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Data Engineering: Why It's About Much More Than Just the Tools You Use
Rethink Data Engineering Than Just Focusing On Tools
There are many tools for data engineering. Data engineering doesn't work solely on Python or SQL. With the era of big data and cloud computing, new data-related open-source projects have been emerging than ever before. Not only do we have multiple options for your tasks, but too many of them that require data engineers to know the pros and cons of each tool to pick.
There are many similar tools for data engineering. The data engineering market is competitive. Many open-source vendors provide almost identical services with minor tweaks—all claim to be the best of the class, have lightning-fast performance, and have an engaging community.
Tools equipped me like a handyperson. I can pull a data framework for a suitable situation from my selections. A few years ago, I loved chasing down the latest and undiscovered data engineering tools. Many of those projects didn't become popular, and many of them needed to be updated. It's terrifying that you find an excellent project on GitHub; the following year, it is abandoned. That's the speed of a data engineering framework dying out if it doesn't get enough usage.
