Confluent Connector¶

The Confluent Connector allows you to ingest data from your existing Confluent Cloud cluster into Tinybird so that you can quickly turn them into high-concurrency, low-latency REST APIs.

The Confluent Connector is fully managed and requires no additional tooling. Connect Tinybird to your Confluent Cloud cluster, choose a topic, and Tinybird will automatically begin consuming messages from Confluent Cloud.

The Confluent Connector is:

• Easy to use. Connect to your Confluent cluster in seconds. Choose your topics, define your schema, and ingest millions of events per second into a fully-managed OLAP.
• SQL-based. Using nothing but SQL, query your Confluent data and enrich it with dimensions from your database, warehouse, or files.
• Secure. Use Auth tokens to control access to API endpoints. Implement access policies as you need. Support for row-level security.

Using the UI¶

To connect Tinybird to your Confluent Cloud cluster, select the + icon next to the data project section on the left navigation menu, select Data Source, and select Confluent from the list of available Data Sources.

Enter the following details:

• Connection name: A name for the Confluent Cloud connection in Tinybird.
• Bootstrap Server: The comma-separated list of bootstrap servers (including port numbers).
• Key: The Key component of the Confluent Cloud API Key.
• Secret: The Secret component of the Confluent Cloud API Key.
• Decode Avro messages with schema registry: (Optional) Enable Schema Registry support to decode Avro messages. To allow this functionality, enter the Schema Registry URL, username, and password.

Once you have entered the details, select Connect. This creates the connection between Tinybird and Confluent Cloud. You then see a list of your existing topics and can select the topic to consume from. Tinybird creates a Group ID that specifies the name of the consumer group that this Kafka consumer belongs to. You can customize the Group ID, but ensure that your Group ID has Read permissions to the topic.

Once you have chosen a topic, you can select the starting offset to consume from. You can choose to consume from the earliest offset or the latest offset:

• If you choose to consume from the earliest offset, Tinybird will consume all messages from the beginning of the topic.
• If you choose to consume from the latest offset, Tinybird will only consume messages that are produced after the connection is created.

Choose the offset, and select Next.

Tinybird then consumes a sample of messages from the topic and displays the schema. You can adjust the schema and Data Source settings as needed, then select Create Data Source.

Tinybird then begins consuming messages from the topic and loading them into the Data Source. Success!

Using .datasource files¶

If you are managing your Tinybird resources in files, there are several settings available to configure the Confluent Connector in .datasource files.

See the datafiles docs for more information.

Using INCLUDE to store connection settings¶

To avoid configuring the same connection settings across many files, or to prevent leaking sensitive information, you can store connection details in an external file and use INCLUDE to import them into one or more .datasource files.

You can find more information about INCLUDE in the Advanced Templates documentation.

As an example, you may have two Confluent Cloud .datasource files, which re-use the same Confluent Cloud connection. You can create an include file which stores the Confluent Cloud connection details.

The Tinybird project may use the following structure:

ecommerce_data_project/
    datasources/
        connections/
          my_connector_name.incl
        my_confluent_datasource.datasource
        another_datasource.datasource
    endpoints/
    pipes/

Where the file my_connector_name.incl has the following content:

KAFKA_CONNECTION_NAME my_connection_name
KAFKA_BOOTSTRAP_SERVERS my_server:9092
KAFKA_KEY my_username
KAFKA_SECRET my_password

And the Confluent Cloud .datasource files look like the following:

SCHEMA >
  `value` String,
  `topic` LowCardinality(String),
  `partition` Int16,
  `offset` Int64,
  `timestamp` DateTime,
  `key` String

ENGINE "MergeTree"
ENGINE_PARTITION_KEY "toYYYYMM(timestamp)"
ENGINE_SORTING_KEY "timestamp"

INCLUDE "connections/my_connection_name.incl"

KAFKA_TOPIC my_topic
KAFKA_GROUP_ID my_group_id

FAQs¶

Is the Confluent Cloud Schema Registry supported?¶

Yes, for decoding Avro messages. You can choose to enable Schema Registry support when connecting Tinybird to Confluent Cloud. You will be prompted to add your Schema Registry connection details, e.g. https://<SCHEMA_REGISTRY_API_KEY>:<SCHEMA_REGISTRY_API_SECRET>@<SCHEMA_REGISTRY_API_URL>. However, the Confluent Cloud Data Source schema will not be defined using the Schema Registry, the Schema Registry is simply used to decode the messages.

Can Tinybird ingest compressed messages?¶

Yes, Tinybird can consume from Kafka topics where Kafka compression has been enabled, as decompressing the message is a standard function of the Kafka Consumer.

However, if you compressed the message before passing it through the Kafka Producer, then Tinybird cannot do post-Consumer processing to decompress the message.

For example, if you compressed a JSON message through gzip and produced it to a Kafka topic as a bytes message, it would be ingested by Tinybird as bytes. If you produced a JSON message to a Kafka topic with the Kafka Producer setting compression.type=gzip, while it would be stored in Kafka as compressed bytes, it would be decoded on ingestion and arrive to Tinybird as JSON.

What are the __<field> fields stored in the Kafka datasource?¶

Those fields represent the raw data received from Kafka:

• __value: A String representing the whole Kafka record inserted
• __topic: The Kafka topic that the message belongs to
• __partition: The kafka partition that the message belongs to
• __offset: The Kafka offset of the message
• __timestamp: The timestamp stored in the Kafka message received by Tinybird
• __key: The key of the kafka message