Airbyte is an open-source data integration engine that helps you consolidate your data in your data warehouses, lakes and databases.

Airbyte

<@U01AS8LGX41> <@U01AB6V6NMQ> <@U01A81VHD45> I tried to google but didn't find good answer for it. Do you have blog post that compares Airbyte and Pipelinewise? Assuming that customer only needs sources and destinations that are already supported by Pipelinewise what argument would you use in favor of Airbyte ?

Maybe this article? <https://airbyte.io/articles/data-engineering-thoughts/the-state-of-open-source-data-integration-and-etl/>

Thank you, Christophe. But why would be the upside in using Airbyte assuming that customer only needs some of mentioned 21 connectors ?

&gt; Pipelinewise’s primary goal is to serve their own needs. They support 21 connectors, and add new ones based on the needs of the mother company. There is no business model attached to the project, and no apparent interest from the company in growing the community.
So argument would be that Airbyte would try to answer a different goal to serve the community needs instead of Transferwise if you need to make changes to those 21 connectors for a different use case

Are you saying that Airbyte would be more receptive to making adhoc changes to existing connectors than Pipelinewise? Isn't it hard to guarantee this kind of promise? I mean people may have all kinds of edge cases. It's hard to keep changing connectors to satisfy everyone.

Also if you don't mind me asking what do you mean by "needs engineering help" in this table ? What kind of help exactly ?
<https://airbyte.io/wp-content/uploads/2021/01/Landscape-of-open-source-data-integration-platforms-3-1024x492.png>

I'm asking all this to build a case for potential customer to use one instead of another :)

(The people you originally pinged should be more qualified than me :student: to answer that question though but they need few more hours to wake up)

Hi <@U01EPLM7ERG>! By "needs engineering help", we mean that a non-technical analyst for instance cannot start replicating data without the help of a technical data engineer.

the success of Fivetran is that they enable potentially non-technical data analysts to be autonomous in replicating the data they need, they are not limited by the priorities of data engineers.

regarding this question - <https://airbytehq.slack.com/archives/C01AHCD885S/p1611233272002900?thread_ts=1611232724.002100&amp;cid=C01AHCD885S> -, it's just a question of interest alignment. Pipelinewise is a tool to satisfy Transferwise's own needs. In the contrary, it is our entire focus. We will try to address as many use cases as we can, Pilelinewise will focus on addressing their own.