Stitch Data Integration Platforms Company Data Engineering High Quality -

Here’s what Stitch got right (and what it means for data engineers today):

#DataEngineering #ELT #StitchData #DataIntegration #DataStack stitch data integration platforms company data engineering

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. Here’s what Stitch got right (and what it

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. What’s your go-to for extraction — Stitch, Fivetran,

What’s your go-to for extraction — Stitch, Fivetran, Airbyte, or something homegrown?

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