Inside the World’s Best Data Architectures: Netflix, Facebook, Airbnb, Pinterest
This article explores the cutting‑edge data pipelines of Netflix, Facebook, Airbnb and Pinterest, detailing the massive event volumes they handle, the core technologies such as Kafka, Spark, Presto and Hadoop, and how these giants design scalable, real‑time analytics infrastructures.
Netflix
Despite having 93 million monthly active users, Netflix records roughly 5 trillion events per day (about 1.3 PB), peaking at 8 million events per second, with over 100 data engineers and analysts.
Their data stack includes Apache Kafka, Elasticsearch, AWS S3, Apache Spark, Apache Hadoop, and EMR.
With over 1 billion active users, Facebook stores more than 300 PB of data, supporting batch processing, image analysis, machine learning and real‑time interactive analytics.
To enable massive interactive queries, Facebook built Presto, a custom distributed SQL engine optimized for analytics, used daily by thousands of engineers across back‑ends such as Hive, HBase and Scribe.
Airbnb
Airbnb serves over 100 million users querying 2 million property records, and provides intelligent travel recommendations. Their data team of more than 30 engineers receives an annual salary budget exceeding $5 million.
Pinterest handles over 100 million monthly active users and 10 billion page views per month. In 2015 its data engineering team grew to more than 250 engineers. Their stack heavily relies on Apache Kafka, Storm, Hadoop, HBase and Redshift.
Pinterest’s engineering blog describes how they use Kafka, AWS S3 and HBase to build a comprehensive analytics system for advertisers.
Twitter / Crashlytics
Crashlytics Answers processes millions of mobile device events daily with a dedicated architecture.
Key components include an event receiver, archiving layer, batch processing, high‑speed computation, and combined view generation.
Acknowledgments
Thanks to the data engineering community for continuous innovation and open‑source contributions. Special thanks to the authors and architects: Netflix’s Steven Wu, Facebook Presto’s Martin Traverso, AirbnbEng, Pinterest Engineering, and Crashlytics Answers’ Ed Solovey.
Original article: https://blog.keen.io/architecture-of-giants-data-stacks-at-facebook-netflix-airbnb-and-pinterest-9b7cd881af54
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