dbt and Airflow: The Natural Pair for Data Analytics

dbt and Airflow are cornerstone tools in the modern data stack, each excelling in different areas of data workflows. Together, dbt and Airflow provide the flexibility and scalability needed to handle complex, end-to-end workflows.

This article delves into what dbt and Airflow are, why they work so well together, and the challenges teams face when managing them independently. It also explores how Datacoves offers a fully managed solution that simplifies operations, allowing organizations to focus on delivering actionable insights rather than managing infrastructure.

What is dbt?

dbt (Data Build Tool) is an open-source analytics engineering framework that transforms raw data into analysis-ready datasets using SQL. It enables teams to write modular, version-controlled workflows that are easy to test and document, bridging the gap between analysts and engineers.

• Adoption: With over 40,000 companies using dbt, the majority rely on open-source dbt Core available to anyone.

• Key Strength: dbt empowers anyone with SQL knowledge to own the logic behind data transformations, giving them control over cleansing data and delivering actionable insights.

• Key Weakness: Teams using open-source dbt on their own must manage infrastructure, developer environments, job scheduling, documentation hosting, and the integration of tools for loading data into their data warehouse.  

What is Airflow?

Apache Airflow is an open-source platform designed to orchestrate workflows and automate tasks. Initially created for ETL processes, it has evolved into a versatile solution for managing any sequence of tasks in data engineering, machine learning, or beyond.

• Adoption: With over 37,000 stars on GitHub, Airflow is one of the most popular orchestration tools, seeing thousands of downloads every month.

• Key strength: Airflow excels at handling diverse workflows. Organizations use it to orchestrate tools like Azure Data Factory, Amazon Glue, and open-source options like dlt (data load tool). Airflow can trigger dbt transformations, post-transformation processes like refreshing dashboards, or even marketing automation tasks. Its versatility extends to orchestrating AI and ML pipelines, making it a go-to solution for modern data stacks.

• Key weakness: Scaling Airflow often requires running it on Kubernetes for its scalable nature. However, this introduces significant operational overhead and a steep learning curve to configure and maintain the Kubernetes cluster.

Why dbt and Airflow are a natural pair

Stitch together disjointed schedules

While dbt excels at SQL-based data transformations, it has no built-in scheduler, and solutions like dbt Cloud’s scheduling capabilities are limited to triggering jobs in isolation or getting a trigger from an external source. This approach risks running transformations on stale or incomplete data if upstream processes fail. Airflow eliminates this risk by orchestrating tasks across the entire pipeline, ensuring transformations occur at the right time as part of a cohesive, integrated workflow.

Tools like Airbyte and Fivetran also provide built-in schedulers, but these are designed for loading data at a given time and optionally trigger a dbt pipeline. As complexity grows and organizations need to trigger dbt pipelines after data loads via different means such as dlt and Fivetran, then this simple approach does not scale. It is also common to trigger operations after a dbt pipeline and scheduling using the data loading tool will not handle that complexity. With dbt and Airflow, a team can connect the entire process and assure that processes don’t run if upstream tasks fail or are delayed.

Airflow centralizes orchestration, automating the timing and dependencies of tasks—extracting and loading data, running dbt transformations, and delivering outputs. This connected approach reduces inefficiencies and ensures workflows run smoothly with minimal manual intervention.

Handle complexity with ease

Modern data workflows extend beyond SQL transformations. Airflow complements dbt by supporting complex, multi-stage processes such as integrating APIs, executing Python scripts, and training machine learning models. This flexibility allows pipelines to adapt as organizational needs evolve.

Airflow also provides a centralized view of pipeline health, offering data teams complete visibility. With its ability to trace issues and manage dependencies, Airflow helps prevent cascading failures and keeps workflows reliable.

By combining dbt’s transformation strengths with Airflow’s orchestration capabilities, teams can move past fragmented processes. Together, these tools enable scalable, efficient analytics workflows, helping organizations focus on delivering actionable insights without being bogged down by operational hurdles.

Managed Airflow and managed dbt in Datacoves

In our previous article, we discussed building vs buying your Airflow and dbt infrastructure. There are many cons associated with self-hosting these two tools, but Datacoves takes the complexity out of managing dbt and Airflow by offering a fully integrated, managed solution. Datacoves has given many organizations the flexibility of open-source tools with the freedom of managed tools. See how we helped Johnson and Johnson MedTech migrate to our managed dbt and airflow platform.

Managed dbt

Datacoves offers the most flexible and robust managed dbt Core environment on the market, enabling teams to fully harness the power of dbt without the complexities of infrastructure management, environment setup, or upgrades. Here’s why our customers choose Datacoves to implement dbt:

• Seamless VS Code environment: Users can log in to a secure, browser-based VS Code development environment and start working immediately. With access to the terminal, VS Code extensions, packages, and libraries, developers have the full power of the tools they already know and love—without the hassle of managing local setups. Unlike inflexible custom IDEs, the familiar and flexible VS Code environment empowers developers to work efficiently. Scaling and onboarding new analytics engineers is streamlined so they can be productive in minutes.

• Optimized for dbt development: Datacoves is designed to enhance the dbt development experience with features like SQL formatting, autocomplete, linting, compiled dbt preview, curated extensions, and python libraries. It ensures teams can develop efficiently and maintain high standards for their project.

• Effortless upgrade management: Datacoves manages both platform and version upgrades. Upgrades require minimal work from the data teams and is usually as simple as “change this line in this file.”  

• CI/CD accelerators: Many teams turn to Datacoves after outgrowing the basic capabilities of dbt Cloud CI. Datacoves integrates seamlessly with leading CI/CD tools like GitHub Actions, GitLab Workflows, and Jenkins. But we don’t stop at providing the tools—we understand that setting up and optimizing these pipelines requires expertise. That’s why we work closely with our customers to implement robust CI/CD pipelines, saving them valuable time and reducing costs.

• dbt best practices and guidance: Datacoves provides accelerators and starting points for dbt projects, offering teams a strong foundation to begin their work or improve their project following best practices. This approach has helped teams minimize technical debt and ensure long-term project success. As an active and engaged member of the dbt community, Datacoves stays up to date on new improvements and changes. Supporting customers by providing expert guidance on required updates and optimizations.

Managed Airflow

Datacoves offers a fully managed Airflow environment, designed for scalability, reliability, and simplicity. Whether you're orchestrating complex ETL workflows, triggering dbt transformations, or integrating with third-party APIs, Datacoves takes care of the heavy lifting by managing the Kubernetes infrastructure, monitoring, and scaling. Here’s what sets Datacoves apart as a managed Airflow solution:

• Multiple Airflow environments: Teams can seamlessly access their hosted Airflow UI and easily set up dedicated development and production instances. Managing secrets is simplified with secure options like Datacoves Secrets Manager or AWS Secrets Manager, enabling a streamlined and secure workflow without the logistical headaches.

• Observability: With built-in tools like Grafana, teams gain comprehensive visibility into their Airflow jobs and workflows. Monitor performance, identify bottlenecks, and troubleshoot issues with ease—all without the operational overhead of managing Kubernetes clusters or runtime errors.

• Upgrade management: Upgrading Airflow is simple and seamless. Teams can transition to newer Airflow versions without downtime or complexity.

• Git sync/S3 sync: Users can effortlessly sync their Airflow DAGs using two popular methods—Git Sync or S3 Sync—without needing to worry about the complexities of setup or configuration.

• My Airflow: Datacoves offers My Airflow, a standalone Airflow instance that lets users instantly test and develop DAGs at the push of a button. This feature provides developers with the freedom to experiment safely without affecting development or production Airflow instances.

• Airflow best practices and guidance: Datacoves provides expert guidance on DAG optimization and Airflow best practices, ensuring your organization avoids costly technical debt and gets it right from the start.

Conclusion

dbt and Airflow are a natural pair in the Modern Data Stack. dbt’s powerful SQL-based transformations enable teams to build clean, reliable datasets, while Airflow orchestrates these transformations within a larger, cohesive pipeline. Their combination allows teams to focus on delivering actionable insights rather than managing disjointed processes or stale data.

However, managing these tools independently can introduce challenges, from infrastructure setup to scaling and ongoing maintenance. That’s where platforms like Datacoves make a difference. For organizations seeking to unlock the full potential of dbt and Airflow without the operational overhead, solutions like Datacoves provide the scalability and efficiency needed to modernize data workflows and accelerate insights.

‍Book a call today to see how Datacoves can help your organization realize the power of Airflow and dbt.