Up your data game with data engineering automation

We often hear from customers that they are ready to graduate from first-generation ETL (extract, transform, load) solutions to something that is more scalable and compatible with their business systems. This allows them to analyze their endless influx of data in real-time to gain insights and to make good business decisions.

They know that in today’s marketplace, data-driven decisions are not a nice-to-have, but a requirement. And, eventually, they learn that the first-generation ETL solution they have been using is now ineffective. So what do we mean when we say first-generation ETL?

It’s often all-too-easy to implement some type of ETL. But once implemented, teams often realize their pipeline aggregation tool is incomplete. Sure, first-generation ETL moves data from point A to point B. But it ends up in a big jumble. It’s like moving books from one library to the next, but piling them on the floor with no system in place to organize them.

These first-generation solutions aren’t able to scale or provide high data quality, so our customers are asking for something better. That’s because they don’t just need to go from point A to point B – they need actionable data intelligence and analysis which their ETL solution doesn’t provide. To use our previous analogy, next-generation ETL involves installing shelves and a Dewey Decimal System to make it easier to find the books you are looking for. And to get to that next level, you need true data engineering automation.

So, what is data engineering automation, and what are the benefits?

There are many types of data automation. But fundamentally, it’s about doing the heavy lifting when it comes to formatting, normalizing, and unifying the data. This enables C-suite teams (CROs, CMOs, etc.) to take control of their data lifecycle without burdening data engineering and IT teams.

These customers want a sophisticated data automation engine that provides modeling, testing, versioning, and analysis to close the chasm between business and technology teams – and provide the insights they need.

So, if you are relying on first-generation solutions that prevent you from making the best use of your data, it’s worth looking into data automation to extract, transform, and load your data in a way that is useful to business teams, quickly, and at scale.