Bad data engineering practices and how to avoid them | Jobs Vox


Data engineering is designing and building systems for collecting, storing, and analyzing massive amounts of data. Organizations need the right people and technology to collect massive amounts of data and ensure that data analysts and data scientists have the data in a usable state. The field of machine learning and deep learning can only be successful by data engineers processing and disseminating data.

Data engineers work in a variety of environments, working with systems to create, 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 measure 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 data useful.

bad data engineering practices

Given the importance of data engineering, the following are some practices that every data engineer should avoid.

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  1. Creating a data model with many tables without consistent naming or a standardized, self-explanatory file naming convention. This can complicate the data engineering infrastructure and may require a lookup table.
  2. The lack of comments and improper formatting make the code difficult to troubleshoot.
  3. Failing to architect backup and recovery can lead to avoidable delays.
  4. Not deleting the original data before incremental updates can result in duplicate records and incorrect reporting.
  5. No foreign key constraints in the warehouse. They act as a safety net and ensure data integrity.
  6. Not checking the validity and consistency of the data on load. This can lead to a misrepresentation of the situation at hand.
  7. Not building composite data sets to speed up queries when working with large amounts of data.
  8. Manually fixing errors in production rather than reverting to a previous high-quality version.
  9. Not keeping versions of production data to allow for troubleshooting.
  10. Not checking the data output of an ETL pipeline after deploying it leads to something going wrong when the data actually needs to be used.

How to avoid these data engineering mistakes?

Knowing the above bad practices will make the job of data scientists and engineers a lot easier. However, they can add the following capabilities to ensure that they remain fully protected against these practices.

  • Using Git-like version control.
  • As soon as production data has quality issues, revert the data to the last commit.
  • Engineers should work in isolation to branch their data repositories and have a separate environment for their data.
  • Engineers must reproduce their results by returning commits of the data in the repository.
  • Make sure changes are safe and atomic. Engineers can work on a branch, test it, and once they’re sure it’s of high quality, they can merge it into the main branch.
  • When dealing with potentially disruptive changes, engineers must create a new branch and experiment with it. Once done, they can discard the branch while being confident that production will remain functional.

From future analysis to today’s day-to-day operations, data engineering is the key to making businesses more sustainable. One can keep track of data every day, but it is of no use if it is not understandable and not consistent. Accessible as well as actionable business intelligence can facilitate decision making up to 5 times faster. So data engineers must make sure they stay away 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 fields.

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