Bad Data Engineering Practices And How To Avoid Them

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.

Bad Data Engineering practices 

Given the importance of Data Engineering, the following are some of the practices that every data engineer must avoid.

  1. Building a data model with numerous tables without consistent naming or a standardized, self-explanatory file naming convention. This could overcomplicate the data engineering infrastructure and may require a lookup table.
  2. Lack of comments and improper formatting makes the code harder to troubleshoot.
  3. Failing to architect for backups and recovery can lead to avoidable delays.
  4. Not deleting the original data before incremental updates can lead to duplicate records and incorrect reporting.
  5. Not having 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 when it is loaded. This could lead to an inaccurate representation of the situation at hand.
  7. Not building aggregate data sets to speed up the queries when working with large volumes of data.
  8. Fixing errors in production manually instead of reverting to the previous high-quality version.
  9. Not keeping versions of the production data to allow troubleshooting.
  10. Not checking the data output of an ETL pipeline after it has been deployed leads to finding something 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 much easier. However, they can add the following capabilities to ensure they escape these practices completely unscathed.

  • Using Git-like version control.
  • Revert the data to the last commit as soon as quality issues occur in the production data.
  • Engineers should work in isolation to branch their data repository and get an isolated environment for their data.
  • Engineers should reproduce their results by returning to a commit of data in the repository.
  • Ensure that the changes are safe and atomic. Engineers could carry out work in a branch, test it, and once they have ensured that it is high quality, they could merge it into the main branch.
  • While dealing with potentially destructive changes, engineers should create a new branch and experiment with it. Once done, they could discard this branch while staying confident that the production will be functional.

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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