Migrating from Apache Airflow to Dagster
This blog post guides data engineers through migrating from Apache Airflow to Dagster, weighing the pros and cons of both platforms. It highlights the developer-friendly nature of Dagster, its support for data quality checks and strong data lineage, while noting the challenges of a less mature community and learning a new system.
So, you're a seasoned data engineer, and you've been using Apache Airflow for quite a while. If you're anything like me, you've had moments of brilliance with it, but also moments of, well, not-so-brilliance. Airflow has its merits, undoubtedly. But, in recent times, I've made the switch to Dagster, and folks, I haven't looked back.
The World of Apache Airflow
Before we get into the Dagster fanfare, let's level-set and give Airflow its due. It's been the go-to for a lot of us for years. Apache Airflow, an open-source platform to programmatically author, schedule, and monitor workflows, has its own set of attractions. The robustness of Airflow is defined by the level of complexity it can handle. It’s wonderful at scheduling complex jobs and has a healthy and growing community (GitHub - apache/airflow).
Airflow's key highlights:
- Strong Community Support: Airflow has a wide community of users and contributors who continue to improve functions and offer solutions to common issues.
- Dynamic Pipeline Creation: With Airflow, you can create dynamic pipelines via its Python-oriented nature.
- Scalable: Airflow has good scalability and can handle a large amount of data volume.
That said, it's not without its disadvantages:
- High Complexity: Despite its power, Airflow's user interface is complex and can be quite difficult to get used to, particularly for newcomers.
- Maintenance: Airflow requires regular monitoring and maintenance for effective functioning.
- Lacks Strong Data Lineage Support: Airflow lacks a concrete data lineage solution, which could be a pain when it comes to tracing data issues.
Dagster is a relatively new kid on the block, but it's rapidly becoming a powerful alternative for data orchestration (GitHub - dagster-io/dagster). With a strong emphasis on development, testing, deployment, and monitoring, it's designed for building and managing ETL pipelines, machine learning pipelines, and similar computational workloads.
Here's why I'm championing Dagster:
- Developer-Focused: Dagster is built with a developer-first mindset. It provides excellent visibility into pipeline execution, configurable execution environments, and local development modes.
- Data Quality Checks: It includes native support for common patterns in ETL and ML workloads, like automated testing and data quality checks, which can save a lot of development time.
- Strong Data Lineage Support: Dagster has a solid data lineage system, aiding the tracking of data issues across a pipeline.
- Flexible Deployment Options: Dagster can run either locally for testing and development or on a server or in a containerized environment for deployment.
Despite these strengths, Dagster isn't without its own set of drawbacks:
- Less Mature Community: As a newer platform, Dagster's community is smaller and less mature, meaning less support and fewer external resources.
- New System Learning Curve: Switching to Dagster will involve learning a new system with its own unique principles and architecture.
Switching from Airflow to Dagster
Here's how my journey from Airflow to Dagster went.
The Migration Process
For me, migration started with becoming familiar with the Dagster system. The Dagster documentation provides an in-depth guide to understanding the basic structure, architecture, and working of Dagster.
The real action starts with code migration. Here is a basic Dagster pipeline for your reference
For detailed migration process, I suggest you check out Airflow migration to Dagster.
Deploying Dagster involves running the dagit service and the Dagster daemon process. Check out the Dagster Deployment Overview for comprehensive information.
What I've Gained
Transitioning to Dagster was a calculated risk that paid off. The development and testing process has become streamlined and the data quality checks have drastically reduced the time I spend troubleshooting.
The built-in tools for managing configurations, maintaining data quality, and visualizing data lineage are a godsend. Plus, the local development mode makes it easy to test pipelines before deployment.
What I've Lost
However, there's no denying that Airflow's larger and mature community was a significant advantage. Often, when I stumbled upon an issue in Airflow, someone in the community had already encountered it and provided a solution.
Migrating to a new system is never a cakewalk. Learning the ins and outs of Dagster took time and patience.
To sum it up, both Airflow and Dagster have their own sets of strengths and weaknesses. Dagster is a solid choice for those prioritizing development environment, data quality, and visibility into pipeline execution.
If you're considering making the switch, take time to familiarize yourself with Dagster, check out its documentation, and, most importantly, understand its architectural principles.
Remember, every tool has its place, and the choice depends on your unique requirements. Happy data engineering!