We recently published a new report: The Case for the Holistic Data Engineering Platform, which outlines why piecemeal tooling isn’t sustainable for most modern companies.
In today’s world, it’s well understood that the best businesses make data-driven decisions to push their organizations forward. The business landscape is largely a race – to be the first, the best, the fastest – in any given industry or niche.
Naturally, the speed and accuracy with which decision-makers are armed with reliable data is a major factor in a business’ good decision-making. For data engineers, the challenge of performing advanced analytics is often bottlenecked in the disjointed data engineering process. To operate efficiently, you need to engineer the data for optimal downstream processing, rather than simply storing it and making it accessible in an analytics application.
So, how can a modern organization make sure its teams are enabled to win this race? Streamlined and scalable data engineering processes are the pillar of effective data analytics, and having a modern data engineering platform and managed data repository are the pillars to success.
Our latest report is an in-depth discussion of the evolution of data engineering, the technical makeup of a good holistic data engineering platform, how they compare to traditional data engineering tools, factors to consider in your evaluation of your business needs against what’s available in the market, and much more.
Interested in learning more about our data engineering platform, Magpie? Request a demo here.
Rishon Roberts is a Marketing Manager at Silectis. You can find her on LinkedIn.