Utilizing a data transformation software such as Coalesce can be an enjoyable experience. Creating data pipelines becomes quick and easy when you have a code-first, GUI based tool to work with. However, implementing Coalesce goes beyond connecting data sources. It requires a solid foundation that enables scalability and efficiency from the start. Coalesce brings speed and flexibility to the modeling process, but without a thoughtful approach, teams can run into challenges with complexity, collaboration, or maintainability.
In this blog, we’ll explain some of the best practices for setting up and managing Coalesce projects so that you can set up your Coalesce environment for success.
What is Coalesce?
Coalesce is a data transformation platform designed for engineers and analysts. It is a hybrid development environment that combines code-first and GUI capabilities, allowing users to build complex transformations visually or write code directly. With Coalesce, users can extend and scale their projects using customizable templates for frequently used transformations and automatically generate standardized, best-practice SQL.
Best Practices for Implementing Coalesce
Standardize Naming
Establishing clear and consistent naming conventions is an important step in building sustainable data pipelines in Coalesce. Projects can quickly scale to include hundreds of nodes, and without standardized naming, it can become difficult to understand pipelines and troubleshoot issues.Â
Whether it’s prefixing table names or aligning workspaces with business domains, a standardized approach creates a common language for your data team. This not only improves readability but also reduces onboarding time for new developers and ensures that anyone working in the platform can easily see where a dataset fits into the broader architecture.
Enforcing naming convention standards is easy with Coalesce’s custom nodes. For example, if we want every fact table in our warehouse to begin with FACT_, we can easily duplicate the fact node that comes with Coalesce out of the box and change it so that the FACT_ is always added to whatever the name of the node is, ensuring that the naming convention is followed:
Utilize Built-in Testing
Coalesce offers built-in testing features right in the data pipeline workflow, which makes it much easier to catch data issues early. You can apply tests at the column level, such as checking for nulls or enforcing uniqueness constraints on individual fields.Â
For more complicated or cross-field checks, Coalesce allows you to define tests using custom SQL. It’s as simple as if the SQL returns results, the test fails. You can also configure whether a failure halts the pipeline, and whether tests run before or after a node runs.
Some best practices when it comes to what to test for include:
Data Types
Check that incoming data matches the data type of the columnMissing or null values
Check for null values, strings that are blank, or integers that are 0Value ranges
Values in the column are within a certain rangeUniqueness
All values in the column are uniqueReferential integrity
Test for foreign key relationships between tables. This is especially useful when using Coalesce with Snowflake, as referential integrity is not enforced for standard tablesCustom business rules
Could be anything else here that your business requires of the data coming through
Version Control and CI/CD
GIT
One of the most important best practices when working in Coalesce is to pair your development with Git. By storing transformation logic in Git, every change is versioned, auditable, and reversible. This provides peace of mind when experimenting with new features and ensures that errors can be rolled back quickly without impacting production pipelines.
Beyond version control, Git opens the door to modern development workflows. Teams can collaborate using branches, submit pull requests for peer reviews, and integrate directly into CI/CD pipelines for automated testing and deployment. This not only reduces the risk of human error but also creates a collaborative framework for building transformations. In fact, Git is such a powerful tool for software development that Coalesce enforces its use of it and registers as a pipeline error if Git is not connected properly.
Environments
Another best practice when working in Coalesce is to take advantage of environments to separate development, testing, and production work. Environments give teams the ability to experiment and build new transformations without risking the stability of production pipelines. Developers can safely iterate in a dev environment, validate logic in test, and only promote changes to production once they’ve been reviewed and approved. This reduces risk and makes the release process more predictable.
Using environments also aligns your data workflows with how modern software is built and deployed. By isolating work into stages, teams can catch issues earlier and ensure that new transformations meet both technical and business requirements before going live.
Closing
Adopting best practices in Coalesce isn’t just about following a checklist; it’s about building a foundation for reliable and collaborative data transformations. Whether it’s leveraging Git for version control, using environments to separate development from production, or implementing testing and CI/CD, each of these best practices helps teams move faster while reducing risk.
By combining Coalesce’s intuitive interface with proven engineering principles, organizations can modernize their data pipelines with confidence. The result is a workflow that’s not only easier to manage day-to-day but also better prepared to grow with the needs of the business.
Ready to unlock these benefits?
To learn how phData can help you implement Coalesce, connect with our team today.
