Okay. Oracle has launched new data integration platform in OCI.

What is ODI in OCI? It is a fully managed, serverless, native cloud service that helps you with common EL-T tasks such as ingesting data from different sources, cleansing, transforming, and reshaping that data, and then efficiently loading it to target data sources on Oracle Cloud Infrastructure. Sounds cool right?

You may perform one or more of the following roles:

  • Administrators: Oversee, manage, and monitor life cycle management and security policies for the service.
  • Data engineers and ETL developers: Develop, build, and test data integration solutions.
  • Operators: Manage, monitor, and diagnose data integration executions.
Overview of Data Integration
Administrators, data engineers, ETL developers, and operators are among the different types of data professionals who use Oracle Cloud Infrastructure Data Integration.

But it does not really matter which role are you on, Data Integration is extremely easy. If you don't want to read following nag-nag description and explanation, just jump to bottom and watch the video.

So let's dive into some of concepts quickly, I didn't mention all of it, because what you would need at minimum is following:

  • Workspace: it is a container for all Data Integration resources
  • Project: A container for resources, such as tasks or data flows.
  • Application: as name suggests, it is a container for tasks that have been published along with their   dependencies.
  • Data Assets: Represents a data source, a physical store containing the data source's metadata and connection details.
  • Connection: it is needed to create your source/target/staging locations and associated to one data asset. A data asset can have more than one connection.
  • Task: Important stuff basically, you can create Integration Task or Data Loader Tasks in your project. Once you created a task, you can publish into an Application to test them or roll them out to production.
  • Data flows : A design-time resource that defines the flow of data and any operations on the data between the source and target systems. You will add a data flow to an Integration Task only.
  • Task run: A run-time artifact that represents the execution of an Integration or Data Loader task.

You basically create your workspace, create your project or choose default seeded project, create data assets with connection to your sources and targets. Once you done these steps, fun part begins. There are two types of tasks, and I will show how to load your data using Data Loader task in my video.

Data Loader Task helps you load diverse data set into data lakes, data marts, and data warehouses. You can create a Data Loader Task from the Console or by using the API, and configure transformations to cleanse and process data while it gets loaded into a target data asset. Another thoughts? You can maybe even use DataLoader task as a quick, small, simple migration method too. Nobody said it's not possible.

Happy Integrating! If you want more cooler demo and discussion about service please watch following recording.

It's edited by Alex. Thanks.

Data Integration demo within team.

Good luck and join our discord community https://discord.gg/nrF3HuF