Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Converging Database Transactions and Analytics

734 views

Published on

delivered at the Gartner Data and Analytics 2018 show in Texas. This presentation discusses real-time applications and their impact on existing data infrastructures

Published in: Technology
  • Be the first to comment

Converging Database Transactions and Analytics

  1. 1. Converging Analytics and Transactions to Unlock Real-Time Opportunities by Mike Boyarski @boyarski @memsql
  2. 2. 2 People Respond to Superior Customer Experience
  3. 3. 3 Loyalty Revenue Insight People Respond to Superior Customer Experience And that drives …
  4. 4. 4 Real-Time Applications are shaping the customer experience
  5. 5. 5 They optimize the travel experience
  6. 6. 6 Enrich shopping experience
  7. 7. 7 Improve the banking experience
  8. 8. 8 And They Span Industries Financial Services - Fraud detection - Portfolio management - Risk management Media and Communications - Operational monitoring - Advertising analytics - Content personalization Energy and Utilities - Predictive maintenance - Supply chain optimization - Grid management Healthcare - Real-time patient care - Prescription management - Contract management
  9. 9. 9 What makes an application a “Real-Time Application”?
  10. 10. 10 Live Data Scalable Intelligent Operational
  11. 11. 11 Let’s review the impact of Real-Time Applications on existing data infrastructures
  12. 12. 12 Real-Time Applications demand: § Live Data Architecture § Scalability § Transaction and Analytics Convergence
  13. 13. 13 Live Data Architecture
  14. 14. 14 Traditional Data Architecture Data Warehouse Application Sources Data Integration Operational Data Store Application ETL
  15. 15. 15 Traditional Data Architecture Data Warehouse Application Sources Data Integration Operational Data Store Application ETL Batch data Integration processes !
  16. 16. 16 Traditional Data Architecture Data Warehouse Application Sources Data Integration Operational Data Store Application ETL ! Multiple specialized databases
  17. 17. 17 Traditional Data Architecture Data Warehouse Application Sources Data Integration Operational Data Store Application ETL ! Stale data due to architecture complexity
  18. 18. 18 Live Data Architecture Application Sources Data Integration Operational and Analytic Data Store Application Change Data Capture
  19. 19. 19 Live Data Architecture Application Sources Data Integration Operational and Analytic Data Store Application Change Data Capture Real-Time Data Synchronization a
  20. 20. 20 Live Data Architecture Application Sources Data Integration Operational and Analytic Data Store Application Change Data Capture Converged Operational Database and Data Warehouse a
  21. 21. 21 Live Data Architecture Application Sources Data Integration Operational and Analytic Data Store Application Change Data Capture Accurate live data view a
  22. 22. 22 Scalability
  23. 23. Planned Concurrency Large Scale Concurrency Scale Up Scale Out
  24. 24. MemSQL scales across nodes As a distributed database, MemSQL scales out with industry standard servers or cloud instances
  25. 25. 25 Transactions and Analytics Convergence
  26. 26. 26 We started with transactions
  27. 27. 27 Then did analytics and transactions
  28. 28. 28 So we separated the two
  29. 29. 29 But this introduces complexity and drag
  30. 30. 30 Convergence drives simplicity and speed
  31. 31. 31 Operational - ACID Transactions - Fast data ingestion - ANSI SQL - Secure Convergence drives simplicity and speed
  32. 32. 32 Analytic - Fast queries - Large concurrency - Petabyte scale Convergence drives simplicity and speed Operational - ACID Transactions - Fast data ingestion - ANSI SQL - Secure
  33. 33. MemSQL Unified Architecture 33 Historical Data Disk-optimized tables with compression for fast analytic queries Live Data Memory optimized tables for analyzing real-time events Streaming Ingest Real-time data pipelines with exactly-once semantics
  34. 34. MemSQL: The Database for Real-Time Applications Converging Transactions and Analytics at Scale 34 Run Anywhere Any cloud, hybrid, or multicloud On-premises Low cost standard hardware Scale Transactions and Analytics Petabyte scale In-memory and disk-based Unified mixed workload architecture Power Real-Time Applications Fast ingestion and queries Operational capabilities Multi-model and data support
  35. 35. 35 Let’s Show a Demo!
  36. 36. 36 Stop by the MemSQL booth (#212), enter to win a Google Home!

×