What do you like best about the product?

Airbyte since it entered into the game of Data tools, has proven to be a disruptive actor in this field, slowly but strongly solidifying its position in the market by incorporating more and more features every month into its platform, by adding a huge amount of connectors that allows developers to build efficient and robust processes to their data pipelines.

Recently Airbyte has added support to pipelines built with Python, by introducing PyAirbyte to their data tools offer, this allows us, developers, to develop our customized connectors and then connect them to different data pipelines.

Besides the possibility to continuously contribute to the development of the features of the tool, it allows to developers to be always connected and updated through the Slack channel, where every developer interested in contributing to the project can take part.

Airbyte is a wise choice for any company or Data professional looking for a robust and reliable ETL tool for their tool stack.

What do you dislike about the product?

So far no dislikes about using the tool for my data workloads, maybe one feature to get better is to keep the connector’s documentation updated. Frequently I haven’t found proper documentation for some of the connectors that are already implemented, and including some code samples on how to use them won’t hurt anybody!

What problems is the product solving and how is that benefiting you?

One of the issues I faced before the adoption of Airbyte as a Data Tool for my project workloads was not counting with an open-source ETL tool that is reliable and that has continuous support. That is a remarkable thing for me as a freelance developer. One of the biggest benefits for me of using Airbyte, has been counting with a reliable, robust, and open-source tool for my projects with different clients.