Connectivity¶
The "self-service" database connectivity solution is a standardized, platform-independent solution that does not require complicated installation and configuration. Once configured, you can read data from production databases for model building and predictions. Connectivity to your data source allows you to quickly train and retrain models on that data, and avoids the unnecessary step of exporting data from your database to a CSV file for ingest into DataRobot. It allows access to more diverse data, which results in more accurate models.
Data store vs. data connection
In DataRobot, data store and data connection are used interchangeably, and both refer to where your data is stored and managed as well as the dynamic link to that data. You will see both terms in the UI.
Connector vs. Driver
Each data store connects to DataRobot through either a driver or a connector, and is indicated in the tables for supported unstructured and structured data stores.
| Type | Description | Recommended use |
|---|---|---|
| Connector | DataRobot connects through the data store's native API using an integration built and maintained by DataRobot. The connection is defined by connection fields instead of a JDBC URL, supports authentication methods such as OAuth and service principals, and, for many data stores, supports unstructured data. Connectors require a registered data store. | Recommended when you want to configure connections and credentials once and reuse them. They are also the preferred option when credentials or other sensitive configuration are stored in DataRobot and should not be embedded in your code. Connectors are also recommended for all agentic workflows due to lower latency and unstructured data support. |
| Legacy JDBC Driver | DataRobot connects using the JDBC driver provided by the database vendor. The connection is defined by a JDBC URL and JDBC parameters, authenticates with basic credentials, and supports structured (tabular) data. For Self-Managed AI Platform installations, an administrator uploads and manages the driver files. Requires a registered data store. | Recommended only for custom connection types that are not supported by connectors or a DataRobot-managed JDBC driver. |
| DataRobot-managed JDBC driver | DataRobot ships and manages the driver set as part of the install (bundled in the browser-jdbc service). There are three main differences from the legacy driver:
This driver currently supports Postgres, MySQL, SQL Server, BigQuery, Snowflake, Databricks, Trino, SAP Datasphere drivers. |
Recommended when you are prepared to manage the connection configuration and credentials directly in the your code. |
The following sections provide reference content that supports working with and understanding connectivity in DataRobot:
| Topic | Description |
|---|---|
| Connectivity workflows | Connect to and manage connections across DataRobot. |
| Supported structured data stores | View the data stores currently supported by DataRobot for structured data ingest, as well as a list of deprecated data stores. |
| Supported unstructured data stores | View the data stores currently supported by DataRobot for unstructured data ingest. |
| Allowed source IP addresses | View a list of allowed source IP addresses for DataRobot. |
| Related reference | |
| Credentials management page | Add and manage securely stored credentials to reuse when accessing secure data sources. |
| Share secure connection configurations | (Admin only) Configure OAuth-based authentication parameters for a data connection, and then securely share them with other users without exposing sensitive fields. |
| Workflows for code-first users | Work with data connections in the Python API. |