Demystifying Data Engineering – InApps 2022

Most organizations today work with data that comes from multiple, disparate sources. Designing and building a system that brings all of this data together, transforms it, and then stores it for analysis is a complex task. This is where data engineering comes into play. While the data scientists and analysts get all the credit for unlocking value out of this data, it’s the data engineers who build the required platform for them to thrive.

While data engineering has emerged as a major trend and is one of the most sought-after jobs today, the role is often misunderstood. This post highlights a few myths and misconceptions about data engineering and its contribution to the business.

Understanding the Data Engineer Role

Data engineers are responsible for building data pipelines that collect, transform, and store organizational data in a usable format for analysis and Business Intelligence (BI). Data engineering blends data science and software engineering.

Data engineers set up and optimize databases, define and implement schema changes, and handle metadata. They also integrate new data management tools and systems, and ensure the smooth functioning of your data pipeline. In short, they set up a robust data infrastructure for data scientists and analysts to leverage rich, transformed data for insight generation.

Now that we’ve defined a data engineer’s role, let’s bust some common myths associated with the job.

Myth #1: Data Engineering Is a “Classic IT Role”

Contrary to some beliefs, data engineering does not involve pulling ethernet cables, resetting passwords, or controlling network infrastructure costs. These responsibilities fall under a separate, dedicated IT function.

Data engineering is a modern, cross-functional role that brings together DevOps, data science, and traditional software engineering. In essence, data engineers are the proverbial Jack (or Jill) of all trades. They must have a breadth of understanding: everything from web application coding to regex to data science. They use this diverse skill set to design and build a data infrastructure that gives teams complete, company-wide visibility into organizational data.

Data engineers also build and maintain a CI/CD pipeline for all organizational data, and maintain version control systems to ensure infrastructure-wide data quality.

Myth #2: Modern SaaS Tools Will put Data Engineers Out of Their Jobs

False — while many companies use off-the-shelf SaaS tools as a part of their core data infrastructure, they need data engineers to manage these tools and to get the most out of them. Architecting a clean, robust data stack and integrating tools for optimal, trouble-free performance requires specialized knowledge and dedicated energy.

Modern SaaS tools will not put data engineers out of their jobs, but they will create efficiencies. As most of these tools are self-managed, they will simplify data engineering tasks related to tooling. This will allow them to focus on what’s important: building and monitoring efficient, optimized, and well-orchestrated data pipelines.

Myth #3: Data Engineers do Everything

The data engineering role can be intimidating. It involves coding in languages such as Python, administering databases, and building ETL systems. It also requires familiarity with cloud infrastructure, an understanding of DevOps and pipeline orchestration, and more. This leads to a common question: “Are data engineers supposed to do everything?”

The answer depends on the scale of the company. In small-scale companies, data engineers have to set up a data pipeline from scratch and manage it. Their tasks become more specific as an organization scales. In mid-to-large-scale organizations, it is rare to find a data engineer whose responsibilities cover the whole spectrum of the data engineering skillset. Instead, their tasks and responsibilities are split among multiple teams and depend on company-specific requirements and use cases.

Myth #4: Data Engineering Requires a College Education or Advanced Degree

Bluntly put, this is not true. No university course or online curriculum can fully teach you to build data systems that allow you to migrate data from disparate sources, transform it, and store it for analysis. Yes, you can discover how certain tools work and learn data management best practices. However, most successful data engineers learn on the job. Nothing beats the knowledge and experience you gain from building a data pipeline from scratch and debugging the errors you encounter during the process.

Those who have a software engineering background will find it easier to transition into the data engineering role, given that coding is an essential aspect of it. However, it is also common that people from other backgrounds — not related to software or computers — become successful data engineers.

Data engineers also learn a lot when operating in the real world with real customers. Ultimately, it boils down to a love for data and a knack for understanding and architecting complex data systems and workflows.

Data Engineering Myths = Busted

In this article, we looked at — and busted — some of the most common data engineering misconceptions. The truth is, data engineering holds a vital place in every data-driven organization. It’s no wonder that data engineering is one of the most sought-after roles in the tech industry today.

From building the data infrastructure to managing systems that support the entire company’s data requirements, data engineers play a crucial role in ensuring the right data is available to every team at the right time, enabling them to make better decisions.

More businesses are beginning to recognize the value of modern data engineering. As the demand for data grows and systems become more complex, the demand for data engineers will increase.

Feature image via Pixabay.