IT AI Index
Index › Vendors › Databricks SQL / Lakehouse · September 2026 Edition
Databricks · 1 category · Named, not ranked

Databricks SQL / Lakehouse

2Judge labels
0First choices
1Negative labels
1 of 12Models named it
1Category
September 2026 Edition. Every number here is derived from the raw labels under vendor table vv2026-09.2, every buyer segment counted.
Standing
1 label, too few to rank
A product needs 10 labels in a category before a share or quadrant is stated. Databricks SQL / Lakehouse was named 1 time in Data warehouses, where Snowflake led with 47%. The labels and the evidence are below, counted exactly.
By buyer segmentRead the same way at every buyer size.
Small business37 of 43
0%1 label
under the label threshold
Mid-market18 of 41
0%1 label
under the label threshold
Enterprisenot named
0%
No model named it for this buyer.
In data warehouses · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading

Standing by category

Every category where a model named Databricks SQL / Lakehouse for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.
CategoryFunctionShareRankNegative rateLabelsQuadrant
Data warehousesData platform0%18 of 410%1under 10 labels · led by Snowflake at 47%

Movement

This is the first edition on this tier, so no move can be computed for Databricks SQL / Lakehouse yet. From the next edition this section shows, per buyer segment, whether its share moved by more than the measured noise floor.

By model

How each model treated Databricks SQL / Lakehouse across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.
ModelFirst choiceAlternativeMentionNegativeLabels
Claude Haiku 4.500000
GPT-5.4 mini01001
Gemini 3.5 Flash00000
Perplexity Sonar00000
Grok 4.1 Fast00000
Mistral Small00000
DeepSeek V4 Flash00000
Llama 4 Maverick00000
Qwen 3.7 Flash00000
Kimi K200000
GLM 4.7 FlashX00000
MiniMax M2.500000

By framing

Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.
FramingLabels by classFirst choices
Direct0 labelsNone
Paraphrase1 labelNone
Comparative0 labelsNone
Budget-constrained1 labelNone
Scale-constrained0 labelsNone
Negative0 labelsNone
First choiceAlternativeMentionNegative2 labels in all, every segment counted; 0 of the 0 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.

What the models said for it

Verbatim evidence the judge attached to positive labels.

“Choose Databricks SQL / Lakehouse if your company needs both warehouse-style BI and data engineering / ML / lakehouse workflows” GPT-5.4 mini · Data warehouses · paraphrase prompt · alternative

And against it

Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.

No model argued against it.

Named alongside

The products named in the same answers as Databricks SQL / Lakehouse, over the 2 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Databricks SQL / Lakehouse was named but was not.
ProductSame answerTook the first choice insteadHead to head
Google BigQuery2 of 21Not in the top three
Snowflake2 of 21Not in the top three
Amazon Redshift2 of 20Not in the top three
Azure Synapse Analytics1 of 20Not in the top three
DuckDB / Postgres + BI tools1 of 20Not in the top three
A head-to-head page exists where both products are in a category's top three. The other rows are the same fact without a page behind them, so they link to the product instead.

What carried it into the answer

The sites and pages cited by the answers that named Databricks SQL / Lakehouse. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, four of the twelve in this edition, so these counts come from 1 of the 2 answers that named Databricks SQL / Lakehouse and are not a share of its labels.

Domains cited

docs.aws.amazon.com1
docs.databricks.comYour site1
snowflake.com1

Two of the three domain citations in answers naming Databricks SQL / Lakehouse came from somebody else's page.

Pages cited

docs.aws.amazon.com/pdfs/whitepapers/latest/big-data-analytics-options/big-data-analytics-1
docs.databricks.com/aws/en/compute/sql-warehouse/warehouse-types1
snowflake.com/en/product/platform1

Pages are listed as the models cited them.

Is this your product?

Claim this page

Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when Databricks SQL / Lakehouse's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as Databricks SQL / Lakehouse, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

It does not get any change to labels, shares or verdicts, any preview, or any say over which quotes appear. A verification link goes to your work email; an address at databricks.com is approved on the spot, any other address is reviewed by hand.

Your name and company appear on the claimed page, or the company alone if you ask below. A title and a LinkedIn address appear there too if you give them, and are left off if you do not. Your email address is never published.

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