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Deequ vs Apache Storm
A side-by-side editorial comparison of Deequ and Apache Storm — release velocity, themes, recent moves, and the top alternatives to consider.
Deequ vs Apache Storm: at a glance
What is Deequ?
Deequ ships GitHub tags whose release notes are one commit message long
Deequ is a data quality library for Spark, and it releases per Spark version — tags carry a -spark-3.5 or -spark-4.0 suffix, so the same code line ships more than once. The release notes are whatever the last commit message said, which here means each entry is a single line. Four releases landed in a month between March and April 2026, and only two of them contain any product change: a Range analyzer with DQDL rule support, and a processRowsTyped API for typed outcome access.
What is Apache Storm?
Storm 3.0 finishes removing the Clojure it was built in, and moves to a Java 21 baseline.
Storm is running two lines: a 2.8.x maintenance branch that is mostly Dependabot traffic punctuated by real security releases, and the new 3.0.0 line cut on 22 July. 3.0.0 removes all remaining Clojure from the codebase, raises the baseline to Java 21 (with master already on 25), and ships throughput work — zstd compression for thrift cluster state, tuple compression between workers, and decoupling of the control plane from the data plane on receive queues. The 2.8.6 and 2.8.7 releases earlier in the window carried four CVEs, including a deserialization RCE reachable by any user with topology submission rights.
Deequ vs Apache Storm: editorial side-by-side
Deequ
0.0ANALYTICS
Deequ ships GitHub tags whose release notes are one commit message long
◆ Current state
Deequ is a data quality library for Spark, and it releases per Spark version — tags carry a -spark-3.5 or -spark-4.0 suffix, so the same code line ships more than once. The release notes are whatever the last commit message said, which here means each entry is a single line. Four releases landed in a month between March and April 2026, and only two of them contain any product change: a Range analyzer with DQDL rule support, and a processRowsTyped API for typed outcome access.
◆ Where it's heading
The visible work points in one direction — making check results programmatically consumable rather than just readable. A typed outcome API and a rule language binding are what you build when Deequ is being called from a pipeline that reacts to the result, not from a notebook where a human reads it. The column-pruning override added alongside the Range analyzer suggests the same attention on the cost side, keeping analyzers from scanning columns they do not reference.
◆ Prediction
The entries are too thin to support a confident read of what comes next; the only clear pattern is that each change will ship separately against Spark 3.5 and Spark 4.0, so the version skew between those branches will keep widening.
Apache Storm
6.3ANALYTICS
Storm 3.0 finishes removing the Clojure it was built in, and moves to a Java 21 baseline.
◆ Current state
Storm is running two lines: a 2.8.x maintenance branch that is mostly Dependabot traffic punctuated by real security releases, and the new 3.0.0 line cut on 22 July. 3.0.0 removes all remaining Clojure from the codebase, raises the baseline to Java 21 (with master already on 25), and ships throughput work — zstd compression for thrift cluster state, tuple compression between workers, and decoupling of the control plane from the data plane on receive queues. The 2.8.6 and 2.8.7 releases earlier in the window carried four CVEs, including a deserialization RCE reachable by any user with topology submission rights.
◆ Where it's heading
The project is converting itself from a legacy JVM codebase into an ordinary modern Java one, and the 3.0 work shows where that energy goes next: scheduling and queueing. Recent PRs add AIMD dynamic batch sizing to JCQueue, jitter metrics and a jitter-aware stream grouping, round-robin rebalance onto returning supervisors, and several fixes for stale or orphaned worker heartbeats. Alongside that, the distribution is being slimmed — optional Hadoop and Kafka dependencies were unbundled and shared jars de-duplicated. The 2.x branch is being kept alive for security and dependency currency, not for features.
◆ Prediction
Expect 3.0.x point releases to concentrate on the scheduler and worker-lifecycle fixes that 3.0.0 opened up, and expect the 2.8.x line to keep receiving CVE backports while feature work stays on 3.x. The Java 25 baseline already on master suggests the next minor will move the floor again.
Alternatives to Deequ and Apache Storm
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either Deequ or Apache Storm.
OpenDataDiscovery
Four ODD Platform releases in two weeks, and not one of them changes the product
Apache Baremaps
Baremaps got geoparquet and hillshading, then went quiet for eighteen months in incubation
Marquez
Marquez spent 2024 turning a lineage store into a UI, then stopped releasing
Amundsen
Amundsen's last release was a config flag, and the feed has been silent for two years
OpenHouse
LinkedIn's Iceberg control plane, shipping one pull request per release.
See all Deequ alternatives → · See all Apache Storm alternatives →
Recent activity from Deequ and Apache Storm
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 19d agoApache StormStorm 3.0 drops Clojure entirely and moves to Java 21 ⚡
- 19d agoApache Storm2.8.9 is a dependency sweep with one Flux viewer guard
- 19d agoApache Storm2.8.8 backports a Kafka topology-lag fix
- 3mo agoDeequDeequ adds a processRowsTyped API for typed outcome access
- 3mo agoDeequDeequ 3.0.1 fixes the publish workflow branch
- 3mo agoDeequDeequ 3.0.0 adds a Range analyzer with DQDL rule support
- 3mo agoApache StormTwo TLS CVEs fixed: JVM-wide downgrade and auth bypass
- 3mo agoApache StormDeserialization RCE and stored XSS in the UI are fixed
- 4mo agoDeequDeequ 2.0.15 tag carries only a pom version bump
- 4mo agoApache Storm2.8.5 is dependency upgrades plus small logging fixes
Frequently asked questions
What is the difference between Deequ and Apache Storm?
They serve adjacent needs but don't currently overlap on shipped themes. Apache Storm 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.
Is Deequ better than Apache Storm?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. Apache Storm 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.
What are the best alternatives to Deequ?
Top Deequ alternatives in Analytics are ranked by recent ship velocity. Browse the "Deequ alternatives" section above for the current picks, or visit /alternatives/deequ for the full list with editorial commentary on each.
What are the best alternatives to Apache Storm?
Top Apache Storm alternatives in Analytics are ranked by recent ship velocity. Browse the "Apache Storm alternatives" section above for the current picks, or visit /alternatives/storm for the full list with editorial commentary on each.