This article highlights the challenges and learnings of working as a data SDET on a data engineering project using low-code tools.

This post shares our journey, mindset and appreciation for the open-source community engaging in small projects such as getting unit testing in dbt from an experiment to an open-source framework. Here’s how we did it:

In his series finale about transferring from software to data engineering, Claudio Diniz recaps his learnings from all those he interviewed, and shares a wealth of knowledge for those thinking of making the change:

Claudio Diniz interviews Yogendra Valeni about making the transition from software to data engineer. Yogendra explains that a key area to research and understand is the technical challenges and trade-offs between streaming versus batch processing.

In the penultimate of his series on moving from software to data engineering, Claudio Diniz talks to Thorben Louw who explains that you shouldn’t be put off trying out data engineering because they don’t know X! You learn what you need to as you need it.

In this post, data engineer Himanshu Agarwal explains why data mesh is a trend he’s keep an eye on, as are all of our data engineers at Equal Experts.

In the next in his series about demystifying data engineering, Claudio Diniz interviews Austin Poulton who explains that software engineering principles should be applicable to data.

In the next in his series about demystifying data engineering, Claudio Diniz interviews Jennifer Stark and learns why she’s keeping an eye on MLOps which is a facet of data engineering that is maturing into its own speciality. More here

In the next in his series about demystifying data engineering, Claudio Diniz interviews Gavin Campbell who explains that one of the hardest things for software engineers is that data engineering almost always involves some kind of external platform. Learn more here: