Introduction
In my journey as someone who transitioned from being a data analyst to a data engineer, I had the opportunity to explore and pilot Dataform and DBT for my organization. These tools offered exciting features that bridged the gap between data analysts and software engineers, with a minimal learning curve. In this post, I want to delve into the benefits of using Dataform at a high-level.
What is Dataform?
Before we dive into the advantages of using Dataform, let’s briefly understand what Dataform is. Dataform is a data transformation tool designed to streamline and enhance the data transformation process. Dataform has been acquired by Google. It is particularly well-suited for those who may not have extensive coding experience but want to adopt software engineering best practices in their data work.
You might be thinking… This sounds very familiar to DBT. And yes, DBT and Dataform are very similar tools. Here is my post about doing some comparative analysis for DBT Core and Dataform for the organizations that use GCP and BigQuery.
The High-Level Advantages of Dataform
Dataform brings a host of benefits to the table, even if you’re already utilizing BigQuery for transformations and Cloud Composer for orchestration. Here’s a closer look at why Dataform can be a game-changer for your data stack:
1. Enhanced Efficiency for Data Analysts
Dataform simplifies the data transformation process, making it accessible to individuals with varying levels of software engineering expertise. This empowers data analysts to be more self-sufficient, reducing the workload on your engineering team.
2. Intuitive User Interface with Git Integration
Dataform provides an intuitive user interface that streamlines the development and management of data transformation projects. Its integration with Git for version control ensures that changes are tracked, making collaboration easier and enabling easy rollbacks if needed.
3. Error Detection and Query Syntax Validation
Dataform’s deep integration with BigQuery extends to error detection, including the identification of SQL query syntax errors. This feature can significantly expedite debugging and troubleshooting.
4. Effortless Scheduling
Configuring scheduling for near-real-time data sources becomes effortless with Dataform. Automated workflows can be easily set up, ensuring your data remains up-to-date without manual intervention.
5. Dependency Visualization
Dataform offers a dependency graph that visualizes the interconnections within your data pipeline. This is particularly valuable for complex transformations, helping you understand data flow and dependencies.
6. Documentation, Testing, and Templating
Dataform simplifies documentation, enables data quality testing, and supports JavaScript templating for dynamic transformations. These features enhance the reliability and maintainability of your data processes.
How Dataform Can Benefit Data Organizations
By empowering data analysts and analytics engineers with Dataform, data engineers can focus on more specialized tasks, such as infrastructure management, monitoring, and creating automated processes.
Dataform encourages best practices like version control, documentation, testing, and dependency control, implicitly improving data quality. Proper training and guidance can ensure that users harness these features effectively.
Caveat of Dataform
Dataform primarily focuses on the transformation layer and doesn’t handle extraction or upstream dependency checks like sensors for completed loads, a capability found in tools like Airflow or GCP Cloud Composer. For workflows with batch-loaded upstream dependencies, a combination of Dataform and Airflow is recommended for scheduling. Conversely, for near-real-time tables, Dataform’s Workflow Configurations with specific TAGs provide a straightforward scheduling solution.
One critical question arises: Who should be responsible for creating and managing Airflow DAGs for Dataform – data analysts or data engineers?
In Conclusion
We’ve explored the high-level advantages of incorporating Dataform into your data stack. While this post covered several key benefits, additional features like JavaScript templating and TAG usage can be explored via Google’s official documentation.
If you have any questions or thoughts on Dataform or how it can enhance your data operations, please feel free to reach out. Dataform has the potential to revolutionize how your team approaches data transformation, making it more efficient, collaborative, and reliable.