RRepoGEO

REPOGEO REPORT · LITE

redpanda-data/connect

Default branch main · commit 6a606d2a · scanned 5/19/2026, 10:46:40 PM

GitHub: 8,675 stars · 944 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface redpanda-data/connect, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root containing the full text of the intended open-source license for Redpanda Connect.
  • highreadme#2
    Reposition README introduction to highlight unique value proposition

    Why:

    CURRENT
    Redpanda Connect is a stream processor that moves data between a wide range of [sources][inputs] and [sinks][outputs], with support for [hydration, enrichment, transformation, and filtering][processors] along the way.
    COPY-PASTE FIX
    Redpanda Connect is a lightweight, Go-based stream processor designed for moving data between a wide range of sources and sinks, offering a simpler, more resource-efficient alternative to JVM-based solutions like Kafka Connect for real-time data pipelines and change-data-capture (CDC).
  • mediumtopics#3
    Add specific topics to improve categorization

    Why:

    CURRENT
    amqp, cqrs, data-engineering, data-ops, etl, event-sourcing, go, golang, kafka, logs, message-bus, message-queue, nats, rabbitmq, stream-processing, stream-processor, streaming-data
    COPY-PASTE FIX
    amqp, cqrs, data-engineering, data-ops, etl, event-sourcing, go, golang, kafka, logs, message-bus, message-queue, nats, rabbitmq, stream-processing, stream-processor, streaming-data, kafka-connect-alternative, cdc-connector, data-integration, real-time-data

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface redpanda-data/connect
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Apache Kafka
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Apache Kafka · recommended 1×
  2. Kafka Streams · recommended 1×
  3. ksqlDB · recommended 1×
  4. Kafka Connect · recommended 1×
  5. Apache Flink · recommended 1×
  • CATEGORY QUERY
    How to build robust data pipelines for real-time stream processing and CDC?
    you: not recommended
    AI recommended (in order):
    1. Apache Kafka
    2. Kafka Streams
    3. ksqlDB
    4. Kafka Connect
    5. Apache Flink
    6. Confluent Platform
    7. Confluent Cloud
    8. Debezium
    9. Apache Spark Streaming
    10. Structured Streaming
    11. Google Cloud Dataflow
    12. Apache Beam
    13. Amazon Kinesis
    14. Kinesis Data Streams
    15. Kinesis Data Analytics

    AI recommended 15 alternatives but never named redpanda-data/connect. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are lightweight, scalable tools for event stream processing with declarative config?
    you: not recommended
    AI recommended (in order):
    1. Apache Flink (apache/flink)
    2. ksqlDB (confluentinc/ksql)
    3. Apache Kafka Streams (apache/kafka)
    4. Materialize (MaterializeInc/materialize)
    5. RisingWave (risingwavelabs/risingwave)

    AI recommended 5 alternatives but never named redpanda-data/connect. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of redpanda-data/connect?
    pass
    AI named redpanda-data/connect explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts redpanda-data/connect in production, what risks or prerequisites should they evaluate first?
    pass
    AI named redpanda-data/connect explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo redpanda-data/connect solve, and who is the primary audience?
    pass
    AI named redpanda-data/connect explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)
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redpanda-data/connect — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite