Overview Leaderboard Methodology
Industry Report

Data Lakehouse Platforms

Unified systems combining data lake scalability with data warehouse management and ACID transactions for analytics and machine learning.

Brands tracked: 31
Brands analyzed: 31
Last updated: 2026-04-22
Model: OpenAI GPT-5
Prompts: 251
Total responses: 1,260
Top Brand Overall?
Databricks
99/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
Databricks
97

Highest score on the LBA metric in this industry.

Authority Leader?
Databricks
100

Highest score on the Authority metric in this industry.

TOM Leader?
Databricks
100

Highest score on the TOM metric in this industry.

Databricks is the default answer in AI responses for Data Lakehouse Platforms

When users ask ChatGPT, Claude, or Gemini about Data Lakehouse Platforms, Databricks is the brand that surfaces first - unprompted, consistently, and usually at the top of any list the model generates. Snowflake is a close second, but the gap between them is meaningful. If you're competing in this space and you're not in the top handful, you're effectively invisible to AI-driven discovery.

Brand Leaderboard All 31 Data Lakehouse Platforms brands ranked

Ranked by overall AI Visibility Score (smoothed geometric mean of LBA, Authority, and TOM with an LBA-based floor on Authority and TOM, see methodology). Click any brand for the full report.

# Brand LBA Authority TOM Overall
1
Databricks
databricks.com
 97
100
100
99
2
Snowflake
snowflake.com
 89
66
94
82
3
Microsoft
microsoft.com
 94
52
82
74
4
Amazon
amazon.com
 86
25
72
55
5
Google
google.com
 74
23
66
49
6
Delta Lake
delta.io
 87
17
28
36
7
Apache
apache.org
 79
19
22
33
8
B
BigLake
googlecloud.com
 74
8
43
32
9
Starburst
starburst.io
 77
10
34
31
10
Dremio
dremio.com
 75
9
32
29
11
IBM
ibm.com
 78
8
8
19
12
Teradata
teradata.com
 77
8
8
19
13
VAST Data
vastdata.com
 80
8
8
19
14
Alluxio
alluxio.io
 74
7
7
18
15
StarRocks
starrocks.io
 75
8
8
18
16
CelerData
celerdata.com
 66
7
7
16
17
Cloudera Data Warehouse
cloudera.com
 64
6
6
16
18
Alibaba Cloud
alibabacloud.com
 62
6
6
15
19
Qubole
qubole.com
 63
6
6
15
20
Databend
databend.com
 58
6
6
14
21
T
Tabular
tabular.io
 58
6
6
14
22
Yellowbrick
yellowbrick.com
 55
6
6
14
23
HPE Ezmeral
hpe.com
 52
5
5
13
24
Kyligence
kyligence.io
 51
5
5
13
25
Tencent Cloud
tencentcloud.com
 50
5
5
13
26
Ahana
ahana.io
 48
5
5
12
27
SelectDB
selectdb.com
 49
5
5
12
28
U
Upsolver
upsolver.com
 45
5
5
12
29
Huawei
huawei.com
 42
4
4
11
30
Onehouse
onehouse.ai
 45
4
4
11
31
e6data
e6data.com
 31
3
3
8

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 251 shared Data Lakehouse Platforms prompts. The same prompts, same model, same iterations. So differences in scores reflect actual differences in AI visibility, not differences in measurement.

Overall AI Visibility Score
Smoothed geometric mean of LBA, Authority and TOM. Authority and TOM are floored at LBA × 0.1 before the geometric mean (the same floor used in the per-metric cards above, so brand cards and the composite tell the same story). Formula: composite = ((LBA + 5)(Authority + 5)(TOM + 5))^(1/3) - 5. The floor keeps brands the model clearly recognises but doesn't yet recommend from collapsing to zero, while a single genuinely weak metric still pulls the composite down. Full methodology.
Shared industry prompts
For Authority and TOM, all brands in the industry are scored against the same 251 category prompts (e.g. "best SEO tools for agencies"). This makes brand-to-brand comparisons valid - everyone faces identical inputs. LBA prompts are per-brand because they ask brand-specific questions.
Latent Brand Association (LBA)
5 brand probes + 1 control prompt, each run 5 times in recall mode (no web search). LBA = quality × meta × stability × share × recognition × 100. Read the full LBA methodology →
LLM Authority
50 organic category prompts (discovery, comparison, problem and transactional intents), each run once in recall mode and once in retrieval mode. Score = frequency × log-decayed prominence × intent weight, then 50/50 averaged across the two modes. Read the full Authority methodology →
Top of Mind (TOM)
15 high-volume discovery prompts (sourced from Keywords Everywhere search-volume data), each run 5 times in pure recall mode (no web). Score = frequency × (0.5 + 0.5 × log-prominence), volume-weighted. Read the full TOM methodology →