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Managing Smart Meter with DataStax DSE

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By Satish Ramjee Principle Software Engineer - First Utility

Published in: Technology
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Managing Smart Meter with DataStax DSE

  1. 1. Satish Ramjee Principal Software Engineer First Utility June 2017 Managing Smart Meter Data with DataStax DSE
  2. 2. • Who are First Utility • Creating a high availability platform • Enabling customer control and insights through analytics • Providing a self healing platform with event history • Monitoring and alerting across the platform • Conclusion Contents
  3. 3. Who are First Utility
  4. 4. First Utility ● Largest independent energy supplier ● Supply almost 1 million UK homes ● One of the first to provide Smart meters ● Provide broadband and home services as well ● Technology driven company
  5. 5. Creating a high availability platform
  6. 6. Creating a high availability platform ● National smart meter rollout ○ Aim is to have all 53 million residential premises in England, Wales, Scotland with Smart Meters by 2020 ● Energy suppliers are required to offer smart meters to all their customers ● Our immediate need to manage a high volume of meter reads with high availability ● Meter access devices can potentially provide data every 5- 10 seconds from the total meter estate
  7. 7. Creating a high availability platform ● Smart reads ideal for an event sourced type database ● Saw the need to add resilience, high volume and linear scaling capability into our key information base ● Opportunity to manage business expectation through service layers ● Providing a meter agnostic business layer “One of the things DataStax offered to us was the ability to really compress that learning curve. So we had some of their key experts come in and spend a few weeks with us over a period of time and provide that technical leadership” Bill Wilkins, CIO, First Utility. http://diginomica.com/2017/02/02/first-utility-makes-smart-meters-even-smarter-datastax-platform/
  8. 8. Creating a high availability platform store From Monolithic to Microservices Architecture Application Server store application (instance 1) application (instance 1) application (instance 1) Docker Container application (instance 2) Application Server Docker Container application (instance 1) application (instance 2) single node rdms multiple node distributed store
  9. 9. Creating a high availability platform Meter Type 2 Meter Type 1 Customer Insights Billing Industry Data Warehouse Meter Type n spark Usage View Billing View Industry View Data Stores spark Meter Data 1 Meter Data 2 Meter Data 3 spark
  10. 10. Creating a high availability platform ● Multiple DCs ● Appropriate RF ● DCs split between Business Critical access and Analytics
  11. 11. Enabling Customer Control and Insights
  12. 12. Enabling Customer Control and Insights ● Provide customers with an insightful view ● Help customers better manage their usage ● My energy comparison of usage ● Allow control of read submission frequency ● In home display reflects current tariff and usage
  13. 13. Enabling Customer Control and Insights Meter Customer Insights Meter Preferences Meter Data Usage Viewread preference/price TariffIHD Data Storage read
  14. 14. Enabling Customer Control and Insights
  15. 15. Enabling Customer Control and Insights
  16. 16. Enabling Customer Control and Insights
  17. 17. Providing a self healing platform
  18. 18. Providing a self healing platform ● Record commands sent to the meter ● Detect failures ● Replay commands (with retry limit) Meter commands meter state reconcile replay spark Event command response
  19. 19. Monitoring and alerting across the platform
  20. 20. Monitoring and alerting across the platform ● Eyes and ears ● Dashboards of platform behaviour ● Operational: is the platform working ● Business information: reporting and analytics
  21. 21. Monitoring and alerting across the platform
  22. 22. Conclusion
  23. 23. Conclusion ● Gone from read only system to ability control of meter (read/write) ● Self healing ● Real time monitoring and alerting ● Enduring solution can scale both processing and persistence ● Lessons learnt ○ Denormalise - many copies with different PK ○ Know your queries in advance ○ Reporting and aggregation - duplicate, spark or counters
  24. 24. That’s all folks

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