Hands-On Salesforce Data Cloud

Chapter 8. Data Transformations

In Chapter 7, we focused our attention on data modeling. As part of our learning, we created the foundations of a successful strategy for data mapping to the Customer 360 standard data model by undertaking the appropriate profiling and relevant classification of the source data. In this chapter, we’ll create any necessary streaming data transformations (which we call data transforms, for short) to clean and normalize the source data before we map the DLOs to their respective DMOs. We’ll also learn how to create batch data transforms for more complex transformations.

Throughout the chapter, we’ll discuss the following menu options:

• Data Transforms
• Data Lake Objects
• Data Streams (for mapping)
• Data Explorer (for validation)

Getting Started

This section contains the required prework as well as some important things you should know before you get started.

Prework

Data transformations are the beginning of the hands-on tasks needed to implement your data model, so you’ll need to have completed all data modeling and data model planning first. Prior to creating data transforms, connectors must be set up and data streams established for data to be ingested into Data Cloud.

What You Should Know

Most of the DLOs you’ll be reviewing and working with were likely automatically created from DSOs when you set up your data streams and leveraged the Salesforce data bundles. It is possible, however, to create a DLO manually, which we will discover as the first ...