Dremel: How Google Made Trillion-Row Queries Feel Interactive
The columnar storage and execution-tree design behind Google's Dremel engine, and how it became the foundation for BigQuery.
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The columnar storage and execution-tree design behind Google's Dremel engine, and how it became the foundation for BigQuery.
How to decide when features need real-time streaming computation versus a scheduled batch job, and how to keep the two consistent with each other.
How Spotify's event pipeline evolved from batch log collection off servers to a real-time, cloud-native Pub/Sub system serving hundreds of consuming teams.
Why Airbnb kept getting conflicting numbers for the same metric across teams, and how Minerva's centralized definitions and certification process fixed it.
How Netflix built Keystone, its Kafka- and Flink-based real-time data platform, and why it later migrated the processing layer from Samza to Flink.
How DoorDash built a unified, Kafka-based event pipeline named Iguazu to feed analytics and machine learning from a sprawling microservices fleet.
How Spotify combines collaborative filtering, text analysis, and audio features in a weekly batch pipeline to power Discover Weekly for every user.
Why cron and ad hoc scripts stopped working for Airbnb's data pipelines, and how the internal tool built to fix it became the industry-standard workflow scheduler.
How Google's 2004 MapReduce paper turned a painful distributed-systems problem into a simple programming model — and accidentally created Hadoop.