Reusable data pipelines are good for you
If you CAN create them 😊
Your data team is probably burning a lot of time and money rebuilding the same pipelines over and over again on different projects. 🔥💸
Here's what I learned from Max Beauchemin, Apache Airflow's creator about why this keeps happening (and how to fix it):
Think about it: How many times has your team rebuilt engagement metrics? Cohort analysis? A/B testing logic? Every time feels like starting from scratch, but it's probably 80% identical to something you've done before.
The solution isn't another framework or tool. It's changing your approach:
Start with high-quality templates that cover 80% of the use cases
Let engineers efficiently customize the final 20%
This allows you to meet your business's unique requirements without reinventing the wheel every single time.
Action steps for your team:
Do a "pipeline redundancy audit" in the next 30 days
Identify your top 3 most-rebuilt patterns
Create internal reference templates for your biggest time sinks
Target 40%+ faster build times by year-end
Your engineering budget will thank you. Your data team also. 😎