#DataEngineering #ELT #StitchData #DataIntegration #DataStack

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

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 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.

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.

Stitch Data Integration Platforms Company Data - Engineering

#DataEngineering #ELT #StitchData #DataIntegration #DataStack

Here’s what Stitch got right (and what it means for data engineers today): stitch data integration platforms company data engineering

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 focused on doing one thing well: replicating

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. If you’ve worked in data engineering over the

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.