OpenDataDiscovery
Four ODD Platform releases in two weeks, and not one of them changes the product
A side-by-side editorial comparison of Apache Superset and Omni — release velocity, themes, recent moves, and the top alternatives to consider.
Superset's feed tracks Helm chart tags, so the BI product itself stays invisible here.
Every entry in this feed is a Helm chart tag from the apache/superset repository, carrying no notes beyond a one-line project blurb. The chart has moved from 0.19.0 to 0.22.5 in about six weeks, a packaging cadence rather than an application one. Nothing here describes a change to Superset's dashboards, SQL Lab, or semantic layer.
Omni ships weekly, and almost every week the headline item is an AI feature
Omni publishes a dated weekly digest whose body is a single line listing that week's items, so each entry compresses several releases into a sentence. Across seven consecutive weeks the pattern is unmistakable: AI-powered semantic model generation reaching general availability, AI Routines creatable from chat and deliverable to Slack, AI model suggestion endpoints, AI credit controls scoped to embed entity groups and individual users, AI Evals support on Azure, and MCP surfaces appearing both in-app and as a searchDashboards tool. The non-AI items are steady BI plumbing — OAuth and GitHub App authentication for dbt connections, mobile dashboard settings, map legend positioning, presentation mode.
Every entry in this feed is a Helm chart tag from the apache/superset repository, carrying no notes beyond a one-line project blurb. The chart has moved from 0.19.0 to 0.22.5 in about six weeks, a packaging cadence rather than an application one. Nothing here describes a change to Superset's dashboards, SQL Lab, or semantic layer.
The chart is being cut on a fast, small-increment schedule — five patches inside 0.22.x in under three weeks. That pattern usually reflects deployment plumbing being tuned against a moving upstream image rather than any single problem being chased. Until the crawl points at the release notes for Superset itself, this feed will keep reporting packaging motion and nothing about the product.
Expect continued 0.22.x patch tags at a similar pace, with a 0.23.0 chart bump likely when the next Superset application release lands. The entries themselves will stay note-free unless the project starts populating chart release bodies.
Omni publishes a dated weekly digest whose body is a single line listing that week's items, so each entry compresses several releases into a sentence. Across seven consecutive weeks the pattern is unmistakable: AI-powered semantic model generation reaching general availability, AI Routines creatable from chat and deliverable to Slack, AI model suggestion endpoints, AI credit controls scoped to embed entity groups and individual users, AI Evals support on Azure, and MCP surfaces appearing both in-app and as a searchDashboards tool. The non-AI items are steady BI plumbing — OAuth and GitHub App authentication for dbt connections, mobile dashboard settings, map legend positioning, presentation mode.
Two things are happening in parallel and they are related. Omni is pushing AI into the modelling layer rather than only the query layer, which is what semantic model generation reaching GA signifies — the artifact that normally takes an analytics engineer weeks is being generated. At the same time it is building the commercial and access controls that AI features require: credit limits per user and per embed entity group arrived within weeks of the AI capabilities that consume them. The MCP work points at a third direction, exposing Omni's content to external agents rather than only serving its own chat.
Credit controls appearing so soon after the AI features suggests consumption limits will keep expanding to cover newer surfaces, and with searchDashboards shipped as an MCP tool, more of Omni's catalog is the obvious next thing to expose that way.
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 Apache Superset or Omni.
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
Deequ ships GitHub tags whose release notes are one commit message long
Marquez spent 2024 turning a lineage store into a UI, then stopped releasing
Amundsen's last release was a config flag, and the feed has been silent for two years
LinkedIn's Iceberg control plane, shipping one pull request per release.
See all Apache Superset alternatives → · See all Omni alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. Omni is currently shipping more aggressively (velocity 6.3 vs 5.0), 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. Omni is currently shipping more aggressively (velocity 6.3 vs 5.0), 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 Superset alternatives in Analytics are ranked by recent ship velocity. Browse the "Apache Superset alternatives" section above for the current picks, or visit /alternatives/apache-superset for the full list with editorial commentary on each.
Top Omni alternatives in Analytics are ranked by recent ship velocity. Browse the "Omni alternatives" section above for the current picks, or visit /alternatives/omni for the full list with editorial commentary on each.