Building Your Own Data Pipeline Tool From Scratch - Should You Do It?

Alright, let's start out with the fact that there are some distinctions between an orchestrator and a data pipeline tool.

But for many data teams, they use Airflow as a tool to either act as their data pipeline tool, or the tool that orchestrates all the other tools that make up their data pipeline.

As you start building your first data pipelines, you’ll slowly realize you need to address a growing number of recurring issues. Maybe you implement a component or process that tracks what jobs are running, a scheduler, a set of generic scripts to run transforms and data ingestion, or even some form of UI.

Before you know it, you’ve pieced together something that looks like Airflow. Something that goes beyond just being a set of data pipelines but starts looking like an orchestrator.

Surprisingly (or maybe not), I’ve seen countless homegrown orchestration/data pipeline systems. Often, it feels like, given enough time, the team might build its own Airflow-esque solution.

So should you build it?

If you prefer reading, here is a written version of this - https://seattledataguy.substack.com/p...

Also, if you're looking for an orchestrator, consider checking out Mage!
https://www.mage.ai/

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About me:
I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consult on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data.

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