Every real-time OLAP benchmark you've seen was run by the company that makes the database. Here's what actually separates ClickHouse, Apache Druid, and Apache Pinot once they're in production.

All three promise sub-second queries over billions of rows, and for most workloads all three deliver. What you're really picking is a set of operational problems you'll live with. I break down how each system stores and queries data underneath, put them head to head on ingestion, query latency, operational burden, updates, and joins, then give you a flow chart for choosing. Fair warning up front: I've run ClickHouse in production with a pager attached, but I've only evaluated Druid and Pinot, so weight my ClickHouse takes accordingly.

⏱️ Chapters
0:00 - The benchmark problem nobody mentions
1:37 - What real-time OLAP actually means
2:52 - ClickHouse: MergeTree and the sparse index
4:30 - Druid: pay at ingestion with rollup
6:13 - Pinot: star-tree pre-aggregation
7:32 - The real fork: where pre-computation starts
8:46 - Head to head on five dimensions
11:12 - The flow chart: what to start with
12:32 - Honest limits

🔗 Links
ClickHouse: https://clickhouse.com
Apache Druid: https://druid.apache.org
Apache Pinot: https://pinot.apache.org


#ClickHouse #ApacheDruid #ApachePinot #realtimeOLAP #OLAP #dataengineering #analyticsdatabase #MergeTree #startree #userfacinganalytics #columnardatabase #datainfrastructure #Kafka #subsecondqueries #databasecomparison