Overview Leaderboard Methodology
Industry Report

Streaming ETL Services

Real-time data pipelines that ingest, process, and route streaming data for low-latency analytics and operational use cases.

Brands tracked: 36
Brands analyzed: 36
Last updated: 2026-04-22
Model: OpenAI GPT-5
Prompts: 281
Total responses: 1,435
Top Brand Overall?
Confluent
87/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
Apache Kafka
99

Highest score on the LBA metric in this industry.

Authority Leader?
Confluent
81

Highest score on the Authority metric in this industry.

TOM Leader?
Confluent
93

Highest score on the TOM metric in this industry.

Confluent is the default answer in AI responses for Streaming ETL Services

When users ask ChatGPT, Claude, or Gemini about Streaming ETL Services, Confluent is the brand that surfaces first - unprompted, consistently, and usually at the top of any list the model generates. Amazon 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 36 Streaming ETL Services 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
Confluent
confluent.io
 87
81
93
87
2
Amazon
amazon.com
 86
59
81
75
3
Google
google.com
 81
40
73
62
4
Apache Flink
flink.apache.org
 87
41
60
60
5
Apache Kafka
kafka.apache.org
 99
32
63
59
6
Microsoft
microsoft.com
 98
26
63
55
7
Fivetran
fivetran.com
 75
30
58
51
8
Databricks
databricks.com
 92
25
38
45
9
Airbyte
airbyte.com
 68
18
33
35
10
Snowflake
snowflake.com
 83
8
23
27
11
Debezium
debezium.io
 74
7
21
24
12
Redpanda
redpanda.com
 74
7
19
24
13
Materialize
materialize.com
 59
6
25
23
14
StreamSets
streamsets.com
 76
8
17
23
15
Matillion
matillion.com
 59
6
23
22
16
Qlik
qlik.com
 75
8
8
18
17
Talend
talend.com
 73
7
7
18
18
Hazelcast
hazelcast.com
 68
7
7
17
19
Hevo Data
hevodata.com
 68
7
7
17
20
RudderStack
rudderstack.com
 70
7
7
17
21
Striim
striim.com
 67
7
7
16
22
Tinybird
tinybird.co
 64
6
6
16
23
Aiven
aiven.io
 61
6
6
15
24
StreamNative
streamnative.io
 60
6
6
15
25
Estuary
estuary.dev
 54
5
7
14
26
WarpStream
warpstream.com
 49
5
5
13
27
Decodable
decodable.co
 48
5
5
12
28
Rivery
rivery.io
 47
5
5
12
29
U
Upsolver
upsolver.com
 47
5
5
12
30
Bytewax
bytewax.io
 41
4
4
11
31
Quix
quix.io
 44
4
4
11
32
Instaclustr
instaclustr.com
 40
4
4
10
33
DataCater
datacater.io
 33
3
3
9
34
Lenses
lenses.io
 35
4
4
9
35
M
Memphis
memphis.dev
 34
3
3
9
36
PeerDB
peerdb.io
 31
3
3
8

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 281 shared Streaming ETL Services 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 281 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 →