Omni
Omni ships weekly, and almost every week the headline item is an AI feature
A side-by-side editorial comparison of Amundsen and Deequ — release velocity, themes, recent moves, and the top alternatives to consider.
Amundsen's last release was a config flag, and the feed has been silent for two years
Amundsen ships as several separately versioned components — databuilder, metadata, frontend, common — cut from one repository, and the release feed shows the same changelog republished under three different component tags on the same afternoon. Reading past the tag names, the content is overwhelmingly dependabot bumps, Python compatibility-matrix maintenance and mypy upgrades, with real features appearing a couple of times a year. The final release, databuilder 7.5.1 in August 2024, contains exactly one change: a config option for implicit transactions in the neo4j extractor.
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.
Amundsen ships as several separately versioned components — databuilder, metadata, frontend, common — cut from one repository, and the release feed shows the same changelog republished under three different component tags on the same afternoon. Reading past the tag names, the content is overwhelmingly dependabot bumps, Python compatibility-matrix maintenance and mypy upgrades, with real features appearing a couple of times a year. The final release, databuilder 7.5.1 in August 2024, contains exactly one change: a config option for implicit transactions in the neo4j extractor.
The direction is contraction. Python 3.7 support was dropped and the matrix narrowed to 3.8 and 3.9 before 3.10 was cautiously added to everything except the metadata service, contributor names moved to emeritus status, and organisations were removed from the README's adopter list. What feature work exists is small and peripheral — a PowerBI logo, aggregated alerts, a gremlin proxy method — rather than anything touching how the catalog works. Nothing has been published since August 2024.
There is no signal in these entries of planned work, and a two-year silence following a single-flag release means the practical expectation is no further releases; the component version skew across databuilder, metadata and frontend is now frozen where it stopped.
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.
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.
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.
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 Amundsen or Deequ.
Omni ships weekly, and almost every week the headline item is an AI feature
Four ODD Platform releases in two weeks, and not one of them changes the product
Baremaps got geoparquet and hillshading, then went quiet for eighteen months in incubation
Marquez spent 2024 turning a lineage store into a UI, then stopped releasing
LinkedIn's Iceberg control plane, shipping one pull request per release.
Google's solver suite where nearly every release note is really about CP-SAT.
See all Amundsen alternatives → · See all Deequ alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. Amundsen and Deequ are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). 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. Amundsen and Deequ are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top Amundsen alternatives in Analytics are ranked by recent ship velocity. Browse the "Amundsen alternatives" section above for the current picks, or visit /alternatives/amundsen for the full list with editorial commentary on each.
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.