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Google BigQuery Source (JDBC) Connector for Confluent Cloud

The fully managed Google BigQuery Source (JDBC) connector for Confluent Cloud reads rows from BigQuery tables and streams them to Apache Kafka® topics, so that downstream services can consume BigQuery data in real time without querying BigQuery directly.

Note

The Google BigQuery Source (JDBC) connector for Confluent Cloud connects to resources in the same region and cloud provider as your Confluent Cloud cluster. If your Google BigQuery instance is in a different region or cloud provider than your Confluent Cloud cluster, contact Confluent Support to enable cross-region or cross-cloud connectivity before you configure the connector.

Features

The connector provides the following features:

  • Query modes: Copies whole tables on each poll in bulk mode, or detects new or modified rows using an incrementing column, a timestamp column, or both.

  • Custom SQL query mode: Runs a custom SQL query instead of copying whole tables, so that you can join tables or select a subset of columns.

  • High-throughput reads: Optionally streams large result sets using the BigQuery Storage Read API instead of paged REST responses, based on configurable row and page thresholds.

  • Authentication methods: Supports a Google Cloud service account key, or Google service account impersonation through a Confluent provider integration, which doesn’t require you to store service account keys. For more information, see Manage a Google Cloud Provider Integration.

  • Output record value and key format: Supports AVRO, JSON_SR (JSON Schema), PROTOBUF, and JSON (schemaless) output data formats for the record value, and AVRO, JSON_SR, PROTOBUF, JSON, or STRING for the record key. You must enable Schema Registry to use a Schema Registry-based format, such as AVRO, JSON_SR (JSON Schema), or PROTOBUF.

  • Client-side encryption (CSFLE) support: The connector supports Client-Side Field Level Encryption (CSFLE) for sensitive data. For more information about CSFLE setup, see the connector configuration.

  • Typed nested schemas for STRUCT and ARRAY columns: When reading from a table (not a custom query), the connector resolves STRUCT and ARRAY columns to typed nested Connect schemas by looking up the table’s real column definitions. For the conditions where this falls back to a JSON string instead, see the connector limitations.

  • Broad type mapping: Maps BigQuery NUMERIC, DATE, DATETIME, and TIMESTAMP types to Connect types, with configurable mapping behavior. The connector maps GEOGRAPHY, JSON, and INTERVAL columns to a string.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect Usage Examples section.

Limitations

Be sure to review the following information.

Quick Start

Use this quick start to get up and running with the Confluent Cloud Google BigQuery JDBC Source connector. The quick start provides the basics of selecting the connector and configuring it to stream rows from BigQuery tables into Kafka topics.

Prerequisites

  • Authorized access to a Confluent Cloud cluster on Amazon Web Services (AWS), Microsoft Azure (Azure), or Google Cloud.

  • The Confluent CLI installed and configured for the cluster. See Install the Confluent CLI.

  • You must enable Schema Registry to use a Schema Registry-based format, such as AVRO, JSON_SR (JSON Schema), or PROTOBUF.

  • A BigQuery dataset that you want to read tables from.

  • A Google Cloud service account with the BigQuery Job User role (roles/bigquery.jobUser) on the project that runs the queries, and the BigQuery Data Viewer role (roles/bigquery.dataViewer) on the target dataset. To use high-throughput reads, the service account also needs bigquery.readsessions.create, for example through roles/bigquery.readSessionUser. You can also use a Confluent provider integration for Google Cloud instead of a service account key.

  • For networking considerations, see Networking and DNS. To use a set of public egress IP addresses, see Public Egress IP Addresses for Confluent Cloud Connectors.

  • Kafka cluster credentials. The following lists the different ways you can provide credentials.

    • Enter an existing service account resource ID.

    • Create a Confluent Cloud service account for the connector. Make sure to review the ACL entries required in the service account documentation. Some connectors have specific ACL requirements.

    • Create a Confluent Cloud API key and secret. To create a key and secret, you can use confluent api-key create or you can autogenerate the API key and secret directly in the Cloud Console when setting up the connector.

Using the Confluent Cloud Console

Step 1: Launch your Confluent Cloud cluster

To create and launch a Kafka cluster in Confluent Cloud, see Create a kafka cluster in Confluent Cloud.

Step 2: Add a connector

In the left navigation menu, click Connectors. If you already have connectors in your cluster, click + Add connector.

Step 3: Select your connector

Click the Google BigQuery Source (JDBC) connector card.

Google BigQuery Source (JDBC) Connector Card

Step 4: Enter the connector details

Enter the connector details in each of the following tabs.

Note

  • Ensure you have all your prerequisites completed.

  • An asterisk ( * ) designates a required entry.

At the Add Google BigQuery Source (JDBC) Connector screen, complete the following:

  1. Select the way you want to provide Kafka Cluster credentials. You can choose one of the following options:

    • My account: This setting allows your connector to globally access everything that you have access to. With a user account, the connector uses an API key and secret to access the Kafka cluster. This option is not recommended for production.

    • Service account: This setting limits the access for your connector by using a service account. This option is recommended for production.

    • Use an existing API key: This setting allows you to specify an API key and a secret pair. You can use an existing pair or create a new one. This method is not recommended for production environments.

    Note

    Freight clusters support only service accounts for Kafka authentication.

  2. Click Continue.

  1. Configure the authentication properties:

    • GCP credentials file: GCP service account JSON file with read permissions for BigQuery.

    GCP credentials

    • Authentication method: Select how you want to authenticate with BigQuery.

    • Provider Integration: Select an existing provider integration that has access to your resource. To use a new Google service account, create a provider integration for it.

    How should we connect to your BigQuery project?

    • BigQuery Project ID: The ID of the GCP project containing the BigQuery dataset to read from.

    • BigQuery Dataset: The BigQuery dataset to read tables from.

  2. Click Continue.

Note

Configuration properties that are not shown in the Cloud Console use the default values. For all property values and definitions, see Configuration Properties.

  • Output Kafka record key format: Sets the output Kafka record key format. Valid entries are AVRO, JSON_SR, PROTOBUF, STRING, or JSON. You must have Confluent Cloud Schema Registry configured if using a schema-based message format.

Output messages

  • Select output record value format: Sets the output Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON. You must have Confluent Cloud Schema Registry configured if using a schema-based message format.

Connector Details

  • Tables Included: A comma-separated list of regular expressions that match the fully-qualified names of tables to copy, in the form project.dataset.table. For example, my-project.my_dataset.orders,my-project.my_dataset.customer.*. Only tables in the project and dataset selected above are considered. Identifier names are case sensitive. Not required if a Custom Query is provided. To copy every table in the dataset, set this to .*.

  • Tables Excluded: A comma-separated list of regular expressions that match the fully-qualified names of tables not to copy, in the form project.dataset.table. This only applies to the tables filtered using the include list. Identifier names are case sensitive.

  • Mode: The mode for updating a table each time it is polled. bulk performs a bulk load of the entire table on each poll. timestamp uses a timestamp (or timestamp-like) column to detect new and modified rows. incrementing uses a strictly incrementing column on each table to detect only new rows. timestamp+incrementing uses both a timestamp column and a strictly incrementing column.

  • Table to timestamp columns mappings: A comma-separated list of fully-qualified table name to timestamp columns mappings. When you specify multiple timestamp columns, the connector uses the COALESCE SQL function to determine the effective timestamp for a row. Use the format table1:[col1|col2],table2:[col3], where table1/table2 can also be a pattern (regular expression) matched against the fully-qualified table names.

  • Table to incrementing column mappings: A comma-separated list of fully-qualified table name to incrementing column mappings. Use the format table1:col1,table2:col2, where table1/table2 can also be a pattern (regular expression) matched against the fully-qualified table names.

  • Custom Query: If specified, the connector uses this custom SQL query instead of Tables Included and Tables Excluded to select rows, allowing joins or a subset of columns. Only SELECT statements are supported. In incremental modes, don’t include a WHERE, ORDER BY, or GROUP BY clause. The connector appends its own WHERE and ORDER BY clauses, which would conflict with them. STRUCT and ARRAY columns fall back to JSON strings in this mode, because the connector can’t resolve their nested schema without a table name.

  • Timestamp column name (custom query): Comma-separated timestamp column(s) used for incremental polling when a Custom Query is set. Use this instead of the table-to-timestamp mappings in custom query mode, since there is no table to match. When you specify multiple columns, the connector uses COALESCE to determine the effective timestamp.

  • Incrementing column name (custom query): Incrementing column used for incremental polling when a Custom Query is set. Use this instead of the table-to-incrementing mappings in custom query mode, since there is no table to match.

  • Table types: By default, the connector detects only tables of type TABLE. This configuration accepts a comma-separated list of table types to extract.

Show advanced configurations

  • Schema context: Select a schema context to use for this connector, if using a schema-based data format. This property defaults to the Default context, which configures the connector to use the default schema set up for Schema Registry in your Confluent Cloud environment. A schema context allows you to use separate schemas (like schema sub-registries) tied to topics in different Kafka clusters that share the same Schema Registry environment. For example, if you select a non-default context, a Source connector uses only that schema context to register a schema and a Sink connector uses only that schema context to read from. For more information about setting up a schema context, see What are schema contexts and when should you use them?.

Additional Configs

  • Value Converter Replace Null With Default: Specifies whether to replace fields that have a default value and that are null to the default value. When set to true, the connector uses the default value; otherwise, it uses null. Applies to the JSON converter.

  • Value Converter Reference Subject Name Strategy: Sets the subject reference name strategy for values. Valid entries are DefaultReferenceSubjectNameStrategy or QualifiedReferenceSubjectNameStrategy. You can use this strategy only with PROTOBUF format; the default strategy is DefaultReferenceSubjectNameStrategy.

  • Value Converter Schemas Enable: Includes schema within each of the serialized values. Input messages must contain schema and payload fields and must not contain additional fields. For plain JSON data, set this to false. Applies to the JSON converter.

  • Errors Tolerance: Use this property to configure the connector’s error handling behavior.

    Warning

    Use this property with caution for sink connectors, as it can lead to data loss. If you set this property to all, the connector does not fail on errant records, but logs them (and sends to DLQ for sink connectors) and continues processing. If you set this property to none, the connector task fails on errant records.

  • Value Converter Ignore Default For Nullables: When set to true, this property ensures that the corresponding record in Kafka is null, instead of showing the default column value. Applies to the AVRO, PROTOBUF, and JSON_SR converters.

  • Value Converter Decimal Format: Specifies the JSON or JSON_SR serialization format for Connect DECIMAL logical type values with two allowed literals: BASE64 to serialize DECIMAL logical types as base64 encoded binary data, and NUMERIC to serialize DECIMAL logical type values in JSON or JSON_SR as a number representing the decimal value.

  • Key Converter Schema ID Serializer: The class name of the schema ID serializer for keys. This is used to serialize schema IDs in the message headers.

  • Value Converter Connect Meta Data: Enables the Connect converter to add its metadata to the output schema. Applies to Avro converters.

  • Value Converter Value Subject Name Strategy: Determines how to construct the subject name under which the value schema is registered with Schema Registry.

  • Key Converter Key Subject Name Strategy: Determines how to construct the subject name for key schema registration.

  • Value Converter Schema ID Serializer: The class name of the schema ID serializer for values. This is used to serialize schema IDs in the message headers.

Auto-restart policy

  • Enable Connector Auto-restart: Enables the auto-restart behavior of the connector and its task in the event of user-actionable errors. Defaults to true, enabling the connector to automatically restart in case of user-actionable errors. Set this property to false to disable auto-restart for failed connectors. If disabled, you must manually restart the connector.

Connector Details

  • Timestamp granularity for timestamp columns: Define the granularity of the timestamp column. CONNECT_LOGICAL (default) represents timestamp values using Kafka Connect built-in representations.

  • Numeric Mapping: Maps NUMERIC values to integral or decimal types by precision and, optionally, scale. Use none to represent all NUMERIC columns using Connect’s DECIMAL logical type.

  • Database timezone: Name of the JDBC timezone the connector uses when querying with time-based criteria. Defaults to UTC.

  • Transaction Isolation Level: Isolation level determines how transaction integrity is visible to other users and systems.

  • Initial timestamp: The epoch timestamp used for initial queries that use timestamp criteria. Set to -1 to use the current time as the initial timestamp. If not specified, the connector retrieves all data.

  • Delay interval (ms): How long to wait after a row with a certain timestamp appears before including it in the result.

  • Date Calendar System: Which calendar system to use when interpreting DATE or TIMESTAMP columns. LEGACY (default) matches historical behavior. PROLEPTIC_GREGORIAN matches modern java.time semantics.

  • Poll interval (ms): Set the time in milliseconds to wait for new change events when no data is returned. Default is 500 ms.

  • Max rows per batch: Maximum number of rows to include in a single batch when polling for new data.

Database details

  • Enable High-Throughput Reads: Streams large result sets using the BigQuery Storage Read API instead of paged REST responses. Requires the service account to have the bigquery.readsessions.create permission, for example roles/bigquery.readSessionUser.

  • High-Throughput Activation Ratio: The number of result pages a query result must exceed before the driver switches from the standard REST API to the faster Storage Read API. The result must also exceed bigquery.high.throughput.min.table.size rows. Only applies when bigquery.high.throughput.enabled is true.

  • High-Throughput Min Table Size: The minimum number of rows a query result must exceed before the driver switches from the standard REST API to the Storage Read API. The result must also exceed the High-Throughput Activation Ratio page threshold. Only applies when bigquery.high.throughput.enabled is true.

  • Query Location: The location where BigQuery runs query jobs. BigQuery determines this automatically if you leave it blank.

  • Query Labels: Labels attached to query jobs for cost attribution, as a comma-separated list of key=value pairs, for example team=data-eng,env=prod. None by default.

  • Use Query Cache: Whether to allow BigQuery to serve query results from its cache. Enabled by default.

  • Job Timeout (seconds): Seconds after which the server cancels a query job. 0 (default) means no timeout.

  • Job Creation Mode: Controls whether BigQuery runs each poll as a tracked query job. A query job has a job ID, appears in BigQuery job history with full statistics, and can be monitored or cancelled. 1 (JOB_CREATION_REQUIRED) always creates a job; choose this when you need per-query auditing, monitoring, or job history. 2 (JOB_CREATION_OPTIONAL, default) lets BigQuery skip job creation for short, fast queries and return results directly, which lowers latency and overhead for frequent small polls (BigQuery still creates a job for queries that are not eligible). Use 2 for typical high-frequency polling where per-query job tracking is not needed.

  • DATETIME Mapping: How the connector maps BigQuery DATETIME columns. string (the default) maps them to a string. Because DATETIME has no timezone, this avoids implying one. timestamp maps them to the Connect Timestamp logical type, reading the naive value as UTC at millisecond precision, regardless of the worker timezone or the configured db.timezone.

  • Maximum Bytes Billed: Maximum bytes a query job can bill before BigQuery cancels it without charging. 0 (default) leaves this unset, so the connector applies no per-query cap.

  • Query Retry Attempts: Number of times to retry a query after a retryable SQL error (for example a timed-out job) before failing the task. Defaults to 10. A bounded value stops an expensive or repeatedly failing query from being retried indefinitely and silently accruing BigQuery cost. -1 retries without limit (not recommended).

Transforms

Data encryption

  • Enable Client-Side Field Level Encryption for data encryption. Specify a Service Account to access the Schema Registry and associated encryption rules or keys with that schema. For more information on CSFLE or CSPE setup, see Manage encryption for connectors.

For all property values and definitions, see Configuration Properties.

  • Click Continue.

Based on the number of topic partitions you select, the connector recommends a number of tasks.

  1. To change the number of recommended tasks, enter the number of tasks for the connector to use in the Tasks field.

  2. Click Continue.

  1. Verify the connection details.

  2. Click Launch.

    The status for the connector should go from Provisioning to Running.

Step 5: Check the Kafka topic

After the connector is running, verify that records from your BigQuery tables are populating the Kafka topic.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect Usage Examples section.

Using the Confluent CLI

Complete the following steps to set up and run the connector using the Confluent CLI.

Note

Make sure you have all your prerequisites completed.

Step 1: List the available connectors

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: List the connector configuration properties

Enter the following command to show the connector configuration properties:

confluent connect plugin describe <connector-plugin-name>

The command output shows the required and optional configuration properties.

Step 3: Create the connector configuration file

Create a JSON file that contains the connector configuration properties. The following example shows required and optional connector properties.

{
  "name": "BigQuerySource_0",
  "config": {
    "topic.prefix": "bigquery_",
    "connector.class": "BigQuerySource",
    "name": "BigQuerySource_0",
    "output.data.format": "AVRO",
    "output.key.format": "STRING",
    "kafka.auth.mode": "KAFKA_API_KEY",
    "kafka.api.key": "<my-kafka-api-key>",
    "kafka.api.secret": "<my-kafka-api-secret>",
    "authentication.method": "Google cloud service account",
    "bigquery.credentials.json": "<my-gcp-service-account-json>",
    "catalog.pattern": "<gcp-project-id>",
    "schema.pattern": "<bigquery-dataset>",
    "table.include.list": ".*",
    "mode": "bulk",
    "tasks.max": "1"
  }
}

Note the following property definitions:

  • "name": Sets a name for your new connector.

  • "connector.class": Identifies the connector plugin name.

  • "topic.prefix": Prefix to prepend to table names to generate the name of the Kafka topic to publish data to. When you use a custom query, the connector uses this value as the full topic name.

  • "output.data.format": Sets the output Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON. You must have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF.

  • "output.key.format": Sets the output Kafka record key format. Valid entries are AVRO, JSON_SR, PROTOBUF, JSON, or STRING (default). You must have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF.

  • "kafka.auth.mode": Identifies the connector authentication mode you want to use. There are two options: SERVICE_ACCOUNT or KAFKA_API_KEY (the default). To use an API key and secret, specify the configuration properties kafka.api.key and kafka.api.secret, as shown in the example configuration (above). To use a service account, specify the Resource ID in the property kafka.service.account.id=<service-account-resource-ID>. To list the available service account resource IDs, use the following command:

    confluent iam service-account list

    For example:

    confluent iam service-account list

   Id     | Resource ID |       Name        |    Description
+---------+-------------+-------------------+-------------------
   123456 | sa-l1r23m   | sa-1              | Service account 1
   789101 | sa-l4d56p   | sa-2              | Service account 2

  • "authentication.method": How the connector authenticates to BigQuery. Valid entries are Google cloud service account (default) and Google service account impersonation.

  • "bigquery.credentials.json": The contents of the downloaded Google Cloud service account JSON key file, as an escaped JSON string (not a file path). The service account must have read permissions for BigQuery. Required when "authentication.method" is set to Google cloud service account.

  • "provider.integration.id": The ID of your Google Cloud provider integration. Required when "authentication.method" is set to Google service account impersonation. For setup, see Manage a Google Cloud Provider Integration.

  • "catalog.pattern": The ID of the GCP project containing the BigQuery dataset to read from.

  • "schema.pattern": The BigQuery dataset to read tables from.

  • "table.include.list": A comma-separated list of regular expressions that match the fully qualified names of tables to copy, in the form project.dataset.table. To copy every table in the dataset, set this to .*.

  • "mode": The mode for updating a table on each poll. Valid entries are bulk (default), timestamp, incrementing, and timestamp+incrementing.

  • "tasks.max": Enter the number of tasks for the connector to use.

Note

To enable CSFLE or CSPE for data encryption, specify the following properties:

  • csfle.enabled: Flag to indicate whether the connector honors CSFLE or CSPE rules.

  • sr.service.account.id: A Service Account to access the Schema Registry and associated encryption rules or keys with that schema.

For more information on CSFLE or CSPE setup, see Manage encryption for connectors.

SMTs: For more information about adding SMTs using the Confluent CLI, see Single Message Transformations.

For all property values and descriptions, see Configuration Properties.

Step 4: Load the configuration file and create the connector

Enter the following command to load the configuration and start the connector:

confluent connect cluster create --config-file <file-name>.json

For example:

confluent connect cluster create --config-file gcp-bigquery-source-config.json

Example output:

Created connector BigQuerySource_0 lcc-ix4dl

Step 5: Check the connector status

Enter the following command to check the connector status:

confluent connect cluster list

Example output:

ID          |          Name         | Status  | Type
+-----------+-----------------------+---------+--------+
lcc-ix4dl   | BigQuerySource_0      | RUNNING | source

Step 6: Check the Kafka topic

After the connector is running, verify that records from your BigQuery tables are populating the Kafka topic.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect Usage Examples section.

Configuration Properties

Use the following configuration properties with the fully managed connector. For self-managed connector property definitions and other details, see the connector docs in Self-managed connectors for Confluent Platform.

How should we connect to your data?

name

Sets a name for your connector.

  • Type: string

  • Valid Values: A string at most 64 characters long

  • Importance: high

Kafka Cluster credentials

kafka.auth.mode

Kafka Authentication mode. It can be one of KAFKA_API_KEY or SERVICE_ACCOUNT. It defaults to KAFKA_API_KEY mode, whenever possible.

  • Type: string

  • Valid Values: SERVICE_ACCOUNT, KAFKA_API_KEY

  • Importance: high

kafka.api.key

Kafka API Key. Required when kafka.auth.mode==KAFKA_API_KEY.

  • Type: password

  • Importance: high

kafka.service.account.id

The Service Account that will be used to generate the API keys to communicate with Kafka Cluster.

  • Type: string

  • Importance: high

kafka.api.secret

Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.

  • Type: password

  • Importance: high

Schema Config

schema.context.name

Add a schema context name. A schema context represents an independent scope in Schema Registry. It is a separate sub-schema tied to topics in different Kafka clusters that share the same Schema Registry instance. If not used, the connector uses the default schema configured for Schema Registry in your Confluent Cloud environment.

  • Type: string

  • Default: default

  • Importance: medium

How do you want to prefix table names?

topic.prefix

Prefix to prepend to table names to generate the name of the Apache Kafka® topic to publish data to.

  • Type: string

  • Importance: high

How should we configure the topic(s)?

topic.creation.topic_prefix_match.partitions

Number of partitions for Kafka topics auto-created by the connector for topics whose name starts with topic.prefix. Kafka preserves message ordering only within a partition. Records without a key are distributed across all partitions, so any value above 1 removes ordering guarantees for them. Keep this at 1 (the default) if you need all records in a topic delivered in strict order.

  • Type: int

  • Default: 1

  • Valid Values: [1,…]

  • Importance: high

topic.creation.topic_prefix_match.cleanup.policy

Cleanup policy applied to Kafka topics auto-created by the connector for topics whose name starts with topic.prefix. delete ages records out based on retention settings; compact retains only the latest value per key and requires every record to have a non-null key.

  • Type: string

  • Default: delete

  • Valid Values: compact, compact,delete, delete

  • Importance: high

Storage

topic.creation.topic_prefix_match.retention.ms

Time-based retention, in milliseconds, applied to Kafka topics auto-created by the connector for topics whose name starts with topic.prefix. Use -1 for infinite retention.

  • Type: long

  • Default: 604800000 (7 days)

  • Valid Values: [-1,…]

  • Importance: high

topic.creation.topic_prefix_match.retention.bytes

Size-based retention, in bytes, applied to Kafka topics auto-created by the connector for topics whose name starts with topic.prefix. Use -1 for unlimited size.

  • Type: long

  • Default: -1

  • Valid Values: [-1,…]

  • Importance: high

GCP credentials

provider.integration.id

Select an existing integration that has access to your resource. In case you need to integrate a new Google Service Account, use provider integration

  • Type: string

  • Importance: high

authentication.method

Select how you want to authenticate with BigQuery.

  • Type: string

  • Default: Google cloud service account

  • Valid Values: Google cloud service account, Google service account impersonation

  • Importance: high

bigquery.credentials.json

GCP service account JSON file with read permissions for BigQuery.

  • Type: password

  • Importance: high

How should we connect to your BigQuery project?

catalog.pattern

The ID of the GCP project containing the BigQuery dataset to read from.

  • Type: string

  • Importance: high

schema.pattern

The BigQuery dataset to read tables from.

  • Type: string

  • Importance: high

Connector Details

table.include.list

A comma-separated list of regular expressions that match the fully-qualified names of tables to copy, in the form project.dataset.table. For example, my-project.my_dataset.orders,my-project.my_dataset.customer.*. Only tables in the project and dataset selected above are considered. Table names are case sensitive. Not required if a custom Query Config is provided instead. To copy every table in the dataset, set this to .* explicitly.

  • Type: list

  • Importance: medium

table.exclude.list

A comma-separated list of regular expressions that match the fully-qualified names of tables not to copy, in the form project.dataset.table. For example, my-project.my_dataset.orders_archive,my-project.my_dataset.tmp_.*. This only applies to the tables filtered using the include list. Table names are case sensitive.

  • Type: list

  • Importance: medium

mode

The mode for updating a table each time it is polled. bulk performs a bulk load of the entire table on each poll. timestamp uses a timestamp (or timestamp-like) column to detect new and modified rows. incrementing uses a strictly incrementing column on each table to detect only new rows. timestamp+incrementing uses both a timestamp column and a strictly incrementing column.

  • Type: string

  • Default: bulk

  • Importance: medium

timestamp.columns.mapping

A comma-separated list of fully-qualified table name to timestamp columns mappings. When you specify multiple timestamp columns, the connector uses the COALESCE SQL function to determine the effective timestamp for a row. Use the format table1:[col1|col2],table2:[col3], where table1/table2 can also be a pattern (regular expression) matched against the fully-qualified table names.

  • Type: list

  • Importance: medium

incrementing.column.mapping

A comma-separated list of fully-qualified table name to incrementing column mappings. Use the format table1:col1,table2:col2, where table1/table2 can also be a pattern (regular expression) matched against the fully-qualified table names.

  • Type: list

  • Importance: medium

query

If specified, the connector uses this custom SQL query instead of Tables Included/Tables Excluded to select rows, allowing joins or a subset of columns. Only SELECT statements are supported; don’t add a WHERE/ORDER BY/GROUP BY clause in incremental modes, since the connector appends those itself. STRUCT/ARRAY columns fall back to JSON strings in this mode, since their real nested schema can’t be resolved without a table name.

  • Type: password

  • Default: [hidden]

  • Importance: medium

timestamp.column.name

Comma-separated timestamp column(s) used for incremental polling when a Custom Query is set. Use this instead of the table-to-timestamp mappings in custom query mode, since there is no table to match. When you specify multiple columns, the connector uses COALESCE to determine the effective timestamp.

  • Type: list

  • Importance: medium

incrementing.column.name

Incrementing column used for incremental polling when a Custom Query is set. Use this instead of the table-to-incrementing mappings in custom query mode, since there is no table to match.

  • Type: string

  • Importance: medium

timestamp.granularity

Define the granularity of the timestamp column. CONNECT_LOGICAL (default) represents timestamp values using Kafka Connect built-in representations.

  • Type: string

  • Default: CONNECT_LOGICAL

  • Importance: low

numeric.mapping

Maps NUMERIC values to integral or decimal types by precision and, optionally, scale. Use none to represent all NUMERIC columns using Connect’s DECIMAL logical type.

  • Type: string

  • Default: none

  • Importance: low

db.timezone

Name of the JDBC timezone the connector uses when querying with time-based criteria. Defaults to UTC.

  • Type: string

  • Default: UTC

  • Importance: medium

transaction.isolation.mode

Isolation level determines how transaction integrity is visible to other users and systems.

  • Type: string

  • Default: DEFAULT

  • Valid Values: DEFAULT, READ_COMMITTED, READ_UNCOMMITTED, REPEATABLE_READ, SERIALIZABLE

  • Importance: medium

timestamp.initial

The epoch timestamp used for initial queries that use timestamp criteria. Set to -1 to use the current time as the initial timestamp. If not specified, the connector retrieves all data.

  • Type: long

  • Valid Values: [-1,…]

  • Importance: medium

timestamp.delay.interval.ms

How long to wait after a row with a certain timestamp appears before including it in the result.

  • Type: int

  • Default: 0

  • Valid Values: [0,…]

  • Importance: high

date.calendar.system

Which calendar system to use when interpreting DATE or TIMESTAMP columns. LEGACY (default) matches historical behavior. PROLEPTIC_GREGORIAN matches modern java.time semantics.

  • Type: string

  • Default: LEGACY

  • Importance: medium

poll.interval.ms

Frequency in ms to poll for new data in each table.

  • Type: int

  • Default: 5000 (5 seconds)

  • Valid Values: [100,…]

  • Importance: high

batch.max.rows

Maximum number of rows to include in a single batch when polling for new data.

  • Type: int

  • Default: 100

  • Valid Values: [1,…,5000]

  • Importance: low

table.types

By default, the connector detects only tables of type TABLE. This configuration accepts a comma-separated list of table types to extract.

  • Type: list

  • Default: TABLE

  • Importance: medium

Database details

bigquery.high.throughput.enabled

Streams large result sets using the BigQuery Storage Read API instead of paged REST responses. Requires the service account to have the bigquery.readsessions.create permission, for example roles/bigquery.readSessionUser.

  • Type: boolean

  • Default: false

  • Importance: medium

bigquery.high.throughput.activation.ratio

The driver’s HighThroughputActivationRatio is a result-page count, not a ratio. The driver switches from the standard REST API to the faster Storage Read API only when a query’s result spans more than this many pages and also has more than bigquery.high.throughput.min.table.size rows. Only applies when bigquery.high.throughput.enabled is true.

  • Type: int

  • Default: 2

  • Importance: low

bigquery.high.throughput.min.table.size

Row count above which the driver switches from REST to the Storage Read API. Only applies when bigquery.high.throughput.enabled is true.

  • Type: int

  • Default: 10000

  • Importance: low

bigquery.query.location

The location where BigQuery runs query jobs. BigQuery determines this automatically if you leave it blank.

  • Type: string

  • Default: “”

  • Importance: low

bigquery.query.labels

Labels attached to query jobs for cost attribution, as a comma-separated list of key=value pairs, for example team=data-eng,env=prod. None by default.

  • Type: string

  • Default: “”

  • Importance: low

bigquery.query.use.cache

Whether to allow BigQuery to serve query results from its cache. Enabled by default.

  • Type: boolean

  • Default: true

  • Importance: low

bigquery.query.job.timeout.seconds

Seconds after which the server cancels a query job. 0 (default) means no timeout.

  • Type: int

  • Default: 0

  • Importance: low

bigquery.query.job.creation.mode

Controls whether BigQuery runs each poll as a tracked query job. A query job has a job ID, appears in BigQuery job history with full statistics, and can be monitored or cancelled. 1 (JOB_CREATION_REQUIRED) always creates a job; choose this when you need per-query auditing, monitoring, or job history. 2 (JOB_CREATION_OPTIONAL, default) lets BigQuery skip job creation for short, fast queries and return results directly, which lowers latency and overhead for frequent small polls (BigQuery still creates a job for queries that are not eligible). Use 2 for typical high-frequency polling where per-query job tracking is not needed.

  • Type: int

  • Default: 2

  • Importance: low

bigquery.datetime.mapping

How the connector maps BigQuery DATETIME columns. string (the default) maps them to a string. Because DATETIME has no timezone, this avoids implying one. timestamp maps them to the Connect Timestamp logical type, reading the naive value as UTC (independent of worker timezone) at millisecond precision.

  • Type: string

  • Default: string

  • Importance: low

bigquery.query.maximum.bytes.billed

Maximum bytes a query job can bill before BigQuery cancels it without charging. 0 (default) leaves this unset, so the connector applies no per-query cap.

  • Type: long

  • Default: 0

  • Valid Values: [0,…]

  • Importance: low

query.retry.attempts

Number of times to retry a query after a retryable SQL error (for example a timed-out job) before failing the task. Defaults to 10. A bounded value stops an expensive or repeatedly failing query from being retried indefinitely and silently accruing BigQuery cost. -1 retries without limit (not recommended).

  • Type: int

  • Default: 10

  • Valid Values: [-1,…]

  • Importance: low

Output messages

output.data.format

Sets the output Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF

  • Type: string

  • Default: JSON

  • Importance: high

output.key.format

Sets the output Kafka record key format. Valid entries are AVRO, JSON_SR, PROTOBUF, STRING or JSON. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF

  • Type: string

  • Default: STRING

  • Valid Values: AVRO, JSON, JSON_SR, PROTOBUF, STRING

  • Importance: high

Number of tasks for this connector

tasks.max

Maximum number of tasks for the connector.

  • Type: int

  • Valid Values: [1,…]

  • Importance: high

Additional Configs

header.converter

The converter class for the headers. This is used to serialize and deserialize the headers of the messages.

  • Type: string

  • Importance: low

producer.override.compression.type

The compression type for all data generated by the producer. Valid values are none, gzip, snappy, lz4, and zstd.

  • Type: string

  • Importance: low

producer.override.linger.ms

The producer groups together any records that arrive in between request transmissions into a single batched request. More details can be found in the documentation: https://docs.confluent.io/platform/current/installation/configuration/producer-configs.html#linger-ms.

  • Type: long

  • Valid Values: [100,…,1000]

  • Importance: low

value.converter.allow.optional.map.keys

Allow optional string map key when converting from Connect Schema to Avro Schema. Applicable for Avro Converters.

  • Type: boolean

  • Importance: low

value.converter.auto.register.schemas

Specify if the Serializer should attempt to register the Schema.

  • Type: boolean

  • Importance: low

value.converter.connect.meta.data

Allow the Connect converter to add its metadata to the output schema. Applicable for Avro Converters.

  • Type: boolean

  • Importance: low

value.converter.enhanced.avro.schema.support

Enable enhanced schema support to preserve package information and Enums. Applicable for Avro Converters.

  • Type: boolean

  • Importance: low

value.converter.enhanced.protobuf.schema.support

Enable enhanced schema support to preserve package information. Applicable for Protobuf Converters.

  • Type: boolean

  • Importance: low

value.converter.flatten.unions

Whether to flatten unions (oneofs). Applicable for Protobuf Converters.

  • Type: boolean

  • Importance: low

value.converter.generate.index.for.unions

Whether to generate an index suffix for unions. Applicable for Protobuf Converters.

  • Type: boolean

  • Importance: low

value.converter.generate.struct.for.nulls

Whether to generate a struct variable for null values. Applicable for Protobuf Converters.

  • Type: boolean

  • Importance: low

value.converter.int.for.enums

Whether to represent enums as integers. Applicable for Protobuf Converters.

  • Type: boolean

  • Importance: low

value.converter.latest.compatibility.strict

Verify latest subject version is backward compatible when use.latest.version is true.

  • Type: boolean

  • Importance: low

value.converter.object.additional.properties

Whether to allow additional properties for object schemas. Applicable for JSON_SR Converters.

  • Type: boolean

  • Importance: low

value.converter.optional.for.nullables

Whether nullable fields should be specified with an optional label. Applicable for Protobuf Converters.

  • Type: boolean

  • Importance: low

value.converter.optional.for.proto2

Whether proto2 optionals are supported. Applicable for Protobuf Converters.

  • Type: boolean

  • Importance: low

value.converter.scrub.invalid.names

Whether to scrub invalid names by replacing invalid characters with valid characters. Applicable for Avro and Protobuf Converters.

  • Type: boolean

  • Importance: low

value.converter.use.latest.version

Use latest version of schema in subject for serialization when auto.register.schemas is false.

  • Type: boolean

  • Importance: low

value.converter.use.optional.for.nonrequired

Whether to set non-required properties to be optional. Applicable for JSON_SR Converters.

  • Type: boolean

  • Importance: low

value.converter.wrapper.for.nullables

Whether nullable fields should use primitive wrapper messages. Applicable for Protobuf Converters.

  • Type: boolean

  • Importance: low

value.converter.wrapper.for.raw.primitives

Whether a wrapper message should be interpreted as a raw primitive at root level. Applicable for Protobuf Converters.

  • Type: boolean

  • Importance: low

errors.tolerance

Use this property if you would like to configure the connector’s error handling behavior. WARNING: This property should be used with CAUTION for SOURCE CONNECTORS as it may lead to dataloss. If you set this property to ‘all’, the connector will not fail on errant records, but will instead log them (and send to DLQ for Sink Connectors) and continue processing. If you set this property to ‘none’, the connector task will fail on errant records.

  • Type: string

  • Default: none

  • Importance: low

key.converter.key.schema.id.serializer

The class name of the schema ID serializer for keys. This is used to serialize schema IDs in the message headers.

  • Type: string

  • Default: io.confluent.kafka.serializers.schema.id.PrefixSchemaIdSerializer

  • Importance: low

key.converter.key.subject.name.strategy

How to construct the subject name for key schema registration.

  • Type: string

  • Default: TopicNameStrategy

  • Importance: low

key.converter.replace.null.with.default

Whether to replace fields that have a default value and that are null to the default value. When set to true, the default value is used, otherwise null is used. Applicable for JSON Key Converter.

  • Type: boolean

  • Default: true

  • Importance: low

key.converter.schemas.enable

Include schemas within each of the serialized keys. Input message keys must contain schema and payload fields and may not contain additional fields. For plain JSON data, set this to false. Applicable for JSON Key Converter.

  • Type: boolean

  • Default: false

  • Importance: low

value.converter.decimal.format

Specify the JSON/JSON_SR serialization format for Connect DECIMAL logical type values with two allowed literals:

BASE64 to serialize DECIMAL logical types as base64 encoded binary data and

NUMERIC to serialize Connect DECIMAL logical type values in JSON/JSON_SR as a number representing the decimal value.

  • Type: string

  • Default: BASE64

  • Importance: low

value.converter.flatten.singleton.unions

Whether to flatten singleton unions. Applicable for Avro and JSON_SR Converters.

  • Type: boolean

  • Default: false

  • Importance: low

value.converter.ignore.default.for.nullables

When set to true, this property ensures that the corresponding record in Kafka is NULL, instead of showing the default column value. Applicable for AVRO,PROTOBUF and JSON_SR Converters.

  • Type: boolean

  • Default: false

  • Importance: low

value.converter.reference.subject.name.strategy

Set the subject reference name strategy for value. Valid entries are DefaultReferenceSubjectNameStrategy or QualifiedReferenceSubjectNameStrategy. Note that the subject reference name strategy can be selected only for PROTOBUF format with the default strategy being DefaultReferenceSubjectNameStrategy.

  • Type: string

  • Default: DefaultReferenceSubjectNameStrategy

  • Importance: low

value.converter.replace.null.with.default

Whether to replace fields that have a default value and that are null to the default value. When set to true, the default value is used, otherwise null is used. Applicable for JSON Converter.

  • Type: boolean

  • Default: true

  • Importance: low

value.converter.schemas.enable

Include schemas within each of the serialized values. Input messages must contain schema and payload fields and may not contain additional fields. For plain JSON data, set this to false. Applicable for JSON Converter.

  • Type: boolean

  • Default: false

  • Importance: low

value.converter.value.schema.id.serializer

The class name of the schema ID serializer for values. This is used to serialize schema IDs in the message headers.

  • Type: string

  • Default: io.confluent.kafka.serializers.schema.id.PrefixSchemaIdSerializer

  • Importance: low

value.converter.value.subject.name.strategy

Determines how to construct the subject name under which the value schema is registered with Schema Registry.

  • Type: string

  • Default: TopicNameStrategy

  • Importance: low

Auto-restart policy

auto.restart.on.user.error

Enable connector to automatically restart on user-actionable errors.

  • Type: boolean

  • Default: true

  • Importance: medium

Next Steps

For an example that shows fully managed Confluent Cloud connectors in action with Confluent Cloud for Apache Flink, see the Cloud ETL Demo. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.

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