Welcome to DBT vs Airflow Blog! Apache Airflow and the Data build tool are the two prominent tools that are being used in the data engineering field to make data processing easier and data engineers work faster. In this blog post, we are gonna discuss the core difference between DBT & apache Airflow and exactly where they fit in the data engineering field.
Before getting into the actual details let's understand what is data engineering. Well, data engineering is a process designed to collect, transform, and analyze massive volumes of data. Irrespective of industry, every business is adopting data engineering to make use of data and sustain in the ever-increasing competition. The collected raw data needs to pass through different tools to make it into a usable format. DBT and Apache airflow are both such tools that play a key role in the modern data stack.
Let's understand the one by one clearly
DBT is a SQL-based data transformation framework that makes data transformation more quick, and reliable. It streamlines the process of the data transformation process in an organization. DBT can be deployed on any data warehouse platform and acts as a single source of truth by facilitating easy collaboration between different data engineering teams.
Currently, DBT is available in two options which are DBT cloud (subscription-based) and DBT core (open-source). DBT offers version control features that help data engineers to modify pipelines, update, and track data.
Relate Article: What is DBT
The data build tool is an open-source data transformation tool that uses simple SQL statements to build optimized models. The users can define and build optimized SQL models that can run against any data warehouse or storage system. DBT allows data engineers to build and maintain a simplified data infrastructure that is easy to maintain and scalable.
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DBT offers a rich set of features to make the data transformation process faster and more reliable using simple SQL statements. It reduces logic errors that arise due to the use of copy-and-paste SQL and instead allows users to build and use reusable models.
Below listed are the DBT features, let's explore each one:
DBT supports boilerplate SQL code which makes building materializations simpler than ever. DBT supports building different types of materializations that include tables, views, incremental, and ephemeral.
In DBT you can enhance the performance by combining SQL with Jinja which is a lightweight templating language. Using Jinja along with SQL offers a programming environment and enables you to perform some operations that are not possible alone with SQL, for example controlling data structures.
Data transformation tasks become easier and make sense when it is done using a staged approach. DBT makes use of the “ref function” to execute stage-wise transformations.
DBT offers you the facility to document & share all DBT models you build. You can also auto-generate DBT models if you are using the DBT cloud version.
DBT offers a test option to test the models and assess the generated results. Test feature will help users to make appropriate changes to the models and improves accuracy.
In Data engineering records are mutable and it becomes a challenging task for data engineers when working on reconstructing historic values. DBT offers a Snapshot mechanism using which you can snapshot data in the past.
Apache workflow is an open-source platform that allows businesses to manage complex and huge numbers of workflows with ease. It is one of the widely used data engineering platforms to build, organize and maintain workflows or data pipelines.
Apache Airflow allows users to view code, scheduled tasks, trigger tasks, work in progress, dependencies, DAGs, etc. It becomes easier for data analysts to view any pipeline, and its status and troubleshoot errors if any occur.
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Apache Airflow is been used to build large-scale workflows and it uses DAG chains. All workflows in airflow are built using Python scripts and each Python script can perform the tasks such as data extraction, transformation, and loading. Airflow offers pre-built operators to automate certain tasks such as data loading into a warehouse, etc.
Apache Airflow is an open-source platform developed using Python language. It also offers greater flexibility, and community support for handling any type of workflow.
Apache airflow comes with a simplified interface using which users can interact with any pipeline.
Airflow enables an easy integration process with the top three cloud solution providers which include Azure, GCP, and AWS.
Airflow was built using Python and users are allowed to Python to build customized workflows.
Here is a comparison of key differences between DBT and Airflow:
Data transformations and modeling are the main areas that DBT focuses on. In a SQL-based environment, it enables you to develop data models, transformations, and pipelines. Data warehousing and analytics applications are the main uses of DBT.
The scheduling, orchestration, and management of complicated workflows are all possible using Airflow, a workflow management application. You may define tasks, dependencies, and pipelines with it. Workflows for data engineering and ETL frequently use airflow.
DBT's primary language for defining transformations and models was SQL. With SQL-based databases and warehouses, it integrates quickly.
When compared to DBT, Airflow offers even more flexibility and extension because it uses Python to build workflows and tasks. This allows you to integrate and perform data operations and transformations with other systems while utilizing a wide range of tools and frameworks.
DBT was created specifically for transformations that use SQL. In SQL queries, data transformations are defined. Features for testing, modeling, and documenting SQL-based transformations are supported by DBT.
In contrast, Airflow executes data transformations written in both SQL and other languages. It can integrate with DBT as a task, enabling you to incorporate DBT transformations within the airflow workflows.
DBT supports dependency management between data transformations. Automatically, it builds dependency graphs depending on the connectivity between models, assuring that transformations are implemented in the correct sequence.
On the other hand, airflow also supports dependency management through its task dependencies and DAG (directed acrylic graph) structure. Dependencies can be defined between tasks, including data transformation tasks to assure proper implementation and sequencing.
Let’s compare incremental processing in the context of DBT and airflow:
DBT has native support for incremental processing. It has the capacity to monitor changes to the source data and process only the data that has changed since the last run. This speeds up how quickly transformations are executed.
By including the proper logic in the data transformation tasks, Airflow can manage incremental processing as well. But unlike DBT, it lacks native functionality made especially for incremental processing.
Although both DBT and Apache Airflow are effective tools for orchestrating data workflows, they have different purposes and use different orchestration techniques.
Data transformation and modeling within a data warehouse are the main areas of areas for DBT. It offers a SQL-based interface for specifying data models and transformations. The primary objective of DBT's orchestration capability is to carry out these transformations in a predetermined sequence based on their dependencies. It arranges the models into a directed acyclic graph (DAG) and makes sure that the upstream models run before the dependent models.
On the other hand, Apache Airflow offers an adaptable workflow management platform. You can use it to design, organize, and execute intricate processes that incorporate duties and system dependencies. Airflow can manage tasks like DBT, but its capabilities go beyond data modeling in a data warehouse. Tasks that can be coordinated with Airflow include loading, transforming, and extracting data, to name a few.
Both the DBT (data build tool) and Apache Airflow are open-source, which means that using them is free. But, it's important to remember that maintaining the infrastructure and services required to run these tools results in costs.
For DBT, since it operates primarily within the data warehouse, this would incur the usual costs associated with your chosen data warehouse platform. These costs are frequently influenced by storage consumption, computing power, and data transfer. To fully understand the associated costs, it is important to refer to the documentation of your particular data warehouse provider for price.
The costs for Apache Airflow will also vary depending on the hosting and operating system you choose. Airflow can be installed on various cloud platforms or on-premises infrastructure. The costs will change depending on the number of virtual machine instances, storage capacity, network traffic, and any extra services used inside the architecture.
There are some key differences between DBT and Airflow in terms of support:
The user base of the open-source tool DBT is growing. Support for DBT is accessible through a variety of ways, including:
A robust ecosystem and community exist for Airflow as well. Here are a few of the ways that Airflow can be supported:
Determining whether DBT or Airflow is the best tool depends on your specific requirements. Both DBT and Airflow serve different purposes and excel in other areas. If your primary focus is on data transformation and modeling within a data warehouse and you value features like SQL-based transformations, testing, and documentation, DBT may be the better choice. However, Airflow might be a better choice if you require comprehensive process orchestration features, system integration, and scalability.
Combining the two tools may even make sense in some situations. It is possible to utilize Airflow to organize and control the complete data pipeline or workflow while utilizing DBT for data transformation and modeling within the data warehouse.
Though Airflow and DBT belong to the data engineering technology segment, they differ in their capabilities and responsibilities. Airflow is specialized in managing workflows or data pipelines that perform operations like data extraction, transformation, and loading. Whereas DBT is responsible for only transformation operations. Following are some of the notable differences between these two tools
| Parameter | Data Build Tool (DBT) | Apache Airflow |
| Language | SQL | Python |
| Used For | Data Transformation | ETL / Workflow management |
| Users | Data Analyst | Data Engineer |
| Orchestration | Slower | Faster |
| Pricing | Subscription-Based | Open-Source |
Conclusion:
Well, by now you may have got a fair idea of how airflow and DBT are different and contributing their share in the data engineering segment. Airflow is powerful workflows management platform whereas the data build tool is an advanced SQL-based data transformation tool. I believe this has helped you in clearing the confusion on DBT vs Airflow.
By Tech Solidity
Last updated on July 25, 2023
