Overview: dbt and Coalesce
In the realm of data engineering, the quality of your data isn’t just a technical concern—it’s the bedrock of sound business decision-making. Accurate, complete, and consistent data is essential, yet achieving this standard is increasingly challenging in today’s landscape of large and intricate datasets.
That’s where genetic tests come in. These are a blend of algorithms designed to intelligently sift through data, revealing hidden patterns and anomalies. It’s able to flag a wide range of data issues like missing values, duplicate records and inconsistent data formats.
dbt, the renowned data build tool, is a powerhouse for data analysts and engineers, offering a streamlined way to transform, test, and document data within data warehouses. It’s a tool that simplifies complex data tasks into manageable processes.
On the other hand, Coalesce stands out with its robust data transformation capabilities, enabling organizations to efficiently handle their data workflows. It caters to a wide range of users, whether you’re a beginner wanting an intuitive experience or a seasoned data pro craving granular control.
For the newcomer:
- Drag-and-drop visual interface: Build and modify data pipelines with ease, no coding required.
- Simple data exploration: Get familiar with your data quickly through interactive visualizations.
For the data master:
- Precise SQL control: Craft custom transformations with the flexibility and power of SQL.
- Individual column tracking: Deeply understand your data by tracing transformations down to the minute detail.
Coalesce also has impressive data tracking features, allowing you to follow data transformations down to individual columns.
Introducing the Reusable Tests
We have built a set of reusable dbt_utils genetic tests for Coalesce users to automate data quality checks and streamline data validation process. These tests are easy to configure and implement, making them a perfect fit for diverse data environments.
Check them out here: Coalesce Reusable Tests
We’ve listed a few just to give you a glimpse:
equal_rowcount: helps ensure that two tables, `node` and `compare_node`, have the same number of rows. This test is useful during data validation tasks, like verifying data transformations or ensuring consistency between source and target tables.
row_count_delta: Perfect when one table should always be a subset of another.
equality: Asserts the equality of two relations, optionally within specific columns.
… And many more, each with its own syntax, parameters, and usage examples.
1. Through the UI:
- Log into your Coalesce account.
- Navigate to Build Settings > Macros.
- Use the GitHub repo to find your desired test, read the description, and copy the source code.
- Paste this code into the Coalesce UI under Macros.
To Apply Tests on Node Level:
- Navigate to the desired Node.
- Click on the Testing toggle > New Test.
- Follow the GitHub repo’s syntax and examples to apply your tests.
- Clone your Coalesce repo.
- Open with your favorite IDE and locate the
- Under the
macros:item, paste the source code from the GitHub repo.
- Commit and push your changes.
Update and Apply in Coalesce:
- In Coalesce, click the git icon and pull the latest changes.
- Navigate to the Node you wish to test.
- Click Testing > New Test.
- Use the GitHub repo as a reference for syntax and examples when applying tests.
To maximize the benefits of these tests, regularly update the test parameters to align with your evolving data models. Also, integrate these tests into your regular data workflow for continuous quality checks. We hope this was helpful. We’d love to hear your feedback.