What Is Data Engineering And Why Is It Important?

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Data engineering is a critical field where data is concerned, but not many people can accurately describe what data engineers do. 

Data drives the operations of businesses small and large. Businesses use data to provide answers to relevant inquiries that range from consumer interest to product viability. 

Without a doubt, data is an important part of scaling your business and gaining valuable insights. And this makes data engineering just as important. 

In March 2016, about 6,500 LinkedIn users listed their title as “data engineers”. They offered a wide variety of skill sets, including a knowledge base of Python, SQL, and Java. 

But what is data engineering? And what do data engineers do? To find out, keep reading!

What Is Data Engineering?

Data engineering, sometimes called information engineering, is a software approach to developing information systems. 

To be clear, data engineering encompasses sourcing, transforming, and managing data from various systems

This process ensures that data is useful and accessible. Above all, data engineering emphasizes the practical applications of data collection and analysis. It should come as no surprise that investigating the inquiries mentioned above requires complex solutions. 

As such, data engineering employs intricate methodologies for gathering and authenticating data that range from data integration tools to artificial intelligence. 

Similarly, data engineering relies on special mechanisms to apply found data to real-world scenarios, usually designing and monitoring sophisticated processing systems to that effect. 

Why Is Data Engineering Important?

Data engineering is important because it allows businesses to optimize data towards usability. For example, data engineering plays a large role in the following pursuits:

  • Finding the best practices for refining your software development life cycle
  • Tightening information security and protecting your business from cyberattacks
  • Increasing your understanding of business domain knowledge
  • Bringing data together into one place via data integration tools

Whether business teams are dealing with sales data or analyzing their lead life cycles, data is present every step of the way. 

Over the years, technological innovation has made a grand impact on the vitality of data. These innovations comprise cloud technology, open-source projects, and the growth of data in scale. 

The last bit especially stresses the importance of engineering skills when it comes to organizing huge amounts of data. 

Data must not only be comprehensive but coherent, and this is the task that data engineers set out to do. 

Data Science vs. Data Engineering?

Though data as a whole encompasses a broad field, data engineering and data science are distinct software engineering disciplines. 

One of the most crucial aspects of data engineering is optimizing big data. Big data is a subset of data engineering and refers to the processes that take place in order to handle overtly complex or large sets of data.

However, in 2017, technology-based research company Gartner determined that between 60% and 85% of big data projects fail

This is largely due to unreliable data structures. Combined with the newfound digital transformation that many companies in the modern era find inevitable, quality data engineering is more important than ever. 

Unfortunately, the early days of big data management did not have much of a clue about data engineering. 

As a result, data science teams took up the job of present-day data engineers. But this didn’t quite work. This is because data scientists are trained for exploratory data analysis, and not much more. 

The job of data scientists is to interpret data. Data scientists do not have a strong understanding of how to model data for interpretation in the first place. 

On the other hand, they utilize mathematics, statistics, and even machine learning techniques to properly evaluate an analytics database. 

Data engineers ensure that this data is ready for data science teams in the first place. To that end, data engineers are made to assess the quality of data

When the quality is not up to par, they then cleanse the data to make it so. Database design, for this reason, makes up a significant share of the job. 

Note that machine learning engineers can do the role of both data scientists and data engineers. Advanced data engineers sometimes do the work and fulfill the role of machine learning engineers. 

What Is a Data Engineer?

A data engineer specializes in database architecture design that enables the collection, storage, and analysis of data.

A creative representation of a hard disk drive with a pie chart graphic on the disk platter, overlaid with code, symbolizing data storage and analytics, with a blue and yellow theme.

Data engineers set up analytics databases and data pipelines for operational use. Much of their job is preparing big data, ensuring that data flows work optimally. 

The responsibilities of a data engineer revolve around building algorithms and databases to help data scientists run queries for predictive analysis, machine learning, and data mining. 

Formatting both structured and unstructured data is part of the job as well. Structured data can conform to a conventional database. Unstructured data includes the likes of text, images, audio, and video, which conventional data models do not accept. 

It is imperative that data engineers know and partake in different methods for assembling and formatting data. 

What Is the Role of a Data Engineer?

There are several different engineering roles that fall under data engineering. Here’s a quick breakdown:

Generalist Data Engineers

A generalist data engineer typically works on small teams, performing end-to-end data collection. 

Those in the generalist role have many skills, more so than other types of data engineers, but have less familiarity with the system architecture. 

Since small teams do not tend to have many users, generalists worry less about large-scale assignments and maintain a fully comprehensive role. 

Pipeline-Centric Data Engineers

Mid-sized and larger companies are more likely to use pipeline-centric data engineers. For reference, a data pipeline is a data workflow that consolidates data from disparate sources, 

A pipeline-centric data engineer will work across distributed systems on complicated data science projects. 

Database-Centric Data Engineers

Large companies rely on database-centric data engineers to work on data that is distributed across several databases. 

Database-centric engineers focus entirely on analytics databases. This means they work closely with data scientists, working across multiple data warehouses and developing table schemas. 

What Skills Do Data Engineers Need?

Data engineers are technically software engineers but traditional programming skills hardly scratch the surface of what data engineers are capable of. 

Here’s a summary of the tools and responsibilities data engineers must be familiar with to do their job.

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ETL Tools

ETL stands for extract, transform, and load. Tools of this kind describe a category of technologies for data integration.

Low-code development platforms have largely taken the place of traditional ETL tools in the present day. But the ETL process, in general, is still paramount to data engineering. 

Informatica and SAP Data Services are some of the more well-known tools for this purpose. 

Programming Languages

Data engineering calls for a variety of programming languages, namely back-end languages as well as query languages and specialized languages for statistical computing. 

Python, Ruby, Java, and C# name some of the most popular programming languages for data engineering, alongside SQL and R. You will often see Python, R, and SQL being used together. 

Python is a general-purpose programming language that is easy to use with an extensive library. This makes it suitable for ETL tasks because the language is flexible and powerful. 

Structured query language (SQL) is also for performing ETL tasks. It is the standard language for querying relational databases which, unsurprisingly, is a big part of data engineering. 

R is the go-to programming language and software environment for statistical computing. It’s a favorite amongst statisticians and data mining.


Application programming interfaces (APIs) are essentially a prerequisite for dealing with anything related to data integration, including data engineering of course. 

APIs are integral to every software engineering project. They provide a link between applications and transport their data. 

Data engineering relies on REST APIs especially. REST or representation state transfer APIs can communicate over HTTP, making them a great asset for any web-based tool. 

Data Warehouses & Data Lakes

Data warehouses and data lakes refer to large, complex datasets organizations store for business intelligence. 

In business-driven information engineering, business analysts manage these datasets through computer clusters. This network of computers helps to better solve problems. 

Spark and Hadoop are two well-known big data frameworks. The use of these frameworks is to prepare and process big data sets. 

They each rely on computer clusters to perform tasks on vast amounts of data, from data mining to data analysis. 

Why Data Engineering Is Critical For Business

Data engineering is an essential piece of nearly every business objective. Data engineers use a number of unique skills and tools to prepare and process data for future analysis. 

It goes without saying that data isn’t useful unless it is readable. Thus, data engineering is the first step in making data useful. 

Trio understands the importance of data engineering for business scalability. That’s why we have highly qualified data engineers on our team equipped to take your organization to the next level.
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With over 10 years of experience in software outsourcing, Alex has assisted in building high-performance teams before co-founding Trio with his partner Daniel. Today he enjoys helping people hire the best software developers from Latin America and writing great content on how to do that!
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