About this catalog.
What it is, why it exists, and who maintains it.
A schema-first reference.
Data Stack Index is a structured reference for data tooling. Each tool is described against the same schema — deployment, license, pricing, warehouse support, capability flags, integrations, alternatives — so that two tools can be compared on the same fields rather than on incompatible vendor language.
The first vertical is data observability, organized into three capability clusters: quality & testing, catalog & discovery, and lineage & metadata. Additional verticals — BI, ingestion, transformation, orchestration — are planned but not committed to a date.
Sitting in a gap.
The existing options for evaluating data tooling each have a known failure mode. Vendor comparison pages are self-serving. G2 and Gartner Peer Insights are review-heavy and weak on structured comparison. GitHub awesome-lists are structured but shallow and frequently stale. Analyst reports are paywalled and slow to update.
Data Stack Index sits in the gap between awesome-lists and analyst reports — free, schema-first, comparable across tools, kept current.
One maintainer.
The catalog is built and maintained by Paul Hallaste — a dbt practitioner with prior content work at Simple Stack Answers and Full Stack Living. The maintainer's experience informs which fields matter and how categories are drawn. It does not produce per-tool editorial verdicts: tools are described, not ranked.
Where a tool has been used hands-on, optional editorial fields (notable strengths, notable weaknesses, ideal-for, avoid-if) are populated. Where it hasn't, those fields are left empty and the tool is described from documentation only. The methodology page explains how this works in detail.
How to reach us.
Factual corrections — wrong pricing, missing capability, outdated status — go to the corrections page. Other inquiries go to hello@datastackindex.com.
What this catalog is not.
Not vendor-paid. Not a review aggregator. Not an opinion site.