Transform Data with Data Transforms in Autonomous AI Database

Use Data Transforms in Data Studio to design graphical data integration and transformation processes for Oracle Autonomous AI Database. Data Transforms lets you create data loads, data flows, and workflows without writing code.

Data loads move data from source connections to target connections, data flows define how data is moved and transformed between systems, and workflows define the sequence in which data flows, data loads, and other steps are executed.

When you run these objects, the Oracle Data Transforms runtime agent orchestrates the jobs and generates the code for you.

This topic provides an entry point for understanding when and how to use Data Transforms with Autonomous AI Database. For complete reference, see the Data Studio documentation.

When to use Data Transforms

Use Data Transforms when you need a visual, low-code way to move, transform, schedule, and monitor data integration work for Autonomous AI Database.

For example, use it to:

Create data loads to load multiple data entities from a source connection to a target connection.
Create data flows to move and transform data between systems by using components such as joins, filters, mappings, constraints, aggregates, expressions, lookups, sets, sorts, and other database functions.
Create workflows to organize multiple data flows, data loads, variables, and workflows into a controlled execution sequence.
Schedule data flows or workflows to run at a specified time or interval.
Monitor execution status for data loads, data flows, and workflows from the status panel or Jobs pages.
Use variables to parameterize data flows and workflows.
Use machine learning models in data flows, including prediction-model steps that write output to a target table.
Export and import Data Transforms objects, such as projects, connections, data loads, data flows, workflows, and schedules, between environments.

Use other Autonomous AI Database loading or transformation options, such as SQL, PL/SQL, DBMS_CLOUD, Oracle Data Pump, or external orchestration tools, when you need a fully scripted, administrative, or application-driven workflow instead of a visual Data Studio workflow. See Load Data into Autonomous AI Database to learn about these data-loading options.

How Data Transforms works with Autonomous AI Database

Data Transforms is available from Database Actions in Data Studio for Autonomous AI Database. You can open it from the Data Transforms card on the Database Actions page or from the Data Studio menu in the Database Actions selector. To use the Data Transforms tool, sign in as the ADMIN user or as a user who has the DATA_TRANSFORM_USER role.

Data Transforms is also available as a separate OCI Marketplace listing called Data Integrator: Web Edition. If a Data Transforms instance from OCI Marketplace is registered with an Autonomous AI Database, the Data Transforms card in Database Actions continues to open that registered Marketplace instance. To use the embedded Data Transforms instance instead, unregister the Marketplace instance.

When you open Data Transforms for the first time, you must provide database user credentials. After the service starts, the Data Transforms home page opens, and you can view the Autonomous AI Database connection from the Connections tab.

See Access Oracle Data Transforms From Data Studio for access steps, required role, first-time sign-in behavior, and Marketplace registration behavior.

Before you begin

Before creating transformations, verify that:

Your user has access to Database Actions and has the DATA_TRANSFORM_USER role, unless you are using the ADMIN user.
Required source and target systems are reachable from the OCI network used by Autonomous AI Database. Data Transforms connections are used to connect to supported technologies reachable from your OCI network. See Work with Connections for more details.
If Autonomous AI Database uses a private endpoint, private data sources must be reachable from clients in the same VCN. See Data Transform Notes for notes for private data sources and long-running jobs.
You understand which Data Transforms environment you are using: embedded Data Transforms in Autonomous AI Database, a registered Data Transforms instance, or the Marketplace Data Integrator: Web Edition listing. Some documented features are marked with badges that indicate where they apply.

Open Data Transforms

To open Data Transforms:

Open Database Actions for your Autonomous AI Database.
Select Data Studio.
Select Data Transforms.
Sign in as ADMIN or as a database user with the DATA_TRANSFORM_USER role.
If prompted, provide the database user credentials required to use the Data Transforms connection.
Use the Data Transforms home page to create or manage connections, projects, data loads, data flows, workflows, jobs, variables, and import or export operations.

For complete task steps, use the Data Studio documentation links in the following section.

What you can do in Data Transforms

Connections: Create and manage connections to source and target systems, including supported database, application, service, Object Storage, REST, and custom JDBC-based connections.

See Work with Connections for connection creation, supported connection types, custom connectors, Object Storage connections, REST connections, and related connection tasks.
Projects: Organize data flows, workflows, variables, data loads, and jobs into logical groups.

See Work with Projects for guidance with organizing Data Transforms work into project containers.
Data Loads: Move multiple data entities from a source connection to a target connection and select load actions such as recreate, truncate, append, incremental append, or incremental merge where supported.

See Create and Run Data Loads to learn how to create data loads, select source and target connections, select load processing options, and run data loads.
Data Entities: Represent tabular source or target structures that can be imported, created, used in data flows, and inspected.

See Work with Data Entities for information about import, create, and inspect data entities used by data loads and data flows.
Data Flows: Build visual transformation logic by connecting sources, targets, and transformation components on a design canvas.

See About Data Flows to learn more about creating and editing visual data flows, adding components, mapping columns, validating, and executing data flows.
Workflows: Orchestrate multiple data flows, data loads, variables, and workflows in a defined sequence.

See Introduction to Workflows for information about creating and running workflows that sequence data flows, data loads, variables, and other workflow steps.
Schedules: Schedule data flows or workflows for later or recurring execution.

See Schedule Data Flows or Workflows to learn how to schedule transformation work for timed execution.
Jobs and monitoring: Track running and completed executions, review job details, rerun jobs, delete jobs, and inspect error details.

See Monitor Status of Data Loads, Data Flows, and Workflows for guidance with using the status panel and job links to monitor execution.
Variables: Store values that can be substituted into data flows and workflows at execution time.

See Create and Use Variables to learn how to create variables and use them in data flows and workflows.
Machine Learning models: Create ML model data entities and use ML model steps in data flows.

See Use Machine Learning (ML) Models for guidance with creating ML model data entities and using ML models in data flows.
Export and import: Move Data Transforms metadata between environments by exporting and importing objects through Object Storage.

See Export and Import Objects for information about moving Data Transforms objects between environments.