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Bad Data Engineering Practices And How To Avoid Them – MarkTechPost

Data Engineering is designing and building systems to collect, store, and analyze data at scale. Organizations need the right people and technology to collect massive amounts of data and ensure that the data is in a usable state by the time data analysts and data scientists get hold of the same. The field of Machine Learning and Deep Learning can only succeed with data engineers processing and channeling the data.
Data engineers work in various environments to create systems that collect, manage, and transform raw data into actionable information that data scientists and business analysts can interpret. The ultimate goal is to make the data accessible so that companies can use it to assess and optimize their performance. It is said that data is useful only when it is readable, and data engineering is the first step in making the data useful.
Given the importance of Data Engineering, the following are some of the practices that every data engineer must avoid.
Knowing the above bad practices will make the job of data scientists and engineers much easier. However, they can add the following capabilities to ensure they escape these practices completely unscathed.
From future analysis to today’s day-to-day operations, data engineering is the key to making businesses more durable. One can keep track of the data daily, but it’s of little use if it is not understandable and coherent. Accessible as well as actionable business intelligence can facilitate up to 5x faster decision-making. Data engineers must therefore ensure that they refrain from the pitfalls mentioned earlier and follow specific guidelines to allow businesses to accelerate their growth and make more sound decisions.
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I am a Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various areas.
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