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Transforming Your Business with Fast Data – Five Use Case Examples


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Dheeraj Remella, Director of Solutions Architecture, VoltDB discusses how five customers transformed their business with VoltDB. Industries covered include ad tech data counting and tracking, content analytics for a global CDN, financial market data latency reduction and regulatory compliance, online game sessionization, player profiling and monetization and mobile network subscriber personalization, and ad delivery.

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Transforming Your Business with Fast Data – Five Use Case Examples

  2. 2. page© 2016 VoltDB OUR SPEAKER 2 Dheeraj Remella Dir. of Solutions Architecture, VoltDB
  3. 3. page© 2016 VoltDB 3 DATA-FICATION OF LIFE "Smartness can be embedded everywhere," said Professor Sangiovanni-Vincentelli, EE/CS at University of California at Berkeley. "The entire environment is going to be full of sensors of all kinds. Chemical sensors, cameras and microphones of all types and shapes. Sensors will check the quality of the air and temperatures. Microphones around your environment will listen to you giving commands.” Computerworld, September 2015 The 10 Trillion Device World
  4. 4. page© 2016 VoltDB FAST = ADVANTAGE 4
  5. 5. page© 2016 VoltDB 5 IN THE FUTURE…. All businesses will compete on their ability to make decisions “in the moment” using Fast Data.
  6. 6. page© 2016 VoltDB 6 Big Data “Perishable insights can have exponentially more value than after-the-fact traditional historical analytics.” Mike Gualtieri, Principal Analyst, Forrester Research Fast Data
  7. 7. page© 2016 VoltDB WHAT’S NEW HERE? 7 Analytics Action
  8. 8. page© 2016 VoltDB TRANSFORM YOUR BUSINESS TO FAST Use Cases 8 Interactive personalized, customer engagement Precision resource counting and billing Real-time policy enforcement IoT sensor data processing
  9. 9. page© 2016 VoltDB TRANSFORM YOUR BUSINESS TO FAST •  Fast is different than Big •  VoltDB is the industry’s only purpose- built architecture for fast data •  VoltDB has helped hundreds of developers build fast data apps 9
  10. 10. page© 2016 VoltDB VOLTDB: DESIGNED BY DATABASE ‘SUPER FRIENDS’ •  Fast Data Technology •  In-Memory operational DB + Data Pipeline •  Scale-out architecture, SQL + Java •  Designed by database ‘super friends’ •  Mike Stonebraker, MIT, 2014 Turing Award Winner •  Dan Abadi, Yale •  Sam Madden, MIT •  Andy Pavlo, CMU •  Stan Zdonik, Brown University 10
  11. 11. page© 2016 VoltDB DATA FLOWS 11 Pipeline Data Lake Ingest Real Time Analytics Export Using Real Time Data Request/ Response
  12. 12. page© 2016 VoltDB COUNTING / BALANCE KEEPING 12 •  High rate of ingestion •  Rapidly changing data •  Materialized Views •  Real-time Group By counts and sums
  13. 13. page© 2016 VoltDB COUNTING / BALANCE KEEPING 13 •  “Last Dollar” problem •  Accuracy is important: •  For billing •  To decide the right action
  14. 14. page© 2016 VoltDB AIRPUSH •  Pay for placement, bill for clicks •  Correlate clicks to ad budgets •  Ad campaign balance tracking •  Cost of overshooting budget: •  Lost revenue •  Opportunity cost •  Bidding cost (would have been a lower bid) •  Real-time analytics to support budget optimization •  Reduced infrastructure costs 14
  15. 15. page© 2016 VoltDB MaxCDN •  Content delivery network (CDN) •  Real-time analytics of content delivery •  Over 32 TB of daily web server log data •  300K log records per second •  Accurate billing •  Custom bulk-loading to batch records by partition •  Procedures load a batch of hundreds of records •  Ad-Hoc SQL queries •  Reduced infrastructure costs 15
  16. 16. page© 2016 VoltDB SESSIONIZATION / CORRELATION •  Sessions consist of segments or events •  Can arrive out of order •  While ingesting each event: •  Query to correlate with previous events in session •  Process when session is complete •  Known “last” event arrives •  Check for a complete set 16 Events Sessions
  17. 17. page© 2016 VoltDB Business model: convert from free to fee •  They needed to select a database that could •  Give consistent single figure millisecond response times for complex transactions •  Maintain in near real time potentially hundreds of transactionally consistent metrics for a user •  This is vital in the online game industry •  Games change on a weekly basis to sustain growth •  Massive numbers of players, creating billions of data points •  Real-time in game interaction to catch the player at exactly the right point 17 DELTADNA
  18. 18. page© 2016 VoltDB EVENT DETECTION •  Filtered events •  Aggregate events crossing thresholds •  Calendar events within range •  Geo-spatial entry or exit events •  Patterns of events •  A then B •  A then B then 3 or more C’s •  Threshold exceeded then 2 or more D’s •  Complexity leads to a Rules Engine 18 Raw Events Business Events
  19. 19. page© 2016 VoltDB PERSONALIZATION •  Personalized experiences •  Fast retrieval and intelligent use of user data •  Segment •  Preferences •  History •  Recommendations 19
  20. 20. page© 2016 VoltDB 20 REAL-TIME EVENT DECISIONING PLATFORM 20 Real-Time Alerting & Monitoring, Reporting, Analytics, Configuration, Auditing IN DWH HLR/ VLR Web CSR Social Filters Accumulators Detectors Pattern Recognition Rules Engine Offer Matching Business Rules Models Decisioning NBA Fulfilment Comms Channels Integration with Real-Time Real-Time Event Detection Real-Time Decisioning Fulfilment Event Decision Execute Network Elements Upstream Deliver the best interaction aligned to each individual customer in real-time to drive customer engagement & maximise business results
  21. 21. page© 2016 VoltDB 21 •  Ingest Mobile Phone usage events in real-time •  Detect marketing opportunity events •  First time use of a feature •  Plan usage thresholds: 50%, 75%, 90%, 95%, 100% •  Time left, or time since activation, first use, etc. •  Personalize offers by segment and history •  Pre-paid phones = much more customer engagement •  Measure success rate of offers •  A/B testing Emagine results show the power of real-time to be 1.5to 2.5times greater
  22. 22. page© 2016 VoltDB REAL-TIME CALCULATIONS •  Similar to Counting: •  High rate of ingestion •  Rapidly changing data •  Materialized Views •  Group By counts and sums •  Real-time •  Accurate •  More complex calculations •  Not just aggregation •  Stored procedures retrieve history to calculate and update values 22
  23. 23. page© 2016 VoltDB MAJOR FINANCIAL CUSTOMER •  National Best Bid and Offer (NBBO) •  The lowest available ask price and highest available bid price across all markets •  Upsert a table with the latest prices for each stock from each market, then query to find the best prices •  Volume-weighted average price (VWAP) •  SUM(price*quantity)/SUM(quantity) •  Time-weighted average price (TWAP) •  SUM(price*seconds at that price)/(elapsed seconds since market opened) 23
  24. 24. page© 2016 VoltDB •  Real-time Slippage •  Comparing trades & executions to the market •  Not getting the best available price •  Incurring market impact costs 24 MAJOR FINANCIAL CUSTOMER
  25. 25. page© 2016 VoltDB FAST DATA: A WIDE VARIETY OF USE CASES Mobile •  Subscriber engagement, personalization •  User session Management •  OSS/BSS – policy, billing, routing 25 Financial Services •  Risk management (portfolio, trading) •  Fraud detection •  Compliance (BB&O) •  Customer engagement Media and Entertainment •  Personalization •  Digital advertising •  Content delivery •  Gaming IoT/Sensors •  Smart Energy •  Connected Home •  Patient monitoring
  26. 26. page© 2016 VoltDB WHY POWER YOUR FAST DATA APPS WITH VOLTDB? Faster Simpler Better ü  Faster •  tps, response latency ü  Simpler •  Fraction of components and coding vs alternatives •  Easier to test, maintain and support ü  Better •  Correct results •  Lower overall system risk •  Higher availability and reliability 26
  27. 27. page© 2016 VoltDB RESOURCES Fast Data and the New Enterprise Data Architecture: Download VoltDB: 27