Here’s what Stitch got right (and what it means for data engineers today):
Stitch's data engineering approach focuses on:
What’s your go-to for extraction — Stitch, Fivetran, Airbyte, or something homegrown? stitch data integration platforms company data engineering
Why do data engineering teams choose Stitch over competitors like Fivetran or Airbyte? It often comes down to three specific differentiators:
This separation of concerns is vital. Stitch handles the plumbing, allowing the data engineer to focus on the data modeling (dbt) rather than API maintenance. Here’s what Stitch got right (and what it
Stitch is ideal for:
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 handles the plumbing, allowing the data engineer
One of the most significant contributions Stitch made to the data engineering community is . Singer is an open-source standard for writing scripts that move data between databases and APIs. Taps: These extract data from sources. Targets: These send data to destinations.