Evan Chan Interview
summary
Built FiloDB and Spark Job Server, and describes a columnar real‑time distributed query engine using Storm, Spark, Kafka, Cassandra and Scala/Akka. Scaling lessons are concrete: things that work on one machine break at each cluster step, ZooKeeper deployment patterns change behavior, and data velocity forces continuous tuning. Predicts more modular cloud architectures and functionized components. Highlights reactive streams for backpressure and Akka for supervision and flexible topologies, no magic—just engineering tradeoffs.
AI-written summary · about
chapters (5)
related talks
25mSPARK AND AKKA FOR BIG DATA SYSTEMS, IN PRACTICE
02h 18mDistributed Systems: In the Daily Life of the Data Engineer
52mDistributed Computing Dealing with Time and Failure in the Wild
19mKeynote Panel Discussion: SMACK in the Enterprise
57mMulti Tenant NoSQL and NewSQL Cloud data Design Patterns Deep dive
37mStorm, a Distributed Real-time Data Processing System
30mEvent Streaming and the Future of Applications
25mGo distributed (and scale out) with Actors and Akka Clustering