Talend Cloud Data Preparation Examples
The use cases described in this document show several way of using Talend Cloud Data Preparation to perform formatting or cleansing operations to your data.
- The Customers dataset is used as source for the Standardize preparation. It shows how you can standardize state names, delete invalid records, consolidate phone numbers, change their format as well as date formats, or use a masking function to protect data. Finally, the Magic Fill function is used to convert dates to the corresponding day of the week.
- The Build_email preparation uses the Marketing Leads and the Emails Reference datasets and joins them in order to recreate emails addresses from customers names, company names, and their corresponding domain addresses.
In addition to these preparations that are available directly within the application, you can download additional datasets and use them to complete the following examples:
- Based on the CRM_export.csv dataset, build a preparation to
consolidate in a new column all the mobile phones or landline phone numbers of your
customers to make sure you have at least a working number for each.
See Consolidating a list of phone numbers coming from a CRM solution.
- The marketing_leads.zip and emails_reference.zip datasets can be used together in
the same preparation via a lookup operation to join the information about companies
domain addresses and recreate entire email addresses.
See Recreating email addresses before uploading them to a marketing solution.
- With the HRMS_export.xlsx dataset, learn how to change date
formats and extract specific strings of characters from any column.
See Cleansing data coming from a human resource management system.
- The customer_contact_data.csv dataset can be improved by
cleaning the empty and invalid datasets, and removing unnecessary blank spaces in
text records.
See Preparing client data to upload it to a marketing solution.
- With the video_customers.xlsx dataset, follow a scenario to
combine filters and create "if" conditions to isolate the data on a specific
customers group.
See Using filters to create "if" conditions on customer data.
- The car_dealership.xlsx dataset is used as base for a
preparation illustrating how to use regular expression to match data that cannot be
selected with usual filters.
See Using regular expressions and filters to create "or" conditions on customer data.
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