Data Engineers is the new hotness, many developers have been already working with distributed data technologies, such as Apache Kafka, Apache Spark, Apache Cassandra as backend developers, building infrastructure for analytics and enabling a healthy flow of data in the organization.

The reason we see more media coverage for that topic is the maturity of Data Science and Machine Learning. ML used to be most hyped. Following the hype, companies realized that they need to enable smarter products, So what did they do? they started hiring Data Scientists, although, they didn't have the infra to support them.

Due to that, many companies are now focused on building in-house ML platforms to enable their Data Scientists to get more value out of the data.
Yes, you read correctly, more value out of the data.
Let's take a look at the Data Science needs pyramid:

Data science layers towards AI by Monica Rogati:

You can see the clear need for Data Infrastructure Engineers and Data Engineers, they are at the base of the pyramid. This means, without them, data scientists won't be able to do their job efficiently. Think about it as similar to Maslow's Human needs pyramid. At the base, there are the physical needs, without them, we won't exist.

you are probably curious, why do I share this with you, well I had a wonderful conversation with Sheel Choksi, Solution Architect at Ascend and we talked exactly about that. How we can help Data Engineers do more and accelerate development and ML in organizations. Watch Now 📺!