Choosing between integration platforms and traditional ETL solutions for data warehouse setup

Other Questions ipaas
1

Our Situation

We’re building a new data infrastructure and need advice on the best approach. Our team has 1 data scientist, 2 analysts, and we’re hiring a data engineer soon.

We have apps running both in the cloud and on-site, so we need good integration capabilities. We’ve been looking at integration platforms like Mulesoft, Workato, and Azure that can also handle data pipeline tasks for our warehouse.

At the same time, we’re checking out dedicated data tools like Databricks, FiveTran, and Snowflake.

What we need to figure out:

  1. What type of solution works best for our data platform needs?

  2. Should we go with an integration platform that our new data engineer might not know yet, or stick with standard data tools?

  3. If we choose the standard route with Azure, Snowflake, or Databricks, should we use their native pipeline features or add FiveTran to the mix?

Any other suggestions would be great too. Thanks for the help!

2

Went through this same decision 18 months ago. Here’s what I learned the hard way: think about maintenance overhead first. We started with a hybrid setup - Snowflake as our warehouse, FiveTran for most connectors, plus some custom pipelines in Azure Data Factory for complex transformations. Big mistake. Too many tools created knowledge silos. When our data engineer quit unexpectedly, we realized how fragmented everything was. Now we’re consolidating around Snowflake’s ecosystem. For your team size, I’d pick one platform and stick with its native features. You’ll hit some feature gaps, but it’s worth it for operational simplicity. FiveTran’s great but adds another vendor and failure point. Since you’re hiring a data engineer anyway, get them involved in the final call - they’re the ones who’ll deal with this stuff every day.