MySQL ingestion connector

Preview

The MySQL connector is in Public Preview. Contact your Databricks account team to request access.

This page helps you understand the MySQL ingestion workflow, including the factors that determine your setup approach and the steps involved for different user personas.

What to know before you start

Topic	Why it matters
Databricks user persona	The workflow depends on your Databricks user persona: Single-user: An admin user configures the source database and creates a Unity Catalog connection, an ingestion gateway, and an ingestion pipeline. Multi-user: An admin user configures the source database and creates a connection for non-admin users to create gateway-pipeline pairs with.
Deployment environment	The source database configuration depends on the MySQL deployment environment (Amazon RDS, Aurora MySQL, Azure Database for MySQL, Google Cloud SQL for MySQL, or MySQL on EC2).
Authentication method	The steps to create a connection depend on the authentication method you choose.
Interface	The steps to create a connection, a gateway, and a pipeline depend on the interface.
Ingestion frequency	The pipeline schedule depends on your latency and cost requirements.
Common patterns	Depending on your ingestion needs, the pipeline might use configurations like history tracking, column selection, and row filtering. Supported configurations vary by connector. See Feature availability.

Start ingesting from MySQL

The following table provides an overview of the end-to-end MySQL ingestion workflow, based on user type:

User	Steps
Admin	Configure MySQL for ingestion into Databricks. Either: Use Catalog Explorer to create a connection so that non-admins can create gateway-pipeline pairs. See MySQL. Use the data ingestion UI to create a connection, a gateway, and a pipeline. See Create a MySQL ingestion pipeline.
Non-admin	Use any supported interface to create a gateway and a pipeline. See Create a MySQL ingestion pipeline.

Feature availability

Feature	Availability
UI-based pipeline authoring	Supported
API-based pipeline authoring	Supported
Declarative Automation Bundles	Supported
Incremental ingestion	Supported
Unity Catalog governance	Supported
Orchestration using Databricks Workflows	Supported
SCD type 2	Supported
API-based column selection and deselection	Supported
API-based row filtering	Not supported
Automated schema evolution: New and deleted columns	Supported
Automated schema evolution: Data type changes	Not supported
Automated schema evolution: Column renames	Supported Treated as a new column (new name) and deleted column (old name).
Automated schema evolution: New tables	Supported If you ingest the entire schema. See the limitations on the number of tables per pipeline.
Maximum number of tables per pipeline	250

Authentication methods

Authentication method	Availability
OAuth U2M	Not supported
OAuth M2M	Not supported
OAuth (manual refresh token)	Not supported
Basic authentication (username/password)	Supported
Basic authentication (API key)	Not supported
Basic authentication (service account JSON key)	Not supported