Which Salesforce Sandbox Setup Method Is Right for You?
Sandbox seeding vs. manual test data generation—which is better? After all, both are viable ways to set up a development environment in Salesforce. In this blog, we explore the basics of each method, how they work, and what their pros and cons are. This will help you determine which one to use when you’re setting up an environment for your DevOps release pipeline.
Sandbox Seeding vs. Manual Test Data Generation: The Basics
To determine whether to use sandbox seeding vs. manual test data generation, it’s important to first have a good overview of what each method is.
What Is Sandbox Seeding?
Sandbox seeding, also referred to as data seeding, involves instant data replication from your production environment into a sandbox. Because this method makes an accurate copy of the data in production, it’s particularly useful when you’re testing specific scenarios that require real data.
The beauty of sandbox seeding is that you usually only need partial replication because you don’t need a copy of all the data in your production environment. All you need is the right data to create high-quality development environments.
On top of that, because sandbox seeding is achieved with automation, it eliminates the need for manual data entry or complex data mapping. This saves you hours on dev environment setup.
What Is Manual Test Data Provisioning?
Manual test data provisioning is the process of manually creating test data in an environment. It’s often used when you need to create specific test scenarios or when you don’t have access to your production environment.
Manual test data generation requires a lot more effort than sandbox seeding. However, some people find it gives them more flexibility regarding what test data to use. For example, you can create custom records, relationships, and data configurations that apply specifically to a future use case.
How Do Data Seeding and Manual Test Data Creation Work?
In the previous section, we discussed the basics of sandbox seeding and manual test data generation. Now let’s dive deeper into how each method works.
How Does Sandbox Seeding Work?
Salesforce sandbox seeding automation makes it super quick and easy to set up your environments with high-quality sample data. It only takes a few steps and a couple of minutes.
Step 1: Choose Which Data to Replicate in Your Org
Choose a subset of the data to replicate based on the size of the environment you’re setting up and your specific needs. Because you can select the exact data and metadata you need, you’re essentially creating a custom environment that’s identical to part of your production environment.
Step 2: Prepare the Data for Seeding
Before seeding the data, you may need to prepare it. For example, you might want to mask sensitive data for security reasons.
Or you might want to change the data to prevent an unwanted action occurring. Think of things like removing “.com” from email addresses so customers don’t receive test emails by mistake.
Prodly Sandbox Seeding lets you redact test data at a granular level with just a few clicks.
Step 3: Seed the Data Into the Sandbox
Once you’ve prepared the data, it’s time to seed it into the new environment. Make sure you’ve selected the right target environment—and just click “Seed Sandbox.”
It might take a minute or two for your test data to appear, depending on the volume of data you’re seeding. But once it’s there, you can get straight to work!
Sandbox Seeding Works for Scratch Orgs
One of the great things about Prodly Sandbox Seeding is that it works for both metadata and data. That means you can seed all your scratch orgs in just a few clicks—and turn them into production-grade environments!
How Does Manual Test Data Generation Work?
Manually creating test data is much more labor intensive and time consuming. This is because you have to first evaluate what types of data you need and then create it all—by hand.
Step 1: Identify the Types of Data You Need to Provision
Begin by identifying specifically what kinds of data you need to create for your dev environment, such as custom accounts, contacts, or leads.
Step 2: Create the Sample Data
Once you’ve determined what types of data you need and how much, it’s time to start generating it manually in your environment. This process can be time consuming, especially if you need a large volume of test data. It’s even more complicated and drawn out if you have to create a lot of relational data.
Note that if your development environment is a scratch org, you’ll also have to get the right metadata in there. This makes the process even more time consuming.
What Are the Benefits of Sandbox Seeding and Manual Sample Data Creation?
Now we’ve covered how sandbox seeding and manual sample data creation work, let’s explore the benefits of each method.
The Benefits of Sandbox Seeding
- It reduces time and effort. Sandbox seeding eliminates the need for manual data entry or complex data mapping. This saves you tons of time and effort when you’re setting up a new environment.
- It saves time on data preparation. Instead of spending a couple of hours anonymizing or editing data, you can mask and redact with just a few clicks.
- It gives you a production-grade environment with a snapshot replication of the real data you need. With sandbox seeding, you can instantly replicate your production data into your development environment. This gives you a production-grade environment for development, testing, and fixing bugs.
- You can save money on Full Copy sandboxes, Partial Copy sandboxes, and Developer Pro sandboxes. With data seeding, you can make every lower-level environment production grade. This eliminates the need to purchase a sandbox at up to 30 percent of your net spend for a Full Copy.
- It improves code quality. Sandbox seeding ensures the data in your sandbox is identical to that in production. This improves the quality of your code.
- It increases data consistency. With sandbox seeding, you can easily maintain data consistency across multiple environments. As a result, everyone on the team is working with the same data.
- It delivers a high ROI. Sandbox seeding saves you time on non-development work. It improves the quality of your code, and it streamlines the release management process. All these factors add up to a considerable return on your investment.
The Benefits of Manual Sample Data Creation
With manual sample data creation, you have the following two benefits:
- It can be more flexible. Manual test data creation can sometimes offer more flexibility if you need very specific data or configurations.
- It doesn’t require an up-front investment. With manual test data generation, you don’t have to budget for sandbox seeding software. However, when you consider the amount of time and labor involved with creating sample data manually, it’s usually less cost efficient than data seeding.
In the next section, we examine the potential drawbacks of each method to help you make an informed decision.
What Are the Challenges of Sandbox Seeding vs. Manual Test Data Generation?
While sandbox seeding and manual test data generation offer several benefits, there are also some challenges to consider with each.
Potential Challenges of Sandbox Seeding
Consider these potential challenges of sandbox seeding in Salesforce:
- The sandbox seeding software needs to be implemented in your org. Depending on the vendor, the implementation time of data seeding automation can take a long time. Fortunately, with Prodly, you can get up and running within minutes!
- You have to budget for it. Because sandbox seeding apps are third-party apps, you need to take the cost and return on investment into account.
Drawbacks of Manual Test Data Generation
On the other hand, manual test data generation has some significant drawbacks:
- It’s time consuming. Creating test data manually can be a time-consuming process, especially when it comes to large data sets.
- It’s challenging to create high-quality test data. Creating data with a lot of parent-child relationships that closely resemble production data is difficult. This affects the quality of your test data, which compromises the quality of your environment.
- It’s prone to error. When you generate test data manually, it’s all too easy to make mistakes. This can cause issues later on in the development process.
- It’s difficult to scale. The manual effort involved with manual data generation is extremely challenging to scale. So when it comes to large data sets, it can become unmanageable.
Tips for Deciding Between Sandbox Seeding vs. Manual Test Data Generation
The right method for setting up your Salesforce environment will depend on your specific situation. To make the right choice, consider the following factors:
- Budget. If you have limited financial resources, manual test data generation may be the more cost-effective option because you don’t have to invest in sandbox seeding software. However, with sandbox seeding, you can save on Full Copies, Partial Copies, and Developer Pro sandboxes.
- Quality of the code. Sandbox seeding ensures high-quality test data, which in turn boosts the quality of your code.
- Velocity. If you want to ramp up the velocity of your releases, it’s helpful to have the higher-quality code you can build as a result of sandbox seeding.
- Time constraints. If you need to set up a new sandbox quickly, you might want to think about sandbox seeding. For example, Prodly lets you seed up to five environments in one go.
- Data requirements. If you need to test specific scenarios with real data, sandbox seeding could be the best choice. If you need more flexibility regarding the data, manual test data generation may be the way to go.
Ultimately, the choice of sandbox seeding vs. manual test data generation is yours. With the information in this blog, you can determine how the pros and cons of each method stack up against your needs. And that will enable you to maximize your Salesforce environments to support your release management process.
Why do I need high-quality test data in Salesforce?
With high-quality sample data, you can improve the quality of your code and shorten your release cycle. You can also find bugs earlier on in the release process when they’re much less expensive to fix.
Is sandbox seeding a DevOps best practice?
Yes, sandbox seeding is a DevOps best practice because it automates the replication of production data into an environment. Automation is a key principle of DevOps. Learn more about DevOps principles.