Create a sink connector from Apache Kafka® to Google BigQuery

Set up the BigQuery sink connector to move data from Aiven for Apache Kafka® into BigQuery tables for analysis and storage.

See the full list of parameters in the GitHub documentation.

Prerequisites

An Aiven for Apache Kafka service with Apache Kafka Connect enabled, or a dedicated Kafka Connect cluster
A Google Cloud project with:
- A service account and JSON service key
- BigQuery API enabled
- A BigQuery dataset to store data
- Dataset access granted to the service account

Collect the following details:

GOOGLE_CLOUD_PROJECT_NAME: Target Google Cloud project name
GOOGLE_CLOUD_SERVICE_KEY: Google Cloud service account key in JSON format
warning
When adding the service key to the connector configuration, provide it as an escaped string:
- Escape all " characters as \"
- Escape any \n in the private_key field as \\n
Example: {\"type\": \"service_account\",\"project_id\": \"XXXXXX\", ...}
BIGQUERY_DATASET_NAME: BigQuery dataset name
TOPIC_LIST: Comma-separated list of Kafka topics to sink
Only if using Avro: Schema Registry connection details:
- APACHE_KAFKA_HOST
- SCHEMA_REGISTRY_PORT
- SCHEMA_REGISTRY_USER
- SCHEMA_REGISTRY_PASSWORD

note

Schema Registry connection details are available in the Overview page of your Kafka service, under Connection information, in the Schema Registry tab. Aiven uses Karapace as the Schema Registry.

Configure Google Cloud

Create a service account and generate a JSON key: In the Google Cloud Console, create a service account and generate a JSON key. See Google’s guide for details. You’ll use this key in the connector configuration.
Enable the BigQuery API: In the API & Services dashboard, enable the BigQuery API if it isn’t already. See Google’s reference for details.
Create a BigQuery dataset: In the BigQuery Console, create a dataset or use an existing one. See Google’s guide. Select a region close to your Kafka service to reduce latency.
Grant dataset access to the service account: In the BigQuery Console, grant your service account the BigQuery Data Editor role on the dataset. See Google’s access control guide.

Write methods

Google Cloud Storage (default): Uses GCS as an intermediate step. Supports all features, including delete and upsert. Parameters used only with delete or upsert:
- intermediateTableSuffix
- kafkaKeyFieldName (required)
- mergeIntervalMs
Storage Write API: Streams data directly into BigQuery. Enable by setting useStorageWriteApi to true. This method provides lower latency for streaming workloads. Parameters used only with Storage Write API:
- bigQueryPartitionDecorator
- commitInterval
- enableBatchMode
warning
Do not use the Storage Write API with deleteEnabled or upsertEnabled.

If useStorageWriteApi is not set, the connector uses the standard Google Cloud Storage API by default.

Create a BigQuery sink connector configuration

Define the connector configuration in a JSON file, for example bigquery_sink.json.

{
  "name": "CONNECTOR_NAME",
  "connector.class": "com.wepay.kafka.connect.bigquery.BigQuerySinkConnector",
  "topics": "TOPIC_LIST",
  "project": "GOOGLE_CLOUD_PROJECT_NAME",
  "defaultDataset": ".*=BIGQUERY_DATASET_NAME",
  "schemaRetriever": "com.wepay.kafka.connect.bigquery.retrieve.IdentitySchemaRetriever",
  "schemaRegistryClient.basic.auth.credentials.source": "URL",
  "schemaRegistryLocation": "https://SCHEMA_REGISTRY_USER:SCHEMA_REGISTRY_PASSWORD@APACHE_KAFKA_HOST:SCHEMA_REGISTRY_PORT",
  "key.converter": "io.confluent.connect.avro.AvroConverter",
  "key.converter.schema.registry.url": "https://APACHE_KAFKA_HOST:SCHEMA_REGISTRY_PORT",
  "key.converter.basic.auth.credentials.source": "USER_INFO",
  "key.converter.schema.registry.basic.auth.user.info": "SCHEMA_REGISTRY_USER:SCHEMA_REGISTRY_PASSWORD",
  "value.converter": "io.confluent.connect.avro.AvroConverter",
  "value.converter.schema.registry.url": "https://APACHE_KAFKA_HOST:SCHEMA_REGISTRY_PORT",
  "value.converter.basic.auth.credentials.source": "USER_INFO",
  "value.converter.schema.registry.basic.auth.user.info": "SCHEMA_REGISTRY_USER:SCHEMA_REGISTRY_PASSWORD",
  "autoCreateTables": "true",
  "allBQFieldsNullable": "true",
  "keySource": "JSON",
  "keyfile": "GOOGLE_CLOUD_SERVICE_KEY"
}

note

To use the Storage Write API instead of the default GCS method, add "useStorageWriteApi": "true" to the configuration.

The configuration file includes:

name: Connector name
connector.class: Must be com.wepay.kafka.connect.bigquery.BigQuerySinkConnector
topics: Comma-separated list of Kafka topics to write to BigQuery
project: Target Google Cloud project name
defaultDataset: BigQuery dataset name, prefixed with .*=

note
By default, table names in BigQuery match the Kafka topic names. Use the Kafka Connect RegexRouter transformation to rename tables if needed.

If your messages are in Avro format, also set these parameters:

schemaRegistryLocation: Karapace schema registry endpoint
key.converter and value.converter: Set to io.confluent.connect.avro.AvroConverter for Avro
key.converter.schema.registry.url and value.converter.schema.registry.url: Schema registry URL (https://APACHE_KAFKA_HOST:SCHEMA_REGISTRY_PORT)
key.converter.schema.registry.basic.auth.user.info and value.converter.schema.registry.basic.auth.user.info: Schema registry credentials in the format SCHEMA_REGISTRY_USER:SCHEMA_REGISTRY_PASSWORD

For table management, you can set:

autoCreateTables: Create target BigQuery tables automatically if they do not exist
allBQFieldsNullable: Set new BigQuery fields to NULLABLE instead of REQUIRED

For schema evolution, you can also set:

allowNewBigQueryFields: Add new fields from Kafka schemas to BigQuery tables
allowBigQueryRequiredFieldRelaxation: Relax REQUIRED fields back to NULLABLE

warning

Automatic schema evolution reduces control over table definitions and may cause errors if message schemas change in ways BigQuery cannot support.

For authentication, set:

keySource: Format of the Google Cloud service account key, set to JSON
keyfile: Google Cloud service account key as an escaped string

Create a BigQuery sink connector

Aiven Console
Aiven CLI

Go to the Aiven Console.
Select your Aiven for Apache Kafka® or Kafka Connect service.
Click Connectors.
Click Create connector (enable Kafka Connect if required).
Select Google BigQuery Sink from the list.
On the Common tab, click Edit in the Connector configuration box.
Paste the contents of bigquery_sink.json. Replace placeholders with actual values.
Click Apply, then Create connector.
Verify the connector status on the Connectors page.
Check that data appears in the BigQuery dataset. By default, table names match topic names. Use the Kafka Connect RegexRouter transformation to rename tables if required.

Run the following command to create the connector:

avn service connector create SERVICE_NAME @bigquery_sink.json

Parameters:

SERVICE_NAME: Name of your Aiven for Apache Kafka service
@bigquery_sink.json: Path to the connector configuration file

Examples

Sink a JSON topic

Suppose you have a topic iot_measurements containing JSON messages with an inline schema:

{
  "schema": {
    "type": "struct",
    "fields": [
      { "type": "int64", "field": "iot_id" },
      { "type": "string", "field": "metric" },
      { "type": "int32", "field": "measurement" }
    ]
  },
  "payload": { "iot_id": 1, "metric": "Temperature", "measurement": 14 }
}

Connector configuration:

{
  "name": "iot_sink",
  "connector.class": "com.wepay.kafka.connect.bigquery.BigQuerySinkConnector",
  "topics": "iot_measurements",
  "project": "GOOGLE_CLOUD_PROJECT_NAME",
  "defaultDataset": ".*=BIGQUERY_DATASET_NAME",
  "value.converter": "org.apache.kafka.connect.json.JsonConverter",
  "autoCreateTables": "true",
  "keySource": "JSON",
  "keyfile": "GOOGLE_CLOUD_SERVICE_KEY"
}

Parameters:

topics: Source topic
value.converter: JSON converter without schema

note

Inline JSON schemas increase message size and add processing overhead. For efficiency, prefer Avro format with Karapace Schema Registry.

Sink an Avro topic

Suppose you have a topic students with messages in Avro format and schemas stored in Karapace.

Connector configuration:

{
  "name": "students_sink",
  "connector.class": "com.wepay.kafka.connect.bigquery.BigQuerySinkConnector",
  "topics": "students",
  "project": "GOOGLE_CLOUD_PROJECT_NAME",
  "defaultDataset": ".*=BIGQUERY_DATASET_NAME",
  "schemaRetriever": "com.wepay.kafka.connect.bigquery.retrieve.IdentitySchemaRetriever",
  "schemaRegistryLocation": "https://SCHEMA_REGISTRY_USER:SCHEMA_REGISTRY_PASSWORD@APACHE_KAFKA_HOST:SCHEMA_REGISTRY_PORT",
  "key.converter": "io.confluent.connect.avro.AvroConverter",
  "key.converter.schema.registry.url": "https://APACHE_KAFKA_HOST:SCHEMA_REGISTRY_PORT",
  "key.converter.basic.auth.credentials.source": "USER_INFO",
  "key.converter.schema.registry.basic.auth.user.info": "SCHEMA_REGISTRY_USER:SCHEMA_REGISTRY_PASSWORD",
  "value.converter": "io.confluent.connect.avro.AvroConverter",
  "value.converter.schema.registry.url": "https://APACHE_KAFKA_HOST:SCHEMA_REGISTRY_PORT",
  "value.converter.basic.auth.credentials.source": "USER_INFO",
  "value.converter.schema.registry.basic.auth.user.info": "SCHEMA_REGISTRY_USER:SCHEMA_REGISTRY_PASSWORD",
  "autoCreateTables": "true",
  "keySource": "JSON",
  "keyfile": "GOOGLE_CLOUD_SERVICE_KEY"
}

Parameters:

topics: Source topic
key.converter and value.converter: Enable Avro parsing with Karapace schema registry

Sink an Avro topic using Storage Write API

To stream Avro messages directly into BigQuery with lower latency.

Connector configuration:

{
  "name": "students_sink_write_api",
  "connector.class": "com.wepay.kafka.connect.bigquery.BigQuerySinkConnector",
  "topics": "students",
  "project": "GOOGLE_CLOUD_PROJECT_NAME",
  "defaultDataset": ".*=BIGQUERY_DATASET_NAME",
  "schemaRetriever": "com.wepay.kafka.connect.bigquery.retrieve.IdentitySchemaRetriever",
  "schemaRegistryLocation": "https://SCHEMA_REGISTRY_USER:SCHEMA_REGISTRY_PASSWORD@APACHE_KAFKA_HOST:SCHEMA_REGISTRY_PORT",
  "key.converter": "io.confluent.connect.avro.AvroConverter",
  "key.converter.schema.registry.url": "https://APACHE_KAFKA_HOST:SCHEMA_REGISTRY_PORT",
  "key.converter.basic.auth.credentials.source": "USER_INFO",
  "key.converter.schema.registry.basic.auth.user.info": "SCHEMA_REGISTRY_USER:SCHEMA_REGISTRY_PASSWORD",
  "value.converter": "io.confluent.connect.avro.AvroConverter",
  "value.converter.schema.registry.url": "https://APACHE_KAFKA_HOST:SCHEMA_REGISTRY_PORT",
  "value.converter.basic.auth.credentials.source": "USER_INFO",
  "value.converter.schema.registry.basic.auth.user.info": "SCHEMA_REGISTRY_USER:SCHEMA_REGISTRY_PASSWORD",
  "autoCreateTables": "true",
  "keySource": "JSON",
  "keyfile": "GOOGLE_CLOUD_SERVICE_KEY",
  "useStorageWriteApi": "true",
  "commitInterval": "1000"
}

Parameters:

useStorageWriteApi: Enables direct streaming into BigQuery
commitInterval: Flush interval for Storage Write API batches (in ms)

Prerequisites​

Configure Google Cloud​

Write methods​

Create a BigQuery sink connector configuration​

Create a BigQuery sink connector​

Examples​

Sink a JSON topic​

Sink an Avro topic​

Sink an Avro topic using Storage Write API​

Prerequisites

Configure Google Cloud

Write methods

Create a BigQuery sink connector configuration

Create a BigQuery sink connector

Examples

Sink a JSON topic

Sink an Avro topic

Sink an Avro topic using Storage Write API