Real Time Analytics at UBER Scale
James Burkhart explains how Uber supports millions of analytical queries daily across real-time data with Apollo. James covers the architectural decisions and lessons learned building an exactly-once ingest pipeline storing raw events across in-memory row storage and on-disk columnar storage and a custom metalanguage and query layer leveraging partial OLAP result set caching and query canonicalization. Putting all the pieces together provides thousands of Uber employees with subsecond p95 latency analytical queries spanning hundreds of millions of recent events.
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