Pathikrit Bhowmick Interview
summary
Coatue Management runs Scala and Spark pipelines on multi‑billion‑row datasets and faces severe skew where some nodes overload and others sit idle, breaking even basic groupBy operations. Moving from tens of billions to hundreds of billions prompts a shift from batch to incremental processing to enable fast iteration. Code follows functional, immutable patterns so a small team can reason about systems; the claim is that immutability reduces logic bugs while operational data misunderstandings remain.
AI-written summary · about
chapters (5)
related talks
05mInterview
40mProductionizing Big Data - Stories from the Trenches
42mBuilding a Company on Scala
04minterview
38mScaling Scala
47mKeeping the “fun” in Apache Spark Datasets and FP
01h 12mBig Data in Java World: Apache Spark and Scala. Arch and live coding
45mScala and the JVM as a Big Data Platform Lessons from Apache Spark
