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Building the Ideal Stack for Real-Time Analytics

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Tools, techniques, and use cases for integrating real-time analytics across your organization.

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Building the Ideal Stack for Real-Time Analytics

  1. 1. Building the Ideal Stack for Real-Time Analytics Steven Camiña, Principal Product Manager
  2. 2. MISSION Make every company a real-time enterprise PRODUCT Database Platform: Real-Time | Scalable | Proven Ranked #1 Operational Data Warehouse ABOUT Founders are former Facebook, SQL Server database engineers YC alumni with $85m in funding from top tier investors MemSQL at a Glance
  3. 3. REAL-TIME ANALYTICS Accessing data patterns that provide meaning to a business as they come into a system.
  4. 4. Today’s Real-Time Discussion: Web Logs, Mobile Apps, IoT
  5. 5. Today’s Real-Time Discussion: Web Logs, Mobile Apps, IoT Huge Opportunity Take Existing Batch Processes Real-Time
  6. 6. Other Drive new revenue opportunities Increase efficiency and reduce cost Power new applications (Example: IoT data processing) Improve customer experience Deliver real-time dashboards 52% 52% 43% 36% 29% 4% Why are you pursuing real-time analytics? 6 MemSQL State of Real Time Survey 2016
  7. 7. 7 The Ideal Real-Time Stack Message Queue Transformation Tier Data Persistence Real-Time Visualization
  8. 8. 8 Real-Time Visualization
  9. 9. 9 Message Queue
  10. 10. 10 Transformation Tier
  11. 11. 11 Data Persistence SSD In-Memory and Solid State Distributed Architecture Data Source Connectivity Flexible Deployment SQL Scalable SQL
  12. 12. 12 The Ideal Real-Time Stack Message Queue Transformation Tier Data Persistence Real-Time Visualization
  13. 13. A massively scalable database and ingest solution allowed for massive growth, real-time analytic applications and faster, targeted. + MemSQL Confidential13
  14. 14. 14 TECHNICAL BENEFITS § Instant accuracy to the latest re-pin § 1 GB/sec totaling 72 TB/day THE PINTEREST REAL-TIME ARCHITECTURE REAL-TIME ANALYTICS
  15. 15. Real-time drilling sensor data to manage the high stakes of producing oil in a depressed market and maximizing productivity. + 15 Energy Company
  16. 16. 16 TECHNICAL BENEFITS § Enabled machine learning scoring of streaming data for real- time Predictive Analytics § Integrated SAS BI PMML for deep analytics § Joined multiple data types and third party sources including geospatial and weather data Energy Company
  17. 17. 17 REAL-TIME INPUTS Spark MLlib Predictive Model Raw Sensor 1 + Predictive Score 1 S1 P1 1 BUSINESS LOGIC Energy Company
  18. 18. DEMO
  19. 19. New O’REILLY BOOK: The Path to Predictive Analytics and Machine Learning Booth #308 or memsql.com/oreillyml 19
  20. 20. Thank You! Try MemSQL: memsql.com/download Contact: info@memsql.com Twitter: @memsql

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