Google BigQuery Sink Connector for Confluent Platform


If you are using Confluent Cloud, see for the cloud Quick Start.

The Kafka Connect Google BigQuery Sink Connector is used to stream data into BigQuery tables. When streaming data from Apache Kafka® topics (that have registered schemas), the sink connector can automatically create BigQuery tables with the appropriate BigQuery table schema. The BigQuery table schema is based upon information in the Kafka schema for the topic.


  • The connector supports insert operations and attempts to detect duplicates. See BigQuery troubleshooting for additional information.

  • The connector uses the BigQuery insertAll streaming api which inserts records one at a time. The records are immediately available in the table for querying.

  • The connector supports streaming from a list of topics into corresponding tables in BigQuery.

  • Even though the connector streams records one at a time by default (as opposed to running in batch mode), the connector is scalable because it contains an internal thread pool that allows it to stream records in parallel.


    The internal thread pool defaults to 10 threads. This is configurable.


The following are required to run the Kafka Connect BigQuery Sink Connector:

  • Kafka Broker: Confluent Platform 3.3.0 or above, or Kafka 0.11.0 or above
  • Connect: Confluent Platform 3.3.0 or above, or Kafka 0.11.0 or above
  • Java 1.8
  • Active Google Cloud Platform (GCP) account with authorization to create resources


Streaming into BigQuery not available via the GCP free tier. If you attempt to use streaming without enabling billing, you receive the following error: BigQuery: Streaming insert is not allowed in the free tier. See Streaming data into BigQuery for details.

Install the BigQuery Connector

You can install this connector by using the Confluent Hub client (recommended) or you can manually download the ZIP file.

confluent-hub install wepay/kafka-connect-bigquery:latest

You can install a specific version by replacing latest with a version number. For example:

confluent-hub install wepay/kafka-connect-bigquery:1.1.2


The GCP BigQuery Sink Connector is an open source connector and does not require a Confluent Enterprise License.

Quick Start

The Confluent BigQuery Sink Connector can stream table records into BigQuery from Kafka topics. These records are streamed at high throughput rates to facilitate analytical queries in near real-time.

Install the Connector

For the following tutorial, you need to have Confluent Platform running locally.

Navigate to your Confluent Platform installation directory and enter the following command:

confluent-hub install wepay/kafka-connect-bigquery:latest

Adding a new connector plugin requires restarting Kafka Connect. Use the Confluent CLI to restart Connect.


The command syntax for the Confluent CLI development commands changed in 5.3.0. These commands have been moved to confluent local. For example, the syntax for confluent start is now confluent local start. For more information, see confluent local.

confluent local stop connect && confluent local start connect
Using CONFLUENT_CURRENT: /Users/username/Sandbox/confluent-snapshots/var/confluent.NuZHxXfq
Starting zookeeper
zookeeper is [UP]
Starting kafka
kafka is [UP]
Starting schema-registry
schema-registry is [UP]
Starting kafka-rest
kafka-rest is [UP]
Starting connect
connect is [UP]

Verify that the BigQuery sink connector plugin has been installed correctly and recognized by the plugin loader:

curl -sS localhost:8083/connector-plugins | jq .[].class | grep BigQuerySinkConnector

Setting up the GCP BigQuery Connector

Make sure you have the following prerequisites completed before setting up the connector.


Streaming into BigQuery not available via the GCP free tier. If you attempt to use streaming without enabling billing, you receive the following error: BigQuery: Streaming insert is not allowed in the free tier. See Streaming data into BigQuery for details.

  • An active GCP account with authorization to create resources.

  • A BigQuery project is required. The project can be created using the Google Cloud Console.

  • A BigQuery dataset is required in the project.

  • A service account that can access the BigQuery project containing the dataset. You can create this service account in the Google Cloud Console.

  • The service account must have access to the BigQuery project containing the dataset.

  • You create and download a key when creating a service account. The key must be downloaded as a JSON file. It resembles the example below:

     "type": "service_account",
     "project_id": "confluent-842583",
     "private_key_id": "...omitted...",
     "private_key": "-----BEGIN PRIVATE ...omitted... =\n-----END PRIVATE KEY-----\n",
     "client_email": "",
     "client_id": "...omitted...",
     "auth_uri": "",
     "token_uri": "",
     "auth_provider_x509_cert_url": "",
     "client_x509_cert_url": ""

    According to GCP specifications, the service account will either have to have the BigQueryEditor primitive IAM role or the bigquery.dataEditor predefined IAM role. The minimum permissions are:


Start the BigQuery sink connector

Create the file register-kcbd-connect-bigquery.json to store the connector configuration.

Connect Distributed REST quick start connector properties:

   "name": "kcbq-connect1",
   "config": {
     "connector.class": "com.wepay.kafka.connect.bigquery.BigQuerySinkConnector",
     "tasks.max" : "1",
     "topics" : "kcbq-quickstart1",
     "sanitizeTopics" : "true",
     "autoCreateTables" : "true",
     "autoUpdateSchemas" : "true",
     "schemaRetriever" : "com.wepay.kafka.connect.bigquery.schemaregistry.schemaretriever.SchemaRegistrySchemaRetriever",
     "bufferSize": "100000",
     "tableWriteWait": "1000",
     "project" : "confluent-243016",
     "datasets" : ".*=ConfluentDataSet",
     "keyfile" : " /Users/titomccutcheon/dev/confluent_fork/kafka-connect-bigquery/kcbq-connector/quickstart/properties/confluent-243016-384a24e2de1a.json"


The project key is the id value of the BigQuery project in GCP. For datasets, the value ConfluentDataSet is the ID of the dataset entered by the user during GCP dataset creation. Please include .*= before the dataset id. keyfile is the service account key JSON file location.

If you don’t want this connector to create a BigQuery table automatically, create a BigQuery table with Partitioning: Partition by ingestion time and a proper schema.

Start the connector.

curl -i -X POST -H "Accept:application/json" -H  "Content-Type:application/json" http://localhost:8083/connectors/ -d @register-kcbd-connect-bigquery.json

Start your Kafka producer

First go to the Kafka bin folder and start a producer in a new terminal session. Type the following command which waits on terminal input.

./kafka-avro-console-producer --broker-list localhost:9092 --topic kcbq-quickstart1 --property value.schema='{"type":"record","name":"myrecord","fields":[{"name":"f1","type":"string"}]}'

Next, enter the text for two test records. Press Enter after typing each line.

./kafka-avro-console-producer --broker-list localhost:9092 --topic kcbq-quickstart1 --property value.schema='{"type":"record","name":"myrecord","fields":[{"name":"f1","type":"string"}]}'
{"f1":"Testing the Kafka-BigQuery Connector!"}
{"f1":"Testing the Kafka-BigQuery Connector for a second time!"}

Check the results in BigQuery

Go to the BigQuery query editor in GCP and enter the following SQL SELECT statement.

SELECT * FROM ConfluentDataSet.kcbq_quickstart1;


ConfluentDataSet is the dataset ID and kcbq_quickstart1 is the name of the BigQuery table taken from the Kafka topic. The Connector converts the topic kcbq-quickstart1 to table name kcbq_quickstart1.

Clean up resources

Delete the connector and stop Confluent services.

curl -X DELETE localhost:8083/connectors/kcbq-connect1
confluent local stop

Partitioning Features

To use timestamp partitioning by field name, the bigQueryPartitionDecorator needs to be set to false. Then, set the timestampPartitionFieldName property to the field name that contains the partitioning timestamps (for example, timestampPartitionFieldName=f2). Using the BigQuery console, the following query can be used to output a list of existing partitions:

SELECT f2 as pt, FORMAT_TIMESTAMP("%Y%m%d", f2) as partition_id

The output lists all rows that have different dates and an additional partition_id column. Records that have a timestamp within a day’s range will have the same partition_id.

Additional Documentation