hours reclaimed for other tasks Customer 1 Customer …500 Customer 2 Greater security Built-in security with AI- based Threat Detection SECURITY
- 22. Saved $¼ Million Sped dev cycle 2x
- 24. App Migration Why thousands? Long-time challenge for IT Manage long tail of tactical apps at scale Reduce burden on uptime and security 406% 98% 53%Less expensive than on-prem/hosted ROI Less DB-related downtime 47%Increase in staff efficiencies Source: IDC White Paper | The Business Value of Microsoft Azure SQL Database Services
- 25. Migrating databases to Azure Azure SQL Database SQL Server in a VM Migration Tools
- 26. The MS IT Example 40,000 databases, across 15,000 SQL Server instances GOAL: Reduce costs and demands on IT 1 DB Assessment 53%SQL Database potentially ready to be migrated Migration2
- 27. Modernize critical applications Security and data protection a top concern Increasing performance requirements as demands grow Pressure to deliver the best service and most engaging experiences Ingest real-time train data: Brakes are hot! Query across historical data: They’ve been hot for 4 hours! Analyze global trends: Could lead to accident
- 28. TPC-H Oracle is #5#2 SQL Server #1 SQL Server #3 SQL Server at massive scale 1M Predictions per second The power of SQL Server: Everything built-in Any platform Any data 1010 0101 0010 {} Any language T-SQL Java PHP Node.js C/C++ C#/VB.NET Python Ruby with in-memory OLTP Most secure database Self-service BI per user National Institute of Standards and Technology Comprehensive Vulnerability Database update 5/4/2015
- 29. In-database Analytics (R) In-memory OLTP & DW Operational Analytics Compression & Partitioning PolyBase Always Encrypted Row-level Security & Data Masking Enterprise Standard Express
- 30. In-database Analytics (R) In-memory OLTP & DW Operational Analytics Compression & Partitioning PolyBase Always Encrypted Row-level Security & Data Masking Enterprise Standard Express
- 31. HTAP built in to SQL Server 2016 Up to 100x faster analytical queries Queries from minutes to seconds Up to 30x faster transactions with in-memory OLTP Transforms business process 01001010101 In-memory ColumnStore In-memory OLTP Real-time business problem detection
- 32. Industry-leading data warehousing performance Oracle is #5#2 SQL Server #1 SQL Server #3 SQL Server Up to 100x faster analytical queries ETL services built-in PolyBase for single T-SQL query across relational and Hadoop
- 33. R built-in for 1M predictions/sec Predicting loan defaults Store Predictions In-memory OLTP ColumnStore Power BI dashboard In-Database analytics at scale R Prepare for analytics Visualize Data from 8M vehicle loans Predicts loan defaults Age, original balance, interest rate, loan remaining months, credit score R enables users to leverage existing tools with data where it already lives Industry-leading innovation in artificial intelligence to discover insights faster Machine learning with 1,000,000+ predictions per second with real- time R scoring
- 34. 100X Faster with R in SQL Server 2016
- 35. SQL Server R Services
- 36. Super-charge performance with database engine enhancements What’s coming in SQL Server vNext
- 37. Database engine enhancements SQL Server on the platform of your choice Linux Linux/Windows container Windows Windows Server • Linux distributions including RedHat Enterprise Linux (RHEL), Ubuntu, and SUSE Enterprise Linux (SLES) • Docker: Windows & Linux containers (and MacOS) • Windows Server / Windows 10 • Package-based installation, yum install, apt-get, and more options
- 38. Database engine enhancements Mission critical HA/DR on Windows and Linux • Availability Groups and Failover Clustering now also work with third party clustering technologies like Pacemaker • Availability Groups can now span Windows and Linux or be Linux- only AlwaysOn • Enables Testing • Enables Migrations • High Availability • Offload Backups • Scale BI Reporting • Third Party Clustering Technology Support
- 39. In-Memory OLTP Enhancements New T-SQL enhancements for In- Memory OLTP Tables and Natively Compiled Stored Procedures • JSON Support • CROSS Apply • Computed Columns Support • Removed index limit • CASE support • Sp_rename/sp_spaceused support • TOP (N) WITH TIES • Improved Performance on ALTER TABLE, failover operations • Azure VM Support for In-Memory Checkpoint Files In-Memory Tables JSON Computed Columns CROSS APPLY TOP CASE More Indexes
- 40. Columnstore Enhancements Clustered Columnstore LOB Support • LOBs are compressed, reducing DB size • Queries run over the compressed format, improving performance • Reduced backup times Online Non-Clustered Columnstore Index Build/Rebuild • Create and Rebuild Indexes without long blocking • Improved Support and Performance for HTAP scenarios
- 41. Database engine enhancements Adaptive query processing • Interleaved Compilation/Execution for challenging queries to improve plan quality and performance • Adjusts memory grants for repeated queries to avoid over or under allocating • Adaptive Joins adjust data joining strategy dynamically to improve query performance and stability over skewed data distributions Improved efficiency with Adaptive Query Processing Query Memory grant Learn Run query Before 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 After 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 1 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 Adaptive Query Processing Greater throughput
- 42. T-SQL Enhancements • STRING_AGG string concatenation aggregate • CONCAT_WS, TRANSLATE, TRIM string functions • New Japanese Collation Families • New BULK Insert options and CSV Support options (including Azure Storage and RFC 4180 CSV file support)
- 43. Database engine enhancements Support for graph data • Full CRUD support to create nodes and edges • Query language extension provides multi-hop navigation using join-free pattern matching • SQL engine integration enables querying across SQL tables and graph data • Existing tools work out of the box with graph dataAdventure Works Statistics Program manager Andy Smith Business analyst Mary Jones Bachelor of Science, Finance Degree earned Position Position Employer Employer Role in company Role in company Skill Skill Coworker Contoso Former employer Role in company Professional networking app
- 44. • State-of-the-art Machine Learning algorithms v1 • Fast Tree, Fast Forest, Fast Linear, One Class SVM, Neural Nets with GPU • Ease-of-Use • Package installation / uninstallation • Easy Model load and unload • Automatic generation of T-SQL • More data sources • OLAP to R Connector
- 45. Transform with Big Data + AI Infinite scale to embrace decades of data and real-time data as it’s created Machine learning for predictive analytics to look ahead at what may happen next Cognition built-in for human-like intelligence at scale across PBs of data Intelligence Cortana Cognitive Services Bot Framework Preconfigured Solutions Dashboards and Visualizations Machine Learning and Analytics Big Data Store Information Management
- 46. Azure SQL Data Warehouse True cloud elasticity for data warehousing Azure SQL Data Warehouse Machine Learning Data Stores BI Tools
- 47. Integrated cloud analytics solution Rich visualizationsPetabyte-scale Speed-of-thought interactivity Azure Analysis Services Power BIAzure SQL Data Warehouse
- 48. Switched off AWS to Azure for higher performance and cost-savings
- 49. 10,000,000,000,000 rows Analytics @ Petabyte Scale using Azure SQL Datawarehouse
- 50. Petabyte Scale Azure SQL DW
- 51. Azure Data Lake Services HDInsight YARN Managed clusters Data Lake Store HDFS No-limits Data Lake Data Lake Analytics YARN Analytics job service
- 52. Information Management Event Hubs Data Catalog Data Factory Big Data Stores SQL Data Warehouse Data Lake Store Machine Learning and Analytics HDInsight Stream Analytics Data Lake Analytics Machine Learning Intelligence Dashboards & Visualizations Cortana Bot Framework Cognitive Services Power BI Data Sources Apps Sensors and devices Action People Automated Systems Apps Web Mobile Bots Data Intelligence Cortana Intelligence Suite Transform data into intelligent action
- 53. Language Speech Search Machine Learning Knowledge Vision Spell check Speech API Entity linking Recommendation API Bing autosuggest Computer vision Emotion Forecasting Text to speech Thumbnail generation Anomaly detection Custom recognition (CRIS) Bing image search Web language model Customer feedback analysis Academic knowledge OCR, tagging, captioning Sentiment scoring Bing news search Bing web search Text analytics Cognition
- 54. “Thousands of partners sign in to our platform every hour. The response time from the Face API is incredible, enabling us to verify our drivers without slowing them down.” Dima Kovalev, Product Manager, Uber
- 55. Meeting customers where they are Optimize IT Business Transformation Intelligent platform for SaaS apps Self-managed services for reduced OPEX Modern data platform for high ROI Integrated intelligence for breakthrough experiences
- 56. Database + Big Data Information Management Machine Learning + Analytics Intelligence Dashboards + Visualizations