silx
silx settles into maintenance a release after its PySide6 migration
A side-by-side editorial comparison of hubPredEvalsData and TimescaleDB — release velocity, themes, recent moves, and the top alternatives to consider.
The pipeline turning hub forecasts into dashboard-ready evaluation data.
hubPredEvalsData generates the scored evaluation data that hubverse prediction dashboards read, driven by a predevals-config.yml and scoring through hubEvals underneath. It is the youngest package in this part of the stack and the fastest-moving in configuration terms, having already passed a breaking 1.0.0 and a schema-versioned feature addition. Its output contract is a scores.csv file consumed downstream, which shapes what its releases care about.
TimescaleDB is paying down correctness debt in its columnstore query paths.
The 2.29 line is in patch mode after 2.29.0 landed chunk exclusion for DML in late July. 2.29.1 carried three security advisories alongside compression fixes, and 2.29.2 is bug fixes only - most of them wrong-results bugs in the columnar execution paths rather than crashes. Every release note in this window recommends upgrading at the next opportunity.
hubPredEvalsData generates the scored evaluation data that hubverse prediction dashboards read, driven by a predevals-config.yml and scoring through hubEvals underneath. It is the youngest package in this part of the stack and the fastest-moving in configuration terms, having already passed a breaking 1.0.0 and a schema-versioned feature addition. Its output contract is a scores.csv file consumed downstream, which shapes what its releases care about.
Each release widens what the config file can express — round selection, then scale transformations with per-target overrides, then target labelling pulled from the hub's own task metadata. The pattern is consistent: capability that already exists in hubEvals gets a declarative surface here so hub maintainers configure it rather than write code. Recent attention to byte-stable output ordering shows the file is being treated as a reproducible artifact, not just a report.
Expect the config schema to keep absorbing hubEvals capabilities as declarative options, with continued attention to making scores.csv reproducible and diffable between runs.
The 2.29 line is in patch mode after 2.29.0 landed chunk exclusion for DML in late July. 2.29.1 carried three security advisories alongside compression fixes, and 2.29.2 is bug fixes only - most of them wrong-results bugs in the columnar execution paths rather than crashes. Every release note in this window recommends upgrading at the next opportunity.
The feature work of 2.27 and 2.28 - vectorized filter evaluation, first/last derived straight from columnstore batch metadata, sparse indexes, SkipScan on compressed data - has been followed by a steady stream of fixes to those same code paths. 2.29.2 alone repairs SkipScan dropping uncompressed rows, sparse-index pushdown returning wrong results for IS NULL, and gapfill over window aggregates. That is the normal cost of pushing query optimizations into a compressed columnar store, and the project is working through it release by release rather than pausing.
With three consecutive patch releases on the 2.29 line and no new highlighted features since 2.29.0, the next minor is likely to resume the columnstore performance work - though the density of wrong-results fixes suggests more patches first.
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 hubPredEvalsData or TimescaleDB.
silx settles into maintenance a release after its PySide6 migration
Plotly is turning its cloud into a metered compute platform with an enterprise on-ramp.
aniread stops asking you to know which tracker wrote the file
Rho's release machinery finally produced a stable build — and it shipped no new product.
Usermaven closed the loop: data comes in from anywhere, and now it goes back out.
OpenCTI spends a release unblocking queues and hardening upserts
See all hubPredEvalsData alternatives → · See all TimescaleDB alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. TimescaleDB 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. TimescaleDB 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.
Top hubPredEvalsData alternatives in Analytics are ranked by recent ship velocity. Browse the "hubPredEvalsData alternatives" section above for the current picks, or visit /alternatives/hubpredevalsdata for the full list with editorial commentary on each.
Top TimescaleDB alternatives in Analytics are ranked by recent ship velocity. Browse the "TimescaleDB alternatives" section above for the current picks, or visit /alternatives/timescaledb for the full list with editorial commentary on each.