Confluent Connector¶

The Confluent Connector allows you to ingest data from your existing Confluent Cloud cluster into Tinybird.

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.

Note that you need to grant READ permissions to both the Topic and the Consumer Group to ingest data from Confluent into Tinybird.

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.

• KAFKA_CONNECTION_NAME: The name of the configured Confluent Cloud connection in Tinybird.
• KAFKA_BOOTSTRAP_SERVERS: The comma-separated list of bootstrap servers (including Port numbers).
• KAFKA_KEY: The Key component of the Confluent Cloud API Key.
• KAFKA_SECRET: The Secret component of the Confluent Cloud API Key.
• KAFKA_TOPIC: The name of the Kafka topic to consume from.
• KAFKA_GROUP_ID: The Kafka Consumer Group ID to use when consuming from Confluent Cloud.
• KAFKA_AUTO_OFFSET_RESET: The offset to use when no previous offset can be found, e.g. when creating a new consumer. Supported values: latest, earliest. Default: latest.
• KAFKA_STORE_RAW_VALUE: Optionally, you can store the raw message in its entirety as an additional column. Supported values: 'True', 'False'. Default: 'False'.

For example, to define Data Source with a new Confluent Cloud connection in a .datasource file:

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"

KAFKA_CONNECTION_NAME my_connection_name
KAFKA_BOOTSTRAP_SERVERS my_server:9092
KAFKA_KEY my_username
KAFKA_SECRET my_password
KAFKA_TOPIC my_topic
KAFKA_GROUP_ID my_group_id

Or, to define Data Source that uses an existing Confluent Cloud connection:

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"

KAFKA_CONNECTION_NAME my_connection_name
KAFKA_TOPIC my_topic
KAFKA_GROUP_ID my_group_id

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.