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OpenHouse vs statsmodels

A side-by-side editorial comparison of OpenHouse and statsmodels — release velocity, themes, recent moves, and the top alternatives to consider.

OpenHouse vs statsmodels: at a glance

FeatureOpenHousestatsmodels
SectorAnalyticsAnalytics
Velocity score5.00.0
Sparks · 30d00
Top themesiceberg, data governance, table policies, observabilitystatistics, python, compatibility, maintenance
Last editorial update1h ago1h ago
WebsiteVisit →Visit →

What is OpenHouse?

OpenHouse is hardening the seams where table policies and jobs quietly fail.

OpenHouse ships continuously — five releases in the twelve days covered here — with each tag carrying a single merged pull request. The substantive recent work sits in two areas: table governance, where CREATE OR REPLACE AS SELECT was silently dropping retention, replication, history and PII column tags, and operability, where the DataLoader gained a typed exception hierarchy with per-request IDs and the scheduler gained targeted log tokens. Table feature toggles also picked up self-service overrides that let table owners opt in to a feature the server has not ramped.

Read the full OpenHouse trajectory →

What is statsmodels?

statsmodels ships only what the ecosystem breaks — six releases, no new statistics.

Every release in this window is a compatibility release. 0.14.2 and 0.14.3 absorbed NumPy 2, 0.14.5 fixed an import failure caused by SciPy 1.16, and 0.14.6 did the same for pandas 3.0. The only additive change across two years is Pyodide support in 0.14.4, described in its own notes as one feature and no fixes. A 0.15.0.dev0 tag exists from 2023 and has not been followed by a 0.15 release.

Read the full statsmodels trajectory →

OpenHouse vs statsmodels: editorial side-by-side

O
OpenHouse
ANALYTICS
5.0

OpenHouse is hardening the seams where table policies and jobs quietly fail.

◆ Current state

OpenHouse ships continuously — five releases in the twelve days covered here — with each tag carrying a single merged pull request. The substantive recent work sits in two areas: table governance, where CREATE OR REPLACE AS SELECT was silently dropping retention, replication, history and PII column tags, and operability, where the DataLoader gained a typed exception hierarchy with per-request IDs and the scheduler gained targeted log tokens. Table feature toggles also picked up self-service overrides that let table owners opt in to a feature the server has not ramped.

◆ Where it's heading

The project is at the stage where correctness at the edges matters more than new surface: policies surviving a replace, auth failures not being retried as if they were transient, scheduler decisions being greppable in production logs. The observability work is explicitly phased, with OTEL gauges and DLQ counters deferred to a later step, so instrumentation is being staged rather than dropped in at once. The pattern of one PR per release tag means the feed reads as a commit log and the meaningful changes have to be picked out of dependency bumps.

◆ Prediction

Phase 2 of the jobs observability plan — OTEL gauges, a heartbeat sampler, and dead-letter-queue counters — is named in the notes as deferred and is the most likely next substantive change.

S
statsmodels
ANALYTICS
0.0

statsmodels ships only what the ecosystem breaks — six releases, no new statistics.

◆ Current state

Every release in this window is a compatibility release. 0.14.2 and 0.14.3 absorbed NumPy 2, 0.14.5 fixed an import failure caused by SciPy 1.16, and 0.14.6 did the same for pandas 3.0. The only additive change across two years is Pyodide support in 0.14.4, described in its own notes as one feature and no fixes. A 0.15.0.dev0 tag exists from 2023 and has not been followed by a 0.15 release.

◆ Where it's heading

The library is being kept alive rather than developed: each release answers a break introduced upstream, and the interval between them is set by the NumPy, SciPy and pandas release calendars rather than by anything statsmodels is building. Two consecutive releases whose stated purpose was restoring the ability to import the package is the sharpest available signal about maintainer bandwidth. The 0.15 line remains a dev tag with no visible progress toward a release.

◆ Prediction

The next release is most likely another compatibility patch triggered by a NumPy, SciPy or pandas major, and nothing in these entries indicates 0.15 is close.

Alternatives to OpenHouse and statsmodels

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 OpenHouse or statsmodels.

See all OpenHouse alternatives → · See all statsmodels alternatives →

Recent activity from OpenHouse and statsmodels

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 16h agoOpenHouseMetrics for misconfigured HCR tables
  2. 8d agoOpenHouseCREATE OR REPLACE no longer silently drops table policies
  3. 9d agoOpenHouseBump iceberg-core to 1.2.0.20
  4. 9d agoOpenHouseScheduler log tokens for jobs observability
  5. 11d agoOpenHouseDataLoader gains request IDs and typed catalog exceptions
  6. 11d agoOpenHouseTable owners can self-serve onto server-gated features
  7. 8mo agostatsmodels0.14.6: restores importing under pandas 3.0
  8. 1y agostatsmodels0.14.5: restores importing under SciPy 1.16
  9. 1y agostatsmodels0.14.4: Pyodide support
  10. 1y agostatsmodels0.14.3: NumPy 2 environments and corrected macOS builds
  11. 2y agostatsmodels0.14.2: full NumPy 2 compatibility
  12. 2y agostatsmodelsRelease 0.14.1

Frequently asked questions

What is the difference between OpenHouse and statsmodels?

They serve adjacent needs but don't currently overlap on shipped themes. OpenHouse is currently shipping more aggressively (velocity 5.0 vs 0.0), with 0 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 OpenHouse better than statsmodels?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. OpenHouse is currently shipping more aggressively (velocity 5.0 vs 0.0), with 0 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 OpenHouse?

Top OpenHouse alternatives in Analytics are ranked by recent ship velocity. Browse the "OpenHouse alternatives" section above for the current picks, or visit /alternatives/openhouse for the full list with editorial commentary on each.

What are the best alternatives to statsmodels?

Top statsmodels alternatives in Analytics are ranked by recent ship velocity. Browse the "statsmodels alternatives" section above for the current picks, or visit /alternatives/statsmodels for the full list with editorial commentary on each.