Data Engineering is an integral part of the data ecosystem, enabling businesses to draw insights and make decisions based on data.
One of the emerging tools in this space is DBT (data build tool). In this article, we delve into DBT’s importance and functionalities.
What is DBT in data engineering?
Full form of DBT is "data build tool". It's a software tool that allows data engineers and analysts to transform and model data in the data warehouse. Unlike traditional ETL (Extract, Transform, Load) processes where transformation happens before loading, DBT fits into the ELT paradigm (Extract, Load, Transform). This means data is extracted, loaded into a data warehouse, and then transformed using DBT.
Why use DBT for ETL?
The shift from ETL to ELT, and consequently the rise of DBT, has several reasons:
- Performance: Modern data warehouses like Snowflake, BigQuery, and Redshift are optimized for heavy computation. By transforming data directly in the warehouse, we leverage their computational power, thus ensuring transformations are swift and efficient.
- Version Control: DBT uses SQL and Jinja2 (a templating engine), allowing for version control of your transformations. This ensures transparency and traceability of changes made to data models.
- Development and Testing: DBT has built-in functionalities for testing and documentation. Data teams can ensure data quality, maintain accurate documentation, and avoid technical debt.
- Collaboration: DBT allows data teams to collaborate effectively. Analysts can share transformation logic, build upon each other’s work, and maintain consistency across the organization.
What exactly does DBT do?
DBT’s primary function is to run transformations on the data inside your data warehouse. It allows you to:
- Define & Run Transformations: Write SQL-based transformations that convert raw data into analytics-ready tables.
- Test: Implement data tests to ensure data quality and consistency.
- Document: Maintain and generate documentation about your data models, ensuring transparency and clarity for all stakeholders.
- Version Control: Work on data models in a similar fashion to software development, leveraging version control tools like Git.
What is DBT vs SQL?
DBT and SQL aren't opposing or different entities. Instead, DBT utilizes SQL for its operations. The distinction lies in their functionalities:
- SQL: A language for managing and querying relational databases.
- DBT: A tool that uses SQL (enhanced with Jinja2 templating) to define, document, and test data transformations in modern data warehouses.
With DBT, you write SQL code to define your transformations, but DBT adds structure, testing, and documentation capabilities that raw SQL doesn't offer.
Is DBT tool open source?
Yes, DBT is open source. The core functionality of DBT is available under the Apache 2.0 license. This means you can use, modify, and distribute it. However, there's also a commercial version called DBT Cloud that offers additional features, like a web-based interface, scheduling, and more.
Is DBT hard to learn?
The difficulty in learning DBT largely depends on your familiarity with SQL and data warehousing concepts. If you're well-versed in SQL, the learning curve is relatively shallow. DBT extends SQL with Jinja2 templating, so there's a bit to learn there, but it's straightforward.
For newcomers to SQL, there might be a steeper learning curve. However, the extensive documentation, active community, and plethora of resources make it easier for anyone to get started.
DBT has made a significant mark in the data engineering world by facilitating efficient and effective ELT processes. Its ability to seamlessly integrate SQL-based transformations, testing, documentation, and collaboration makes it a favored tool for many data teams. Whether you're a seasoned data engineer or just starting out, DBT offers a robust platform to transform and model your data.