Good post. The Snapshot-based approach during ingestion time was the part where I couldn't figure out why it was considered a good decision during implementatio

Good post. The Snapshot-based approach during ingestion time was the part where I couldn't figure out why it was considered a good decision during implementation? I've experimented with Parquet data on S3 for a work POC, and the latency to fetch the data/create tables/run the Spark-SQL query (running on EMR cluster) was quite noticeable. I was advised that EMR-FS would make it run quicker, but never got around to playing with that. But I guess the creating of in-memory tables using raw data snapshots would still remain true? Or maybe I missed something. Also, I take it if 24 hrs is the latency requirements for ingestion to availability of this data, obviously this isn't the data platform that is powering the real time booking/sharing of Uber rides. I'd be curious to see what is the data pipeline that powers that for Uber.