Dataform vs DBT: 6 Tips To Make The Right Choice
Dataform vs DBT: There are essential elements to consider when making an intelligent choice. We've worked on countless cloud migrations in the last ten years, many on Google Cloud Platform, and there’s a recurring trend regarding the evolution of data management: The company switches to GCP, AWS, or another cloud provider and then starts moving its data to the cloud, and at first, it’s a mess. Queries and views are saved everywhere: in BigQuery and production databases, and I’ve even seen them stored in Notepad!
At first, this solution might be enough. After all, there is typically only one data person in the company at that stage, and value is still needed to justify more significant investments in data. At that step, chron jobs, scheduled queries, and datasets might be enough to organize data.
The company then hires a second data person, or the first one leaves and gets replaced, and quickly, this hacky way of doing things starts creating more problems than benefits. Maintaining queries to produce a few operational metrics on dashboards takes much time.
That’s typically when organization leaders realize that it’s unsustainable. But can we blame the data folks? After all, look at the chart below to understand how dependencies can quickly grow in complexity within your data.
At this point, the company must streamline its SQL data pipelines. But what tools should we use to solve this problem: Dataform, DBT, Stored procedures, or Airflow? Solutions like the last two are often not an option, except if the company is ready to hire a data engineer and sees an ROI going that route. If you’re not familiar with this problem, here’s what we’re trying to accomplish:
Develop and execute SQL workflows for data transformation.
Collaborate with team members on SQL workflow development through Git.
Manage a large number of tables and their dependencies.
Declare source data and manage table dependencies.
View a visualization of the dependency tree of your SQL workflow.
Manage data with SQL code in a central repository.
Reuse code with JavaScript.
Test data correctness with quality tests on source and output tables.
Version control SQL code.
Document data tables inside SQL code.
Choosing between Dataform and DBT is often the most cost-effective and streamlined solution. Both have been designed to handle SQL workflows, collaborate through Git, manage table dependencies, and provide a clear visualization of dependency trees. With them, you can centralize SQL code, enable code reuse, ensure data accuracy through testing, implement version control, and facilitate documentation.
This is an important, strategic decision to improve operational efficiency and data integrity. Having worked extensively with both tools, I will break down in this article the process we went through with our recent client to help them decide.
Dataform vs DBT Tip #1: Core or Managed Solutions
Contrary to popular belief, Dataform is available for BigQuery and supports Snowflake, Redshift, Azure SQL Data Warehouse, and Postgres. However, only the open-source core version of Dataform will support them, not the managed version you find in Google Cloud Platform.
DBT uses Jinja, a Python-based template engine, for templating. Dataform Core, now fully integrated into Google Cloud, was designed with similar principles but uses JavaScript for scripting. Even though the scripting tasks are relatively light with both Dataform core and DBT core, it is often easier for data folks to pick up DBT because of its Pythonic nature.
If you intend to go with a core implementation, then it’s a no-brainer to go with DBT. Both aim to streamline data transformation processes, but DBT Core offers broader warehouse support and a large user community, while Dataform provides tight integration with Google Cloud's ecosystem.
Tip #2: BigQuery Integration
If you are considering using a managed version instead, such as DBT Cloud and GCP Dataform, the question to answer is: Are you using BigQuery? If you are using AWS, Azure, or another cloud provider, it wouldn’t make much sense to use GCP Dataform. However, if you use BigQuery, Dataform integrates natively with the rest of Google tools, making it a no-brainer. Yet, remember that integration with tools outside the Google ecosystem is poor.
Not only that, Dataform offers an IDE out-of-the-box with features such as version control, code validation, etc. The most significant advantage is that the IDE is very familiar to people used to working with BigQuery. Because of that, the learning curve is not as steep compared to working with DBT for those used to BigQuery.
Another advantage of using Dataform when working with BigQuery is that it allows for logging and visualization. This feature benefits beginners, allowing them to see the effects of their transformations directly and understand the data flow more intuitively.
This integration is perfect for novices to understand data transformations and flows effectively. The synergy between Dataform and BigQuery is significant and enhances user experience. It streamlines the transition for teams already familiarized with Google Cloud's suite of tools.
Tip #3: Data Accuracy
Data pipelines will never be 100% accurate, and neither should that be what we aim for. The critical question is: What is the cost of an inaccuracy? For most companies, the cost is simply data on a dashboard that is wrong for a couple of days, which is the time it takes to fix it. At worst, it can impact some operational decisions for a short period.
However, if you’re in an industry where data accuracy is crucial, such as the banking or health sectors, the consequences can be more significant. In that case, having solid testing frameworks for data validation is vital. In that case, DBT is the clear winner. DBT can support complex tests that quickly scale with your infrastructure.
Tip #4: Multi-cloud Infrastructure Adaptability
Our client worked with GCP on this project. However, I have worked with clients using multiple cloud environments, such as GCP and AWS. This situation is often the case with agencies supporting numerous clients.
Data tools' adaptability becomes essential in the context of multi-cloud strategies. DBT's comprehensive support for multiple cloud services and proactive community presents an excellent solution for working with complex cloud architectures, providing seamless integration and operational efficiency across diverse platforms.
Tip #5: Community Support and Ecosystem
A community's size and collaboration energy can significantly influence its value in troubleshooting issues with the tool. DBT's community is active, and its ecosystem is very collaborative. It has a rich repository of plugins, integrations, and educational materials, making it appealing for teams needing a supportive and innovative environment.
If you plan to build complex SQL data pipelines, you’ll have better support from the community with DBT. When searching for issues with Dataform online, it’s rare to find helpful articles or videos for a specific problem.
Tip #6: Scalability and Performance
Anticipating future data operational growth requires a tool that scales efficiently and optimizes performance. DBT's incremental data processing capabilities and Dataform's targeted performance optimizations for GCP highlight their respective strengths.
DBT excels with its incremental data processing feature, which increases efficiency by only transforming new or modified data since the last execution. This method accelerates the data transformation, saves computational resources, and reduces operational costs. It's particularly beneficial for organizations dealing with large, frequently updated datasets across multiple cloud platforms, offering a flexible solution that will scale with your data.
Dataform is amazing within Google Cloud's ecosystem, offering performance optimizations tailored explicitly for GCP. These optimizations include intelligent caching and query optimization to reduce the computational load and cost of data operations in BigQuery. Dataform's integration with Google Cloud tools provides an added layer of efficiency and insight, making it an ideal choice for teams already using Google Cloud services and looking to leverage BigQuery's full potential.
DBT's incremental approach offers flexibility across cloud environments for complex datasets requiring efficient, scalable processing. Contrarily, for those fully invested in Google Cloud, Dataform's specialized optimizations provide seamless integration and improved performance, specifically with BigQuery operations.
Conclusion
We conducted a nuanced comparison with our client, discussing their needs and relying on our expertise in Dataform and DBT. This helped them make informed, strategic decisions aligned with their specific operational needs, accuracy requirements, and infrastructure complexities.
Our client decided to move forward with Dataform. But that is just the beginning. We then have to start creating the actual SQL data pipeline. Not only did we help them with the strategic decision of choosing the right tool, but we are currently helping them with the implementation. Contact us if you are debating between Dataform and DBT, and we will consider your specific requirements and needs to assist you in making the best decision.
