Streaming · Event platform connector

The Confluent data catalog your whole team can trust.

The Dawiso Confluent Kafka data catalog turns your Confluent Cloud into a searchable inventory: clusters, topics, Stream Governance schemas and consumer groups.

Read the docs

Live connector Stable connector

Confluent

Dawiso

Metadata-only · your data never leaves the source

Type: Managed Kafka platform
Auth: Confluent API Key + Secret (cluster + Schema Registry)
Sync: Scheduled, incremental
Direction: Read-only · metadata

First things first

What is a data connector?

Metadata-only Read-only access Incremental sync Cross-system lineage

A data connector is the bridge between a tool in your stack and the catalog that gives you a unified view of it. Once a connector is configured, it reaches into the source system on a schedule, reads out the metadata - schemas, tables, dashboards, jobs, ownership, lineage - and represents it inside the catalog. Your actual rows and values stay where they are.

Connectors are the reason a data catalog can answer questions like "which Power BI dashboard depends on this Snowflake table?" or "who owns the orders topic in Kafka?" - automatically, without anyone keeping a spreadsheet up to date.

Three properties separate a good connector from a brittle one: it should be read-only and safe, it should be incremental so a full re-scan isn't required for every refresh, and it should resolve lineage across system boundaries, not just inside one tool.

About the platform

What is Confluent Kafka?

Confluent Kafka is the managed Apache Kafka platform from the company founded by Kafka's original creators. Teams use Confluent Cloud (or Confluent Platform on-prem) when they want Kafka without running brokers, with extras around it: Stream Governance, Schema Registry, ksqlDB, RBAC and a Stream Catalog for topic discovery.

Confluent's Stream Catalog handles topics inside Confluent. What it doesn't cover is the Power BI report consuming the downstream warehouse table, or the data product the business actually owns end to end. That's where the Dawiso Confluent Kafka data catalog joins the picture: read-only, metadata-only, and cross-platform.

Architecture

How Dawiso connects to Confluent

A small read-only role on the Confluent side. The Dawiso scanner pulls metadata on a schedule. Everything ends up in your catalog, business-readable.

Source

Confluent Cloud environment

Clusters & REST endpoint
Topics & partitions
Stream Governance schemas
Consumer groups & ACLs

REST · JDBC

Dawiso scanner

Read-only metadata

Schema & object discovery
Dependency resolution
SQL flow parsing (optional)
Sampling on opt-in

Internal

Catalog

Dawiso platform

Searchable metadata
Lineage & ownership
Business glossary
Policy & classifications

Connection details

Protocol: Confluent Cloud REST API + Schema Registry API
Authentication: API Key + Secret for cluster · API Key + Secret for Schema Registry · service-account ACLs
Lineage: Topics, schemas and consumer groups cataloged from cluster metadata and the Stream Governance Schema Registry, with ownership and schema versions documented in Dawiso

Setup

Connect Confluent in 4 steps

01

Get cluster ID and REST endpoint

In Confluent Cloud, open Cluster Overview > Cluster settings. Copy the Cluster ID and the REST endpoint URL. Store them for the connection form.
02

Create cluster API key

Under Cluster Overview > API keys, create a Service-account API key with ACLs scoped to DescribeConfigs on Cluster, Describe on Consumer Group, Describe + DescribeConfigs on Topic.
03

Create Schema Registry API key

From the environment's Stream Governance API panel, copy the endpoint URL and create a Schema Registry API key + secret. Both pairs are needed to ingest topics and schemas.
04

Connect and run ingestion

Provide cluster + Schema Registry endpoints and credentials in Dawiso. Pick projects via regex, save the data source, run ingestion. Scheduled incremental sync keeps the catalog current.

Capabilities

What you get with the Confluent connector

Topic & cluster catalog

Every topic across every Confluent environment is searchable, with partition count, retention, owner and the team that built the producer.
Stream Governance sync

Avro, Protobuf and JSON schemas from the Confluent Schema Registry, with version history and diff view between releases.
Consumer group visibility

Consumer groups are pulled into the catalog alongside the topics they read, so it is clear which groups are registered against each topic.
PII in events

Classify schema fields once. Dawiso flags every Confluent topic carrying email, IBAN or government IDs across all clusters and environments.
Data contracts

Promote a schema to a contract. Block breaking changes before they ship and notify subscribers when the contract evolves.
RBAC & ACL visibility

Cluster ACLs and service-account assignments are pulled into the catalog so business sees who can read what, where.

Business value

Why teams turn on the Confluent connector

0
Silent schema breakages

Schema diffs and consumer notifications stop a Protobuf rename from taking down three downstream services overnight.
−80%
Time to find a topic

New engineers stop pinging Slack to ask 'which topic has order events.' They search the Confluent Kafka data catalog and read.
EU-grade
Streaming governance

PII flowing through Confluent is classified, auditable and policy-tracked the same way as data sitting in your warehouse.

Ready to catalog your Confluent?

Set up the connector in an afternoon. See your first lineage graph the same day.

Read the docs

Frequently asked questions

Still curious? Talk to our team ->

Does Confluent have a data catalog?

Confluent Cloud offers Stream Catalog for streaming metadata. Dawiso brings your Confluent topics, schemas and consumer groups into the same catalog as your warehouses and BI, with ownership and classification across the whole stack.

What is Kafka metadata?

Kafka metadata covers topics, partitions, schemas and consumer groups; Confluent adds Schema Registry and Stream Governance. Dawiso reads topic and schema metadata read-only and documents each topic with ownership, meaning and schema versions.

What is the difference between Kafka and Confluent Kafka?

Confluent Kafka adds enterprise tooling - Schema Registry, Stream Governance, connectors - on top of open-source Kafka. Dawiso connects to Confluent Cloud and catalogs your topics, schemas and consumer groups alongside the rest of your data.

What permissions does Dawiso need in Confluent?

Two API key pairs. One for the cluster, scoped to a service account with DescribeConfigs on Cluster, Describe on Consumer Group, Describe + DescribeConfigs on Topic. One for the Schema Registry. Read-only end to end.

Does Dawiso consume from our topics?

No. Default mode is metadata-only via the Confluent REST API and the Stream Governance Schema Registry. Topic sampling is opt-in per data source and never runs automatically.

How does Dawiso compare to Confluent Stream Catalog?

Stream Catalog covers topics inside Confluent. Dawiso brings Confluent topics and schemas into one catalog alongside your warehouse and BI assets, with shared ownership and classification. Both can run side by side; Dawiso reads metadata-only.

Does the connector support Confluent Platform on-prem?

The connection form is built around Confluent Cloud's REST endpoint, Schema Registry and API-key auth. For Confluent Platform on-prem, contact Customer Success - on-premise ingestion uses Dawiso Integration Runtime (DIR).