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AI Visibility IndexBest Cloud Data Warehouses / Data Platforms (2026)
Independent ranking for the data week of 3 November 2026. About 15 companies. Composite scores use listicle consensus at 60% and review standing at 40%. AI-answer presence is omitted in v1. Refreshed monthly.
What this board measures
Cloud data platforms on this board include warehouses, lakehouses, open table formats, and engines teams still use when they have not bought a warehouse. Databricks, Google BigQuery, and Snowflake lead, each named on 5 of 5 usable pages. Amazon Redshift is next on the same mention count.
Microsoft Fabric / Synapse is the Microsoft analytics row. Apache Iceberg ecosystems is a row because comparison pages name the open table format beside the warehouses. PostgreSQL / cloud PG analytics is the "we are not on a warehouse yet" row. Teradata, Oracle Autonomous Data Warehouse, and IBM Db2 Warehouse are the long-running enterprise rows the pages still include.
ClickHouse is the real-time OLAP row. MotherDuck, SingleStore, and Yellowbrick have zero listicle hits in this sample, so their scores are review standing only. A startup on DuckDB will not see that choice reflected as a top rank on a page titled like a classic warehouse roundup.
Scores run from 18 to 98. 4 of 15 companies are named on every usable listicle page in their row. Top 3 by published rank: Databricks, Google BigQuery, Snowflake.
3 companies on this board have zero listicle mentions in the usable scrapes: MotherDuck, SingleStore, Yellowbrick. Their published scores come from the review-standing pillar only.
Data week of 2026-11-03. Scraped 5 November 2026 (Europe/Warsaw). Methodology version v1-listicle-review. Full method: AI Visibility Index methodology.
Ranked board
Score is the published 0 to 100 composite. Listicle mentions are the count of usable scraped best-of pages that name the company, over the denominator stored on that row. The review note is the qualitative mapping recorded with the row. It is not a star rating and it is not an AI mention rate.
| Rank | Company | Score | Listicle mentions | Review note |
|---|---|---|---|---|
| 1 | Databricks | 98 | 5/5 | Lakehouse platform leader |
| 2 | Google BigQuery | 98 | 5/5 | Serverless warehouse on GCP |
| 3 | Snowflake | 98 | 5/5 | Cloud warehouse category king |
| 4 | Amazon Redshift | 95 | 5/5 | AWS-native warehouse |
| 5 | Microsoft Fabric / Synapse | 83 | 4/5 | Microsoft analytics platform |
| 6 | Apache Iceberg ecosystems | 80 | 4/5 | Open table format center of gravity |
| 7 | PostgreSQL / cloud PG analytics | 76 | 4/5 | Operational + light analytics |
| 8 | Teradata | 71 | 4/5 | Legacy MPP warehouse |
| 9 | ClickHouse | 71 | 3/5 | Real-time OLAP speed |
| 10 | Oracle Autonomous Data Warehouse | 64 | 3/5 | Oracle cloud warehouse |
| 11 | IBM Db2 Warehouse | 59 | 3/5 | Legacy enterprise warehouse |
| 12 | Firebolt | 47 | 2/5 | Performance-focused cloud warehouse |
| 13 | MotherDuck | 28 | 0/5 | Cloud DuckDB for teams |
| 14 | SingleStore | 23 | 0/5 | HTAP / real-time analytics |
| 15 | Yellowbrick | 18 | 0/5 | Hybrid cloud warehouse |
Why the top 5 rank here
These five notes expand the blurbs and the mention counts from the table. They do not add benchmark scores or AI-answer percentages. The arithmetic uses only the published mention count, the row denominator, and the published composite.
1. Databricks (score 98)
Databricks is named on every usable listicle in this sample (5 of 5, 100%). Unified lakehouse for AI, engineering, and SQL analytics. Review note recorded for this row: Lakehouse platform leader.
Listicle share for this row is 5 divided by 5. At a 60% weight that share contributes 60 points before rounding. The published composite is 98. The rest of the score is the review-standing pillar (40%) after rounding. When the next row has the same mention fraction, that gap is review standing, not a hidden extra listicle. When the next row has fewer mentions, roundup consensus is the larger part of the gap. Neither gap is a product lab result, and this page does not assign Databricks a percentage of AI answers.
2. Google BigQuery (score 98)
Google BigQuery is named on every usable listicle in this sample (5 of 5, 100%). Serverless SQL analytics tightly tied to Google Cloud. Review note recorded for this row: Serverless warehouse on GCP.
Listicle share for this row is 5 divided by 5. At a 60% weight that share contributes 60 points before rounding. The published composite is 98. The rest of the score is the review-standing pillar (40%) after rounding. When the next row has the same mention fraction, that gap is review standing, not a hidden extra listicle. When the next row has fewer mentions, roundup consensus is the larger part of the gap. Neither gap is a product lab result, and this page does not assign Google BigQuery a percentage of AI answers.
3. Snowflake (score 98)
Snowflake is named on every usable listicle in this sample (5 of 5, 100%). Separation of storage/compute and data sharing keep it the enterprise default. Review note recorded for this row: Cloud warehouse category king.
Listicle share for this row is 5 divided by 5. At a 60% weight that share contributes 60 points before rounding. The published composite is 98. The rest of the score is the review-standing pillar (40%) after rounding. When the next row has the same mention fraction, that gap is review standing, not a hidden extra listicle. When the next row has fewer mentions, roundup consensus is the larger part of the gap. Neither gap is a product lab result, and this page does not assign Snowflake a percentage of AI answers.
4. Amazon Redshift (score 95)
Amazon Redshift is named on every usable listicle in this sample (5 of 5, 100%). AWS estates standardize here for warehouse SQL. Review note recorded for this row: AWS-native warehouse.
Listicle share for this row is 5 divided by 5. At a 60% weight that share contributes 60 points before rounding. The published composite is 95. The rest of the score is the review-standing pillar (40%) after rounding. When the next row has the same mention fraction, that gap is review standing, not a hidden extra listicle. When the next row has fewer mentions, roundup consensus is the larger part of the gap. Neither gap is a product lab result, and this page does not assign Amazon Redshift a percentage of AI answers.
5. Microsoft Fabric / Synapse (score 83)
Microsoft Fabric / Synapse is named on 4 of 5 usable listicles (80% of the sample). Microsoft-centric analytics spanning warehouse and lakehouse. Review note recorded for this row: Microsoft analytics platform.
Listicle share for this row is 4 divided by 5. At a 60% weight that share contributes 48 points before rounding. The published composite is 83. The rest of the score is the review-standing pillar (40%) after rounding. When the next row has the same mention fraction, that gap is review standing, not a hidden extra listicle. When the next row has fewer mentions, roundup consensus is the larger part of the gap. Neither gap is a product lab result, and this page does not assign Microsoft Fabric / Synapse a percentage of AI answers.
The rest of the board
Ranks 6 through 15 use the same v1 composite: 60% listicle consensus and 40% review standing. That number is not a percentage of answers inside ChatGPT, Claude, Gemini, Perplexity, or Google AI Overviews. Some of these rows are still named on every usable page, so a gap versus the top is review standing. Others are named on fewer pages, or on none. A narrower buyer can still prefer a lower row. The blurb is the note recorded with the dataset. The mention fraction is what this scrape counted.
6. Apache Iceberg ecosystems (score 80)
Apache Iceberg ecosystems is named on 4 of 5 usable listicles (80% of the sample). Open formats reshaping how warehouses and lakes interoperate. Review note recorded for this row: Open table format center of gravity.
7. PostgreSQL / cloud PG analytics (score 76)
PostgreSQL / cloud PG analytics is named on 4 of 5 usable listicles (80% of the sample). Teams delaying a warehouse still analyze in Postgres. Review note recorded for this row: Operational + light analytics.
8. Teradata (score 71)
Teradata is named on 4 of 5 usable listicles (80% of the sample). Long-running enterprise analytic estates. Review note recorded for this row: Legacy MPP warehouse.
9. ClickHouse (score 71)
ClickHouse is named on 3 of 5 usable listicles (60% of the sample). High-speed analytical queries for product analytics and logs. Review note recorded for this row: Real-time OLAP speed.
10. Oracle Autonomous Data Warehouse (score 64)
Oracle Autonomous Data Warehouse is named on 3 of 5 usable listicles (60% of the sample). Oracle shops consolidating analytics in OCI. Review note recorded for this row: Oracle cloud warehouse.
11. IBM Db2 Warehouse (score 59)
IBM Db2 Warehouse is named on 3 of 5 usable listicles (60% of the sample). Still present in enterprise IT comparison tables. Review note recorded for this row: Legacy enterprise warehouse.
12. Firebolt (score 47)
Firebolt is named on 2 of 5 usable listicles (40% of the sample). Speed-oriented warehouse alternative in specialist lists. Review note recorded for this row: Performance-focused cloud warehouse.
13. MotherDuck (score 28)
MotherDuck is named on none of the 5 usable listicles in this sample. The published score of 28 is the review-standing portion of the formula, because the listicle share is zero. That is thin roundup coverage in this scrape. It is not an AI-answer rate and it is not a lab test of product quality. Lightweight cloud analytics on DuckDB for startups. Review note recorded for this row: Cloud DuckDB for teams.
14. SingleStore (score 23)
SingleStore is named on none of the 5 usable listicles in this sample. The published score of 23 is the review-standing portion of the formula, because the listicle share is zero. That is thin roundup coverage in this scrape. It is not an AI-answer rate and it is not a lab test of product quality. Real-time apps needing operational and analytic queries together. Review note recorded for this row: HTAP / real-time analytics.
15. Yellowbrick (score 18)
Yellowbrick is named on none of the 5 usable listicles in this sample. The published score of 18 is the review-standing portion of the formula, because the listicle share is zero. That is thin roundup coverage in this scrape. It is not an AI-answer rate and it is not a lab test of product quality. Hybrid/on-prem analytic warehouse mentions. Review note recorded for this row: Hybrid cloud warehouse.
How to read this score
Composite score is an integer from 0 to 100.
- Listicle consensus (60%). Share of scraped independent best-of pages that name the company. listicle_score = 100 times (mentions divided by the row denominator). Vendor pages that are mostly self-promotional were avoided when stronger independent sources existed. Some vendor-authored roundups remain when they still name competitors and showed up in the open results.
- Review standing (40%). Public G2 category footprint and well-known editorial reviews, mapped to documented bands: leader_high 95, leader 88, high 80, solid 70, moderate 58, emerging 45, thin 32, and na 40 when there is no clear category-page signal. Bands are qualitative. They are not scraped star averages.
- AI-answer presence. Omitted in v1. No raw ChatGPT, Claude, Gemini, Perplexity, or Google AI Overview answers were captured and saved for this release. This page refuses invented weekly AI percentages. A later refresh can add the pillar only after those answers are stored, and the weights will be republished.
composite = round(0.6 times listicle_score + 0.4 times review_score).
Every company row on this board uses a listicle denominator of 5. That is the count of usable scraped pages stored with the row. The scoring record matches that denominator.
The planned citation-surface pillar (a later 15% weight) is also deferred. Until weights change, do not compare this board to a future version as if the formula were the same.
Limits that apply on every board: listicles disagree with each other, review sites lag the product, and brands with quiet public relations can be under-counted. A one-point gap is not statistically decisive.
Sources scraped for this board
These are the public URLs recorded for the data week of 3 November 2026. Listicle hits were counted from main content. Review hubs informed the band, not a star-to-score formula. Open the page if you want to see the wording the count came from.
- clickhouse.com: https://clickhouse.com/resources/engineering/top-5-cloud-data-warehouses
- weld.app: https://weld.app/blog/top-5-data-warehouses
- domo.com: https://www.domo.com/learn/article/best-data-warehouse-platforms
- fortegrp.com: https://www.fortegrp.com/insights/best-cloud-data-warehouse
- g2.com: https://www.g2.com/categories/data-warehouse
Listicle file count recorded with the dataset: 5. Source URL count: 5. Where those counts differ, the row denominator in the table is still the figure to use for a mention fraction.
Frequently asked questions
How is this Best Cloud Data Warehouses / Data Platforms ranking calculated?
Each company is scored from public 2026 best-of listicles and public review-platform standing such as G2 category pages. Listicle consensus is 60% of the score. Review standing is 40%. A listicle hit is a usable scraped page whose main content names the company or a documented alias. AI-answer presence is omitted in v1 until raw answers are captured and saved. This page does not publish a ChatGPT, Claude, Gemini, Perplexity, or AI Overview mention percentage.
How often is this board updated?
Monthly. This snapshot is the data week of 3 November 2026. The page was published on 5 November 2026. The methodology version is v1-listicle-review.
Why is a well-known brand missing or lower than expected?
Ranks follow documented listicle mentions and review standing in the sources on this page. A brand can be excellent and still rank lower if fewer scraped roundups name it, or if the public review evidence mapped for this category is thinner. Companies outside the roughly 15-name sample are not labeled bad. They fell outside this scrape.
Do you take payment for placement?
No. There are no paid placements on this index. Austin Heaton may advise listed or unlisted companies. An advisory relationship does not change scores.
How can my company improve its position?
Publish a clear page for the category buyers search, earn mentions in independent roundups, and strengthen the public review profiles those roundups and buyers already check. Build pages that AI systems can cite once answer tracking is added. Book a Call if you want a category audit. This board will not move a row because someone asked.
Does a one-point gap mean one product is better?
No. Listicles disagree, review sites lag, and a brand with quieter public relations can be under-counted. The composite is rounded. A one-point gap is not a decisive quality result.
Other boards
- AI Visibility Index hub
- Best BI & Analytics Tools
- Best Observability Platforms
- Best AI Agent Platforms / Builders
Related reading on this site, separate from these ranks: Measuring AI visibility explains mention rate and citation rate inside live answers. This board does not report those rates. AEO basics defines answer engine optimization.
Not on this list, or ranking lower than you should?
Austin Heaton helps B2B and SaaS brands win AI answers. The index is public research. A call is how you ask whether your category pages, roundup mentions, and review profiles are the reason a board looks the way it does.