Data Engineering: The Backbone of Structured Data

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Ever thought who’s behind every structured data? Data Engineers are the invisible heroes who are making it possible.

Welcome back to my A-Z Technology series! Today let’s explore the letter D.

This article provides a high-level overview of what, where, and why data engineering matters.

What is Data Engineering7

Data Engineering is a combination of designing, maintaining, and building scalable data pipelines and infrastructure to extract, store, process and transform unstructured data into structured data.

Real-world analogy

Think of the Library as a storage container. The books in the library act as data. Every time new books arrive, they are messy and unorganised. Later, the books are organised in a structured manner, such as genre-specific. Similarly, unstructured data is messy; the process of transforming it into structured data is data engineering.

Why does Data Engineering exist?

I always wondered why data engineering exists until I landed in one of the data engineering projects. At that time, I learned many things about data engineering. It’s more than building pipelines and turning messy data into structured data. It can extract data from multiple sources, such as on-premises systems, cloud platforms, databases, and data warehouses. The data can be used by businesses, analysts, dashboards, or machine learning models immediately.

Data engineering helps extract and process the data automatically, which can reduce human errors. There are several pipelines, including Batch, Streaming, ELT/ETL, and Change Data Capture (CDC) pipelines, etc., which automate the process.

How Data Engineering works (high-level)

At a high level, I always used to think data engineering was just about moving data from one system to another. Over time, I realised it’s more than moving the data. The responsibilities of a Data Engineer start with raw data from different sources. One thing to remember is that the data often gets raw and rarely receives clean or consistent.

The first job starts with extracting the raw data without modifying source systems. The arrival of data might be ingested in batches or streamed continuously, depending on the use case. The data is not structured even after it reaches the target system. The actual process begins in the silver layer, where the data is gradually cleaned, validated and transformed.

Each layer has its own purpose first layer focuses on preserving the raw data, the second layer on improving data quality, and the third layer on making the data ready for analytics or reporting.

You may encounter difficulties in the process, such as data arrival being delayed, unexpected schema changes, and handling pipeline failures. Working with these issues, fixing data quality problems, and ensuring reliability is a big part of what data engineers actually do day to day.

Over time, this made me realize that data engineering isn’t just about tools or pipelines. It’s about building systems that people can trust, so that the data is available, accurate, reliable and ready whenever it’s needed.

Where is Data Engineering used?

Data Engineers build the foundation for meaningful data. Any system that collects, stores, and processes data relies on data engineering.

Data Engineering plays a key role in many areas:

Business Intelligence (BI): Reliable datasets.
Finance: Structured data supports risk management, fraud detection, and strategic decision-making.
Machine Learning: Providing cleaned datasets to data scientists to train AI models.
Retail & E-commerce: For inventory & supply-chain optimization and maintaining operational efficiency.
Healthcare: Improves data accessibility.
Government: Uses data responsibly to improve societal outcomes.
Transportation: Manages movement, tracking, and planning data.

What deeper topics are intentionally left out