Give Kafka Topics a Visual Schema with DbSchema

DbSchema gives Kafka teams a design-first workflow: import the existing schema as an interactive ER diagram, refine it visually, and ship every change as a reviewed SQL script.

Built for document and NoSQL structure exploration with visual relationships and team docs, with an offline model you can keep in Git, team collaboration, and documentation that developers, analysts, and stakeholders can navigate in minutes.

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Visualize Kafka Topics and Message Structure

Apache Kafka stores event streams in topics rather than relational tables, but teams still need a clear picture of which topics exist, what fields appear in message payloads, and how schemas evolve over time. DbSchema connects to Kafka through its open-source JDBC driver, lists topics from the cluster metadata, and builds a schema view for each topic — either from Confluent Schema Registry subjects or by sampling recent messages and inferring JSON field types, similar to the approach used for MongoDB collections.

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Query Topic Data with SQL-Style Statements

DbSchema's SQL editor connects to Kafka through the JDBC driver and supports statements such as LIST TOPICS and SELECT * FROM orders LIMIT 100. Message values can be returned as JSON documents or flattened into columns when expand=true is set on the connection URL. This is useful for inspecting production traffic, validating serializers, or exploring topics during development without writing a custom consumer application.

Browse Messages in the Data Explorer

The data explorer displays rows sampled from a Kafka topic in a paginated grid. Filter and inspect individual messages to verify payload shape, key formats, and header metadata — helpful when debugging stream processors, CDC pipelines, or new producers before they reach downstream consumers.

Set Up DbSchema's Kafka Connection

DbSchema reaches Kafka through its own open-source JDBC driver (source on GitHub):

1. Download and install DbSchema.
2. Add a Kafka connection in the connection dialog; the driver is fetched automatically.
3. Enter the broker address — Kafka listens on port 9092 by default — to form a basic URL such as jdbc:kafka://host:9092.
4. Optionally append query parameters for a more controlled connection, for example jdbc:kafka://localhost:9092?scan=fast&schemaRegistry=http://localhost:8081&groupId=dbschema-jdbc. The scan parameter (fast, medium, or full) controls how many messages are sampled when inferring structure, and pointing schemaRegistry at a running Confluent Schema Registry makes the driver use registered Avro or JSON schemas for stable column definitions instead.
5. Connect — DbSchema lists the topics and builds a schema view for each one.

Why Teams Use DbSchema with Kafka

• List cluster topics and document inferred or registry-backed message schemas in one visual workspace.
• Sample topic payloads to understand JSON structure before building consumers or stream joins.
• Run SELECT and LIST TOPICS from DbSchema's SQL editor without a separate CLI tool.
• Integrate Kafka exploration into the same tool used for relational and NoSQL database design.
• Share schema documentation with teams that consume your event streams.

Need to know what is actually flowing through a topic before you build the next consumer? Get DbSchema, free to download, and sample Kafka messages into a schema view in minutes.

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