Poor data governance could lead to poor decision making that puts your business and its reputation at risk. But what does good governance look like and how do we implement it?

Only 15% of data projects that use AI will deliver the expected ROI. Here’s how to build data projects at scale that will drive transformation and higher returns.

This expert talk will show you how to get the best possible performance out of multiple test environments when using modern cloud microservices

Data is the lifeblood of the modern organisation, providing the insights we rely on for everything from digital strategy to customer service priorities. But what happens when your data isn’t delivering as expected? Poor data quality, visibility or analytics can derail or delay critical business activities and needs to be addressed.

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:

In this blog post, Bejul Shah explains why the inquisitive and exploratory mindset of a QA helps in transitioning to the role of delivery lead within a team.

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.