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Guide to migrating from Apache Airflow to Dagster, covering pros, cons, data quality checks, lineage, and learning curve.
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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.
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:
That said, it's not without its disadvantages:
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:
Despite these strengths, Dagster isn't without its own set of drawbacks:
Here's how my journey from Airflow to Dagster went.
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.
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.
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!
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