Stitch focused on doing one thing well: replicating data from 100+ sources to a cloud data warehouse. No pipelines to maintain, no DAGs to debug. That freed engineers to focus on transformation (dbt, SQL, etc.) rather than extraction.
Before Stitch, many teams wrote custom Python/Scala extraction scripts. Stitch (and tools like Fivetran) made extraction a commodity. Today’s data engineers spend less time dealing with API rate limits or pagination — and more time on modeling, governance, and quality. stitch data integration platforms company data engineering
#DataEngineering #ELT #StitchData #DataIntegration #DataStack Stitch focused on doing one thing well: replicating
What’s your go-to for extraction — Stitch, Fivetran, Airbyte, or something homegrown? and the State of Data Engineering
Here’s what Stitch got right (and what it means for data engineers today):
If you’ve worked in data engineering over the last few years, you’ve probably encountered — the extract-and-load platform that helped popularize the "ELT" approach before it became standard.
Here’s a concise, professional LinkedIn post about Stitch as a data integration platform in the context of modern data engineering. Stitch, Data Integration, and the State of Data Engineering
