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Avoid These Data Engineering Practices if You Want to Ensure Success

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By Anurag Sinha, Co-Founder & Managing Director, Wissen Technology

Data is one factor that differentiates a great company from a mediocre one. Companies like Amazon and Nordstrom use data to reach clients, personalize experiences, and improve profits.

However, companies need a system to store and process large volumes of data and extract valuable insights. That’s where data engineering comes into the picture.

Data engineering involves building systems and infrastructure, data warehousing, mining, modeling, and metadata management. It helps convert unintelligible, raw data into intelligent and usable information. It also involves designing and deploying data pipelines and setting up data lakes to build a ready-to-use data repository.

The ultimate goal is to make data accessible for data scientists, so they can optimize and make it valuable for companies to make informed decisions. Also, since it provides clean, structured data, data scientists can use it for machine learning projects.

But data engineering is complex. Hence, companies must stop following certain data engineering practices that can hinder their potential.

6 Data Engineering Practices to Avoid

1. Building Complex Systems
Data engineers build complex, unmaintainable systems that become unsustainable after a time. Most often, these systems are not scalable. So, when data volume increases, the system doesn’t adapt accordingly. It fails to scale and results in an overall failure. Besides that, data engineers also choose technologies that may not work well for a long time. It leads to unnecessary expenditure for the company.

2. Using Complex Code Logic
Sometimes data engineers write lengthy and complex codes, which leads to unnecessary complications and confusion for other engineers. Take the example of something as basic as using the proper naming convention. The file names must be standardized and explain what the code does. Not using a standardized, self-explanatory naming convention could waste the team’s time and make the code hard to maintain. Engineers must also ensure that the codes are written in as few lines as possible to enable other members to manage them effectively.

3. Not Prioritizing Data Quality
Although data engineers understand the significance of data quality, they don’t prioritize it enough. They do not perform the basic quality assurance (QA)  checks on the data before sending it to production. It leads to duplicates and missing values in primary key fields. Also, it delays the sign-off process as data analysts have to perform and review the QA checks before pushing the Extract, Transform, Load (ETL) changes into production. Failure in auditing data quality can have massive repercussions on business, as business leaders rely on this data to make decisions.

4. Wrong Tools Pose Issues with Data Ingestion, Transformation, and Orchestration
Data engineers complete the data ingestion (transferring data from various sources to a centralised data warehouse), transformation (converting the data from one format to another), and orchestration process (bridging the data silos) before making it available for analysis. However, these tasks can become cumbersome when data volume increases.

Take data ingestion and transformation, for instance. Data comes in various formats, such as JSON, comma, and tab-separated files. Engineers need to manage and transform them correctly. They also need to remove incorrect and duplicate data from the dataset and standardize them. Wrong tools can prevent data engineers from performing these tasks efficiently. They can make the entire process ineffective.

5. Not Deleting Data While Making Updates
Typically, data engineers must delete all the data from the table while updating the pipeline in production. Failure to do so can lead to data duplication and incorrect reporting in downstream processes. The only way to prevent this issue is by adding a code to delete the records for the same period before making incremental updates.

6. Not Checking the Data Output of the ETL Pipeline
The most common mistake that data engineers commit is not checking the data output of the ETL pipeline after deploying the code in production. They assume the code requires no checking after it passes the QA tests. However, most times, the codes don’t account for the sample files and development databases that run in the development stage but don’t reflect real-world scenarios.

The data pipelines could also fail if the data output is left unchecked. That’s why it’s essential to check the data output regularly to ensure it’s working as expected.

5 Best Practices to Make Data Engineering Successful 
According to Deborah Leff, the CTO at IBM, only  13%  of data science projects reach the production stage. As the volume of data increases, companies will need a foolproof strategy to organise and maintain the data’s quality.

Implementing best data engineering practices is necessary. Here are a few things that companies can do:

1. Check the data output of the ETL pipeline regularly, especially after it’s deployed in production, to ensure that it’s working as expected.
2. Monitor and maintain the data quality. Sometimes the data could be inaccurate or irrelevant to the end user. The onus lies with data scientists to do thorough checks before sending it to the production stage. Data scientists must also ensure that the data is relevant to the end user. Knowing what end users want and coordinating with business teams will help improve the data’s quality.
3. Data becomes complex as the volume increases. Data scientists must build scalable data pipelines to manage the increasing data volume. They must ensure that the infrastructure can support the pipeline as the data volume increases.
4. Testing is necessary to ensure that the data pipelines work as expected and catch errors at an early stage. So, keep testing the data pipelines and ensure that the data is always reliable and accurate.
5. Maintain the version controls when multiple users work on the same data pipeline to track the changes and roll back if needed. A Git-like approach could work for maintaining version controls.

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