It’s challenging to envision Salesforce operations without the essential component of data. When considering the creation of testing or “dummy” data, the complexity and potential costs involved might not be immediately apparent.
This article will guide you through a comprehensive workshop, providing step-by-step instructions that promise significant enhancements to your data creation process.
Use Cases (Everybody Needs Data)
A data factory comprises a suite of tools designed to facilitate the generation of data to meet various specific requirements:
1. For Apex unit tests, a developer requires data creation tools.
2. To streamline development, a developer needs to populate scratch orgs with hundreds of records.
3. An administrator or architect aims to fill a sandbox with thousands of records for more efficient UAT testing.
4. In testing large data volumes, an architect requires a sandbox with millions of records to assess the performance of SOQL queries.
Using data from the Production org is not advisable, especially in scenarios A and B, due to various reasons. In the context of a well-established Salesforce project with intricate data demands, the Enterprise Data Factory (EDF) serves as the solution. This data factory empowers organizations with complex data requirements, enabling effortless and efficient data generation. It ensures that even individuals with basic programming skills can effectively wield the magic wand to obtain the required data.
The Magic Wand (We Call It Forceea)
Forceea (forsi:a) represents an open-source GitHub initiative that I initiated back in 2017. You might be curious about the capabilities of Forceea, so here’s an overview of its fundamental functionalities (with many more awaiting your exploration):
– Facilitates the insertion, updating, and deletion of records for both standard and custom sObjects, including Big Objects. Operations can be synchronous (for test methods) or asynchronous (ideal for populating your org with extensive record sets).
– Accommodates all field data types, whether standard or custom, and supports the Dadela data generation language for field definitions.
– Automatically defines required fields and adeptly manages record types and field dependencies, including dependent picklists.
– Introduces support for record groups, enabling the seamless manipulation of records through insertion, updating, and deletion.
– Offers templates for constructing a versatile DataFactory class, empowering flexible data generation.
– Incorporates support for variables and functions, adding a layer of sophistication to data manipulation.
– Validates definitions based on the field data type, ensuring accuracy and integrity.
– Features an extended error messaging system for comprehensive debugging.
This article will be a step-by-step workshop, so log into your org (Developer, Scratch, or Sandbox) and install the Forceea unlocked package with:
sf package install -w 10 -p 04t4I0000011V6UQAU -r -o <TargetOrg> or visit the Forceea GitHub page and click the suitable button to install it.
Your Complex Scenario
Imagine you’ve successfully installed Forceea and are all set to generate data for accounts, opportunities, and products, each with specific requirements. For accounts, your criteria include:
– Industry: Choose random values from a picklist.
– Phone: Generate random values in the format “(44) 7911 XXXXXX.”
– Type: Random picklist values, excluding “Prospect” and “Other.”
– Website: Any random URL.
– Site: Match the URL from the Website field.
Furthermore, you aim to obtain:
– Products of the “Service” record type with standard price book entries.
– Opportunities linked to an account, with closing dates set between 30 and 120 days in the future.
– Opportunity products (opportunity line items) featuring standard price book unit prices and random quantities ranging from 1 to 10.
This intricate scenario serves as a robust benchmark for testing the capabilities and flexibility of any EDF in a workshop setting. Get ready; our workshop is about to commence!
What can Forceea do?
This tool facilitates the seamless insertion, updating, and deletion of records, catering to both standard and custom SObjects, either synchronously for test methods or asynchronously for efficiently populating your organization with extensive data.
Moreover, it extends its capabilities to encompass standard or custom Big Objects, offering synchronous and asynchronous operations.
It boasts comprehensive support for all field data types, whether they are standard or custom, and accommodates the Dadela data generation language for defining fields. The tool can automatically discern and define the necessary fields, covering a spectrum of record types and field dependencies, including dependent picklists.
Record groups are effortlessly managed for inserting, updating, and deleting records, providing flexibility and precision in data manipulation.
Templates are available for constructing a DataFactory class, facilitating adaptable data generation, and the integration of DML Optimizer optimizes the efficiency of insert statements.
The tool is adept at handling variables and functions, ensuring versatility in record manipulation.
Validation of definitions is intrinsic to the tool, incorporating checks based on the field data type.
An extended error messaging system enhances user understanding of any encountered issues, contributing to a smoother and more informed data management process.
Summary
Forceea derives its power from the synergistic interplay between the Dadela data generation language and its Templates. This potent combination empowers you to build a resilient, dynamic, and effortlessly scalable Enterprise Data Factory.
This workshop article provides a glimpse into some of Forceea’s numerous features, but a comprehensive exploration of its capabilities can be found in the detailed Forceea Success Guide.
I firmly believe that Forceea stands ready to be your indispensable ally for all your data requirements. I trust you will find immense value in its capabilities!
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