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A side-by-side editorial comparison of Apache Druid and Apache Pinot — release velocity, themes, recent moves, and the top alternatives to consider.
| Feature | Apache Druid | Apache Pinot |
|---|---|---|
| Sector | Analytics, Infra & APIs | Infra & APIs, Analytics |
| Velocity score | 0.0 | 6.3 |
| Sparks · 30d | 0 | 1 |
| Top themes | real-time-analytics, apache-project, java-upgrade, operator-tooling | real-time-olap, sql-ddl, vector-search, materialized-views |
| Last editorial update | 2h ago | 59m ago |
| Website | Visit → | Visit → |
Druid 38 drops Java 17, recommends Java 25 and took longer to arrive than its predecessors
Druid 38.0.0 shipped on October 1 with over 250 changes from 37 contributors. It adds Java 25 support, drops Java 17, and introduces Historical tier aliases and a realtimeSegmentsMode query context that replaces realtimeSegmentsOnly. MSQ workers now fetch input files in the background by default, and Druid 38 is the last major that reads legacy ZSTD frames.
Pinot 1.6 adds SQL DDL and vector backends, and now needs Java 25 to run its services.
1.6.0 on 29 October lets operators manage tables through CREATE, DROP and SHOW statements, gives materialized views DDL and single-stage query rewrite, and adds GROUPING SETS, ROLLUP and CUBE to both query engines. Vector search gains IVF_FLAT, IVF_PQ and IVF_ON_DISK backends alongside HNSW. The release also removes legacy pieces: native text and FST indexes, Spark 2 and Presto integrations, and APIs deprecated through 1.3.0.
Druid 38.0.0 shipped on October 1 with over 250 changes from 37 contributors. It adds Java 25 support, drops Java 17, and introduces Historical tier aliases and a realtimeSegmentsMode query context that replaces realtimeSegmentsOnly. MSQ workers now fetch input files in the background by default, and Druid 38 is the last major that reads legacy ZSTD frames.
The release is operator-focused: tier aliases and segment-mode controls target blue/green deployments and large clusters, and background fetching and Dart prefetching chase throughput. Compatibility remains a stated part of shipping, with explicit deprecations and upgrade notes. The gap from 37.0.0 was nearly five months, against about three for the release before, so the quarterly rhythm has loosened.
A 39 release candidate is the next visible step, and removal of legacy ZSTD frame support is already announced for a future release. Timing is less certain now that the last gap ran past a quarter.
1.6.0 on 29 October lets operators manage tables through CREATE, DROP and SHOW statements, gives materialized views DDL and single-stage query rewrite, and adds GROUPING SETS, ROLLUP and CUBE to both query engines. Vector search gains IVF_FLAT, IVF_PQ and IVF_ON_DISK backends alongside HNSW. The release also removes legacy pieces: native text and FST indexes, Spark 2 and Presto integrations, and APIs deprecated through 1.3.0.
Pinot is moving toward being operated like a SQL database. The multi-stage engine's SQL coverage has grown release by release, and table lifecycle now joins it, while vector backends pull retrieval workloads into the same store. The upgrade cost is real: Java 25 for services, Scala 2.13 for Spark artifacts, and mixed-version limits around SMART aggregation. Federation, the headline of 1.5, doesn't feature in the 1.6 highlights.
Expect materialized-view rewrite to reach the multi-stage engine, since it is single-stage only today. A security patch on 1.6 would match what followed 1.5.0.
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Tap any card for the full editorial trajectory or compare directly with Apache Druid.
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Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Tap any card for the full editorial trajectory or compare directly with Apache Pinot.
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Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — apache-project — within Analytics. Apache Pinot is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. Apache Pinot is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top Apache Druid alternatives in Analytics are ranked by recent ship velocity. Browse the "Apache Druid alternatives" section above for the current picks, or visit /alternatives/apache-druid for the full list with editorial commentary on each.
Top Apache Pinot alternatives in Analytics are ranked by recent ship velocity. Browse the "Apache Pinot alternatives" section above for the current picks, or visit /alternatives/apache-pinot for the full list with editorial commentary on each.