Open-Source vs. Commercial Software: How To Better Solve Data Integration

Open-Source vs. Commercial Software: How To Better Solve Data Integration

Breakdown of a DBT Slack debate on the state of open-source alternatives to Fivetran and whether an OSS approach is more relevant than commercial software. In this article, we want to discuss the second point and go over the different points mentioned by each party. The first point will come in another article.

There was an awesome debate on DBT’s Slack last week discussing mainly 2 things:

  1. The state of open-source alternatives to Fivetran
  2. Whether an open-source (OSS) approach is more relevant than a commercial software approach in addressing the data integration problem. 

If you’re already on DBT’s Slack, here is the thread’s URL. Even Fivetran’s CEO was involved in the debate. 

In this article, we want to discuss the second point and go over the different points mentioned by each party. The first point will come in another article. It’s more relevant to discuss whether an OSS approach makes sense before drilling down into the different alternatives.

We’ll go over the main challenges that companies face and see which approach fits best. We’ll call “commercial companies” the ones with a commercial software product, and “OSS companies” the ones with an open-source approach.

TL;DR

To better understand this table, we invite you to read the list of challenges each approach faces below.

open source data science data analysis data integration etl data ingestion elt

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In this article, we want to discuss the second point and go over the different points mentioned by each party. The first point will come in another article. It’s more relevant to discuss whether an OSS approach makes sense before drilling down into the different alternatives.