# Onboarding data

The first step of creating a data pipeline in a Qlik Open Lakehouse project is onboarding the data. This process involves transferring data from the source and storing datasets in optimized Iceberg tables.

Onboarding is created in a single operation, but performed in two steps. The data source type, either CDC or streaming, determines the tasks in your project:

### CDC sources

* Landing the data

  This involves transferring the data in continuous mini-batches from the on-premises data source to a landing area, using a Landing data task.

  [Landing data from data sources](https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/DataIntegration/Landing/Landing-data-data-sources.htm)

  You can also land data to a lakehouse, where the data is landed to S3 file storage.

  [Landing data to Qlik Open Lakehouse](https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/DataIntegration/Lakehouse/landing-data.htm)

* Storing datasets

  This involves reading the initial load of landing data or incremental loads, and applying the data in read-optimized format using a Storage data task.

  [Storing datasets](https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/DataIntegration/Lakehouse/storing-data.htm)

### Streaming sources

* Landing the data

  This involves continuously streaming the data from the source to a landing area, using a Streaming landing data task.

  [Landing streaming data to Qlik Open Lakehouse](https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/DataIntegration/Lakehouse/landing-data-streaming.htm)

* Storing datasets

  This involves reading the initial load of landing data, and applying the data in read-optimized format using a Storage Transform data task.

  [Storing streaming datasets](https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/DataIntegration/Lakehouse/storing-data-streaming.htm)

## Using onboarded data

When you have onboarded the data, you can use the stored datasets in several ways, including:

* You can use the datasets in an analytics application.

* You can mirror data to one or more cloud data warehouses, including Amazon Redshift and Snowflake, by adding a Mirror data task directly to the Storage data task for CDC sources, or the Streaming Transform task for streaming sources.

  For more information, see [Mirroring data to a cloud data warehouse](https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/DataIntegration/Lakehouse/mirroring-data.htm).

* You can transform data in your cloud data warehouse by creating a cross-project pipeline that consumes data from your onboarding project.