Whether you’re a Salesforce newbie or an experienced user looking to expand your data mastery, you’ve probably heard about Data Loader and its awesome features that can help you manage data within Salesforce orgs. As something that streamlines the process of importing and exporting data – we can’t deny that it’s definitely a powerful tool …
Here are highlights from article How to Download and Install Data Loader: A Tutorial | Salesforce Ben
1. Understanding Data Loader:
– Data Loader is a tool that helps manage data within Salesforce orgs.
– It can perform actions like inserting, updating, deleting, and extracting large volumes of data.
– Makes it easier to work with records in bulk.
2. Accessing the Data Loader Download:
– Download the latest version of Data Loader from the Salesforce Data Loader website.
– Install Zulu OpenJDK before installing Data Loader.
– Windows OS users should download Azul Zulu (.msi file) and MacOS users should download the appropriate installer (.dmg file).
– Extract the downloaded files and run the installation file (installer.bat for Windows OS, installer.command for MacOS).
– Accept the default installation directory name “dataloader” and choose whether to create desktop or Start Menu shortcuts.
3. Launching Data Loader:
– After installation, launch Data Loader from the desktop shortcut or applications menu.
– On the first launch, log in with Salesforce credentials using OAuth if already logged in on a browser, or use Password Authentication.
– Make sure you have API Enabled and appropriate permissions for desired actions.
4. Using Data Loader:
– Once logged in, select the desired action (insert, update, delete, or extract).
– Choose the CSV file containing the data to be processed.
– Map the CSV fields to Salesforce fields.
– Configure settings like the operation mode (insert/update or upsert), batch size, and error handling.
– Click “Next” to preview the data and make any necessary adjustments.
– Click “Finish” to start the data load process.
5. Tips for Data Loader:
– Make sure to review and understand the data mapping before starting the data load process.
– Use smaller batch sizes for large data sets to avoid timeouts.
– Take advantage of the error handling options to handle any data load errors.
– Monitor the data load process and review the success and error files generated.
You can read it here: https://sfdc.blog/dWgSY
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