How to Become a Data Engineer: A Hype Profession or a Necessary Thing
“Big Data is the profession of the future” is all over the news. I will say even more: data engineering skills for a developer is an urgent need. Before 2003, we had created as many petabytes of data as we do today every two days. Gartner analysts named cloud services and cybersecurity among the top techno trends of 2021.
The trend is easily explained. Huge arrays of Big Data need to be stored securely and processed to obtain useful information. When the companies moved to remote work, these needs have become even more tangible. E-commerce, Healthcare, EdTech — all these industries want to know everything about their online consumers. While the data is only stored on the servers, there is no sense in it at all.
Clean, structure, conver — these are the basic operations of Data Engineering. The professional should know how to combine data of different formats, collected from several sources. I have been programming with Python for three years, of which I have been immersed in Big Data for two years. From personal experience, I realized that for daily work you need to be able to do more.
Basically, a Data Engineer is a combination of four roles:
Software Engineer. Writes code, tests and optimizes it. In my opinion, the simplest path to Data Engineering is Software Engineering. This specialist knows how a computer and programs are arranged. He/she is familiar with the development of high-quality software and working with databases.
Big Data Developer. Understands the principles of data processing, uses various tools to transform them. He prepares a description of data models depending on the client’s request or business processes.
Database Administrator. Building a storage architecture is placed on his shoulders. Knows how to store data in the best way and perform basic operations on it.
Cloud Engineer. The volumes of data today are so massive that it is too expensive or impossible to store them on servers – it simply does not fit there. Cloud solutions are there for you. This engineer understands what cloud solutions are, what their structure and specific features are, how they interact with each other, and how to set up cloud services.
From any of these positions, you can switch to Data Engineering.
This trio of specialists is deeply data-driven. Each one has his own responsibilities. The Data Engineer receives a request from colleagues to find relevant data in order to find out the effectiveness of a new feature, for example. The engineer pulls specific data from different sources (server, application or cloud), simplifies, processes it and loads it into the right storage. From there, Data Analyst takes it – analyzes the information and translates it into a format understandable to the client. This could be a report, infographic, presentation. The specialist sees the connection between the found indicators and compares them. When you need to predict a patient condition or a market dynamic, a Data Scientist is needed. Let’s take an example of how all roles collaborate in a project.
Imagine a regular social network for learning foreign languages. People find penpals and practice English, German, Chinese, etc. Millions of daily users leave digital traces: they log in via their personal mail, buy premium accounts, download the application, and make calls via video. Every click is registered and sent to the server. The company wants to track the effectiveness and profitability of the platform. How can a Data Engineer help with this? Personally, he cannot. But with colleagues like Data Scientists and Data Analysts, the data he found turns into useful information — statistics, infographics, and forecasts.
This is not to say that some of them are more useful, do more work, or better copes with responsibilities. The scope of their tasks may indeed differ and depends on the tasks set by the client. The only thing is that the Data Engineer seems to be working in the “shadow.
