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In-Memory Computing Webcast. Market Predictions 2017


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Jason Stamper, 451 Research and Gary Orenstein, MemSQL

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In-Memory Computing Webcast. Market Predictions 2017

  1. 1. + Jason Stamper and Gary Orenstein IN-MEMORY COMPUTING WEBCAST MARKET PREDICTIONS 2017 1
  2. 2. Market Predictions 2017: In-Memory Computing Jason Stamper, Analyst, 451 Research @jasonstamper
  3. 3. 451 Research is an information technology research & advisory company Founded in 2000 210+ employees, including over 100 analysts 1,000+ clients: Technology & Service providers, corporate advisory, finance, professional services, and IT decision makers 10,000+ senior IT professionals in our research community Over 52 million data points each quarter 4,500+ reports published each year covering 2,000+ innovative technology & service providers Headquartered in New York City with offices in London, Boston, San Francisco, and Washington D.C. 451 Research and its sister company Uptime Institute comprise the two divisions of The 451 Group Research & Data Advisory Services Events 3
  4. 4. 4 451 Research’s view of the ‘Total Data’ Model
  5. 5. 5 Growth? 2015 2016 2017 2018 2019 2020 $69,612 $79,612 $90,777 $103,126 $116,796 $132,049 14% 2015-20 CAGR Source: 451 Research, Forecast: Total Data 2016 - Data Platforms and Analytics Market Sizing and Forecasts. 284 Vendors included in this analysis 9 Market segments included
  6. 6. Defining the in-memory database “An in-memory database (IMDB; also main memory database system or MMDB or memory resident database) is a database management system that primarily relies on main memory for computer data storage. It is contrasted with database management systems that employ a disk storage mechanism. Main memory databases are faster than disk-optimized databases because the disk access is slower than memory access, the internal optimization algorithms are simpler and execute fewer CPU instructions. Accessing data in memory eliminates seek time when querying the data, which provides faster and more predictable performance than disk. Applications where response time is critical, such as those running telecommunications network equipment and mobile advertising networks, often use main-memory databases. IMDBs have gained a lot of traction, especially in the data analytics space, starting in the mid- 2000s - mainly due to multi-core processors that can address large memory and due to less expensive RAM.” - Wikipedia 6
  7. 7. 7 In-memory systems come in several guises  Pure in-memory database  Disk-based/persistent database with an in-memory ‘option’ or column store  In-memory data grid or fabric
  8. 8. What’s driving in-memory adoption? • Increasing # of users & transactions • More data! • Growing # of writes • Insufficient capacity • Declining throughput • Performance inconsistencies • Cost of ETL processes 8
  9. 9. How are different data platform and analytic technologies shaping up? $120,000 $100,000 $80,000 $60,000 $40,000 $20,000 $0 2015 2016 2017 2018 2019 2020 Event/Stream Processing Data Management Distributed Data Grid/Cache Analytic Database Search Performance Management Reporting and Analytics Hadoop Operational Databases $140,000 9
  10. 10. Some questions to ask  Will my data fit in memory?!  Is the platform optimized for analytics, transactions or both?  Is the platform durable (ACID compliant)? What about restores?  What is the programming model – does it support SQL?  Is there an open source or community edition?  Does it support production requirements such as high availability, cross data center replication, granular user permissions, and SSL?  Can I run it in the cloud, on-premises or both? 10
  11. 11. Some potential solutions, and their pros and cons • In-memory ‘options’ added to existing relational databases • Pure in-memory databases • Data streaming offerings • Analytics as a service or database as a service – on- prem/hybrid/cloud • In memory data grid/cache 11
  12. 12. Some predictions for 2016/7 • Multi-modal databases – that can handle both transactions (OLTP) and analytics (OLAP) become the norm (with in-memory being a key enabler) • In-memory databases continue to grow – both pure and ‘hybrid’ • E-commerce, ad-tech, gaming, financial services, high tech grow in-memory use particularly fast, but all businesses waking up to ‘latency sensitivity’ • From a slow start, the Internet of Things (IoT) gathers pace, thanks to both demand, and the ability to do something about it • A new President of the US, and Andy Murray to become world #1 12
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  14. 14. Architecting with In-Memory Gary Orenstein
  15. 15. The nature of transactions has changed. 1515
  16. 16. 16 Traditional Transactions Modern Transactions BATCH • Exactly-Once • Governed • Structured • ERP/CRM Applications REAL TIME • Duplicates • Optional auditing • Unstructured • Social and Sensor feeds
  17. 17. Modern Transactions REAL TIME • Duplicates • Optional auditing • Unstructured • Social and Sensor feeds 17 Traditional Transactions BATCH • Exactly-Once • Governed • Structured • ERP/CRM Applications Converged Transactions REAL TIME • Exactly-Once • Governed • Any Structure • All Sources
  18. 18. A Real-Time Data Platform Processing real-time and batch data to maximize traditional and modern transactions 18
  19. 19. Architecting A Real-Time Data Platform Database Workloads Data Warehouse Workloads Real-Time Streaming 19
  20. 20. 20 Architecting A Real-Time Data Platform Orchestration / Containers Cloud / On-Premises Platform MessagingInputs Real-Time Applications Business Intelligence Dashboards Relational Key-Value Document Geospatial Existing Data Stores Database Workloads Data Warehouse Workloads Real-Time Streaming Hadoop Amazon S3MySQL Transformation
  21. 21. 21 A Deeper Look at Two Industry Sectors Customer 360 Supply Chain Logistics
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  23. 23. 23 DellEMC Adopts MemSQL for Customer 360 Application +
  24. 24. 24 The Need for “CALM” Customer Asset Lifecycle Management For enterprise sales Who need accurate and timely customer information CALM is a real-time application Providing up to the moment customer 360 dashboards For enterprise sales Who need accurate and timely customer information CALM is a real-time application Providing up to the moment customer 360 o dashboards Install Base Pricing Device Config Contacts Contracts Analytics Contracts Component Data Offers Scorecard Source: DellEMC
  25. 25. 25 Data Lake Architecture D A T A P L A T F O R M V M W A R E V C L O U D S U I T E E X E C U T I O N P R O C E S S GREENPLUM DBSPRING XD PIVOTAL HD Gemfire H A D O O P INGESTION DATAGOVERNANCE Cassandra PostgreSQL MemSQL HDFS ON ISILON HADOOP ON SCALEIO VCE VBLOCK/VxRACK | XTREMIO | DATA DOMAIN A N A L Y T I C S T O O L B O X Network WebSensor SupplierSocial Media Market S T R U C T U R E DU N S T R U C T U R E D CRM PLMERP APPLICATIONS ApacheRangerAttivioCollibra Real-TimeMicro-BatchBatch Source: DellEMC
  26. 26. BUSINESS BENEFITS  Deliver real-time customer updates to EMC Sales Department  Scale customer analytics to increase sales productivity  Drive real-time personalization for operational efficiency TECHNICAL ACHIEVEMENT  Execute joins across multiple distributed data sets 2626
  27. 27. The Pace of Supply Chain Success 27 Amazon 30 minute post-order drone deliveries FedEx 317 million packages shipped over Xmas with real-time tracking Uber Freight Matching shippers with trucks in real time
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  29. 29. | MemEx An IoT showcase application that powers supply chain monitoring and management through predictive analytics.
  30. 30. 30 TECHNICAL BENEFITS  Processes 2 million data points, based on 2,000 sensors across 1,000 warehouses  Two million reads and writes per second  Combines MemSQL, Apache Kafka, and Streamliner with machine learning, IoT sensors, and predictive analytics  Enables enterprises to predict throughput of supply warehouses | MemEx 30
  31. 31. Data Producers (simulating sensor activity) Raw Sensor 1 + Predictive Score 1 S1 P1 1 | MemEx Real-Time Scoring MemEx UI 32
  32. 32. 33 THANK YOU! Follow our speakers on Twitter @jasonstamper @garyorenstein Try MemSQL today at