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Spark meetup - Zoomdata Streaming

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Zoomdata explains how it uses Spark for streaming interactive visualizations on big data.

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Spark meetup - Zoomdata Streaming

  1. 1. Interactive Visualization of Data powered by Spark
  2. 2. Streaming Data @ Zoomdata Visualizations react to new data delivered Users start, stop, pause the stream Users select a rolling window or pin a start time to capture cumulative metrics
  3. 3. Drivers for Streaming Data Data Freshness Time to Analytic Business Context
  4. 4. Challenges ● Time ● Frequency ● Retention ● Synchronization ● Order ● Updates
  5. 5. Addressing the Problem @ Zoomdata Historical Revised Receive Data JMS Kafka Manipulate Stream Single JVM in Memory Spark Streaming Hold Data in Buffer MongoDB Pluggable Interact with Data Custom Code Pluggable
  6. 6. Technology Cast ● The Stream - Kafka, Kinesis, JMS ● Processing Fabric - Spark Streaming ● Landing Area - MemSQL, Solr, Kudu, Others
  7. 7. How it looks
  8. 8. With the rest of the app
  9. 9. Scale Out
  10. 10. Benefits ● Contextual Expressiveness with Streaming Data ● Independent scalability (scale-up, scale-around) ● Expressiveness powered by Spark -- using Windowing (dataframe API with stream)
  11. 11. Side Benefits ● Separation of concerns ● Disaster Recovery, COOP, other Data management concerns ● Restatements ● Options!
  12. 12. Demo ● Twitter Producer ● Spark Streaming ● MemSQL & Solr Sinks
  13. 13. Future Work ● Cross Stream Synchronization & Fusion ● On-demand scale out and resource management via Mesos ● Schema Evolution ● Storage Tiering
  14. 14. Thanks For more information contact: ruhollah@zoomdata.com quan@zoomdata.com

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