Topic payloads, mapped like table schemas

Give Kafka Topics a Visual Schema with DbSchema

Kafka keeps events in topics, not tables — DbSchema still gives them a schema view. Its open-source JDBC driver lists the cluster's topics and derives each topic's structure from Confluent Schema Registry subjects or by sampling recent messages, the way DbSchema infers MongoDB collections.

  • List every topic straight from the cluster metadata
  • Infer JSON field types by sampling live messages
  • Pin column definitions to registered Avro or JSON schemas
  • Query topics with SELECT and LIST TOPICS in the SQL editor
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DbSchema visualizing Kafka topic structures as schema diagrams
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Producers Know the Payload
Everyone Else Reads Raw Bytes

A topic's structure lives in the producer's source code and nowhere else. Consumer teams reverse-engineer payloads from captured messages, serializer changes surface as downstream breakage, and "which fields does this topic carry?" has no place to be looked up.

DbSchema gives it one: connect to the cluster and every topic gets a schema view — inferred from real traffic or taken from the registry — to browse, query, and document.

Discover, Query & Sample
Kafka Topics from a Desktop App

Stream introspection with database-tool ergonomics — no custom consumer required.

Discover Kafka topics and infer their message structure
Topics Found, Structure Inferred

The driver reads cluster metadata to list the topics, then samples messages or consults the Schema Registry to build a schema view for each one.

Query Kafka topics with SQL statements in DbSchema
SELECT from a Stream

Run statements like LIST TOPICS and SELECT * FROM orders LIMIT 100; with expand=true on the URL, message values flatten from JSON documents into columns.

Browse Kafka topic messages in the DbSchema data explorer
Messages in a Grid

Page through sampled messages, filter them, and check keys, payload shape, and header metadata — the quickest audit of a new producer or CDC pipeline.

Topic Documentation
Your Consumers Can Browse

Export the inferred and registry-backed topic schemas as interactive HTML5 documentation. Teams consuming your streams read field names and types in a browser — instead of asking around or scraping example messages.

Explore the Documentation Tool

HTML5 documentation of Kafka topic schemas generated by DbSchema

Kafka Connection: Driver, Port, and URL Parameters

DbSchema reaches Kafka through its own open-source JDBC driver (source on GitHub), fetched automatically when the connection is added. Brokers listen on port 9092 by default, so the basic URL is jdbc:kafka://host:9092.

URL parameters tune the discovery: scan (fast, medium, or full) sets how many messages are sampled when inferring structure, schemaRegistry points at a Confluent Schema Registry so registered Avro or JSON schemas define stable columns, and groupId names the consumer group — for example jdbc:kafka://localhost:9092?scan=fast&schemaRegistry=http://localhost:8081&groupId=dbschema-jdbc. More on the Kafka JDBC driver page.

See What Really Flows Through Your Topics

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Teams working with Kafka often use these engines too. Explore dedicated guides and JDBC setup for each.

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