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ProductArena

Data Warehouses & Lakehouses Arena

Data Warehouses & Lakehouses — procurement report

ProductArena · rankings as of 2026-09-16 · evidence as of 2026-09-16 · 4 products · 54 judged requirements · 216 judged cells

Methodology: Every product is judged against a shared taxonomy of user stories using cited evidence — hands-on probes > repository code > independent community sources > vendor claims — never opinion. Full writeup: https://ultrametric.ai/productarena/methodology

Leaderboard

#ProductPA ScoreCoverage scoreApplicable cellsConfidence
1Databricks47.652.053/54C
2Snowflake43.345.652/54C
3BigQuery40.545.052/54B
4MotherDuck36.834.654/54B

PA Score = agent-readiness blend (see methodology). Coverage score = weighted share of judged requirements met. Confidence = how much of the score rests on tested vs claimed evidence (A–D).

Uncertainty note

The current #1/#2 gap in this arena is not close enough to qualify for the multi-judge uncertainty pass (or the pass has not covered it yet) — no extra caveat applies beyond the per-product confidence grades above.

Buyer checklist (RFP)

The arena's 54 judged user stories as requirements, grouped by theme. Priorities mirror the story weights our scoring uses (3 = must-have, 2 = should-have, 1 = nice-to-have). Interactive version with per-requirement verdicts for the top products: /arena/data-warehouses/checklist

Agent analytics — stories about agent analytics in this arenaAgent analytics

Stories about agent analytics in this arena

  • ai-native userMy agent can run governed SQL end to end — authenticate, discover schemas, query, and read results back through a CLI or API with no dashboard in the loopmust-have
  • ai-native userA built-in AI assistant writes, fixes, and explains SQL against my schemas from natural language, inside the productshould-have
  • ai-native userBusiness users can ask questions in natural language and get governed, semantically-grounded answers rather than hallucinated joinsnice-to-have

Agenticness — how well agents can access and operate the productAgenticness

How well agents can access and operate the product

  • ai-native userPlug MCP servers into this product so it can use their toolsmust-have
  • ai-native userConnect an agent via an official MCP servermust-have
  • ai-native userDrive the product through a documented public APImust-have
  • ai-native userDelegate tasks to a built-in AI assistant inside the productmust-have
  • ai-native userPoint an agent at llms.txt or agent-oriented docsshould-have
  • ai-native userRun the product headlessly / in CI for automationshould-have
  • ai-native userUse an official CLIshould-have
  • ai-native userIssue scoped/least-privilege API credentials for an agentshould-have
  • ai-native userBuild against official SDKsshould-have
  • ai-native userSubscribe to events via webhooksshould-have
  • ai-native userGet AI-generated insights and suggestions from my data inside the productshould-have
  • ai-native userSet up automations that run autonomously in the backgroundshould-have
  • ai-native userOperate the product with natural-language commandsshould-have
  • ai-native userExplore an interactive API reference with runnable examplesshould-have
  • ai-native userDownload a machine-readable API spec (OpenAPI or equivalent)should-have
  • ai-native userRely on versioned APIs with a documented deprecation policyshould-have
  • ai-native userTest against a sandbox environment without touching production datanice-to-have

Automation depth — how much of the product can run unattendedAutomation depth

How much of the product can run unattended

  • ai-native userDefine rules that trigger actions automatically on eventsmust-have
  • ai-native userPerform bulk operations across many items at onceshould-have
  • ai-native userSchedule recurring jobs or workflowsshould-have
  • ai-native userVersion, review, and roll back my automationsnice-to-have

Cost economics — stories about cost economics in this arenaCost economics

Stories about cost economics in this arena

  • platform-engineerThe pricing model is documented clearly enough that I can estimate a monthly bill for my workload before committingmust-have
  • platform-engineerBudgets, resource monitors, or auto-suspend stop a runaway query or idle compute from burning money overnightshould-have
  • analystEvaluate with a free tier or trial — real queries on real data without a credit card or a sales callnice-to-have

Ecosystem integrations — the surrounding ecosystem — integrations, marketplaces, community packagesEcosystem integrations

The surrounding ecosystem — integrations, marketplaces, community packages

  • data-engineerI get a fast local or free dev loop — a local engine, emulator, or sandbox — to develop transformations before touching production computeshould-have
  • data-engineerDbt is a first-class citizen — a documented adapter or native dbt project support with vendor docs to matchshould-have
  • analystStandard drivers (JDBC/ODBC) and documented BI-tool integrations connect my dashboards without custom gluenice-to-have

Governance access — stories about governance access in this arenaGovernance access

Stories about governance access in this arena

  • platform-engineerAccess control reaches tables, columns, and rows — roles plus masking policies — so one warehouse can serve many teams safelymust-have
  • platform-engineerI get audit logs of who ran what and column-level lineage of where data came fromshould-have
  • platform-engineerCompliance attestations (SOC 2, HIPAA, PCI) are documented so security review does not stall the rolloutnice-to-have

Ingestion pipelines — stories about ingestion pipelines in this arenaIngestion pipelines

Stories about ingestion pipelines in this arena

  • data-engineerBulk-load CSV, JSON, and Parquet from cloud object storage with a single documented commandmust-have
  • data-engineerFirst-party and partner connectors cover my sources — SaaS apps, databases, and ETL/ELT tools — with documented setupshould-have
  • data-engineerA managed service continuously ingests new files or events as they arrive, without me running my own pipeline infrastructureshould-have

Notebooks workspace — stories about notebooks workspace in this arenaNotebooks workspace

Stories about notebooks workspace in this arena

  • analystFirst-party notebooks let me mix SQL and Python against warehouse data, with results and charts inlineshould-have

Openness — open source, data portability, and self-hosting storiesOpenness

Open source, data portability, and self-hosting stories

  • ai-native userExport all of my data in open formats and leavemust-have
  • ai-native userSelf-host the core productmust-have
  • ai-native userDo everything through the API that I can do in the UIshould-have
  • ai-native userRead the product's source under an open licenseshould-have

Privacy posture — data-handling and privacy storiesPrivacy posture

Data-handling and privacy stories

  • ai-native userPrevent my data from being used to train AI modelsmust-have
  • ai-native userChoose where my data is stored (region/residency)should-have
  • ai-native userControl data retention and deletionshould-have
  • ai-native userOpt out of telemetry and usage trackingshould-have

Semantic layer — stories about semantic layer in this arenaSemantic layer

Stories about semantic layer in this arena

  • analystDefine a governed semantic model — metrics, dimensions, and joins declared once — that queries and AI tools answer against consistentlyshould-have

Sharing marketplace — stories about sharing marketplace in this arenaSharing marketplace

Stories about sharing marketplace in this arena

  • data-engineerShare live datasets with another account or organization without copying data or building an export pipelineshould-have
  • analystA marketplace of third-party datasets lets me enrich my own data directly inside the platformnice-to-have

Sql analytics — stories about sql analytics in this arenaSql analytics

Stories about sql analytics in this arena

  • analystI get a full analytical SQL surface — window functions, CTEs, semi-structured JSON, arrays, and rich date/time types — without bolt-on extensionsmust-have
  • data-engineerQuery open table formats and files in object storage — Iceberg, Delta, Parquet — without first loading them into proprietary storageshould-have
  • data-engineerInspect query profiles and execution plans to find why a query is slow or expensiveshould-have
  • data-engineerTime-travel — query data as of a past point and restore dropped or corrupted tables from historyshould-have

Streaming realtime — stories about streaming realtime in this arenaStreaming realtime

Stories about streaming realtime in this arena

  • data-engineerStreaming writes land queryable within seconds through a documented streaming ingestion APIshould-have
  • data-engineerRun continuous or incremental transformations — streams, tasks, declarative pipelines, or continuous queries — inside the platformnice-to-have

Appendix: recorded probes

Hands-on probe recordings — transcripts/videos a human can replay, the strongest evidence tier. Watch them at https://ultrametric.ai/productarena/proofs

  • BigQuerynpx -y @toolbox-sdk/server --version # MCP Toolbox for Databases (prebuilt BigQuery tools)terminal · recorded 2026-09-07 · exit 0
  • BigQuerycurl -s https://bigquery.googleapis.com/discovery/v1/apis/bigquery/v2/rest | python3 -c '<print id/title/basePath/resources>'terminal · recorded 2026-09-07 · exit 0
  • Databricksdatabricks aitools install --path /tmp/pa-dbx-skills # vendor-shipped agent skills, then list + print one SKILL.md frontmatterterminal · recorded 2026-09-07 · exit 0
  • Databricksdatabricks --version # installed via `brew tap databricks/tap && brew install databricks`terminal · recorded 2026-09-07 · exit 0
  • MotherDuckcurl -s https://motherduck.com/.well-known/agent-skills/index.json | head -20terminal · recorded 2026-09-07 · exit 0
  • MotherDuckduckdb -c 'CREATE TABLE events AS SELECT ... FROM range(1000000); SELECT region, count(*), sum(revenue) FROM events GROUP BY region' # keyless in-memory engineterminal · recorded 2026-09-07 · exit 0
  • MotherDuckprintf '<initialize> <initialized> <tools/call execute_query GROUP BY>' | uvx mcp-server-motherduck --db-path :memory: --read-write # real SQL through MCP, keylessterminal · recorded 2026-09-07 · exit 0
  • MotherDuckcurl -si -X POST https://api.motherduck.com/mcp -H 'Content-Type: application/json' -d '<jsonrpc initialize>'terminal · recorded 2026-09-07 · exit 0
  • MotherDuckprintf '<jsonrpc initialize>' | uvx mcp-server-motherduck --db-path :memory: --read-write # stdio handshake, no accountterminal · recorded 2026-09-07 · exit 0
  • Snowflakeuvx --from snowflake-cli snow sql --helpterminal · recorded 2026-09-07 · exit 0
  • Snowflakeuvx --from snowflake-cli snow --versionterminal · recorded 2026-09-07 · exit 0

Cite as: ProductArena by Ultrametric Inc, Data Warehouses & Lakehouses arena, rankings as of 2026-09-16 — https://ultrametric.ai/productarena/arena/data-warehouses

License: © 2026 Ultrametric Inc. Brief quotation of individual verdicts, scores, or evidence excerpts is permitted with attribution to "ProductArena by Ultrametric Inc (ultrametric.ai/productarena)", as is use of the data to evaluate, contest, or contribute corrections. Bulk copying, redistribution, or use to build competing datasets requires prior written permission (see DATA-LICENSE in the repository).

No liability: rankings, verdicts, and scores are research outputs derived from the cited evidence at a point in time, provided "as is", without warranties. Ultrametric Inc accepts no responsibility for procurement, purchasing, or other decisions made in reliance on them — verify against the cited evidence before acting (https://ultrametric.ai/productarena/terms).