Understanding data engineering and what it means for the future
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The idea of collecting and analyzing data to gather insights isn’t really new. However, the specific roles involved in the collection and analysis of data have grown and evolved considerably over the last decade as the amount of data being created has increased at a staggering rate. In this article, Cher Zavala explains why data engineers are so important.
To the uninitiated, the roles of data scientist, data analyst, and data engineer are synonymous and used interchangeably. However, they are quite different altogether, with the title of data engineer a relatively new development in the field of data science. In the past, the tasks of a data engineer may have fallen under the purview of a business intelligence developer, but the complexity and sheer volume of data today have expanded the role well beyond that of a simple developer.
What is a data engineer?
While data scientists and data analysts perform some of the more visible aspects of data mining and insight gathering, the data engineer forms the foundation of the process. At the risk of oversimplifying, data engineers develop and build the infrastructure that collects the data to be analyzed by data scientists and analysts. At their foundation, they are software engineers, designing and maintaining systems that can collect and integrate data from various disparate sources and create data sets that can be massaged into meaningful insights.
Although they aren’t typically involved with the development of machine learning or other analytical tasks, they are responsible for creating the queries that make those functions possible, and ensuring that data gathering is accurate and complete. In short, data engineering is responsible for all aspects of the foundational systems on which the computations and other analysis take place.
Typically, data engineers come from a background in engineering, computer science, or software development, with knowledge in both database development and management and engineering practices. Most hold advanced degrees in one or more of these fields, having earned an engineering degree online or at a traditional college while gaining additional experience in computer science. Among the skills in high demand include database administration (in particular data cleaning, and ensuring accurate data sets), an understanding of scaling, the ability to build fault-tolerant data pipelines, system monitoring, and error management. In general, those who have an understanding and passion for programming and engineering data pipelines, but have little or no interest in mathematics or statistics are well-suited for data engineering rather than data analytics.
Why data engineers are so important
Data science is repeatedly referred to as one of the most important and critical industries of the future. In fact, the Harvard Business Review once called data science the “sexiest job of the 21t century.” However, often these statements refer to data analysis, or the actual process of gleaning actionable, valuable insights from data. We hear about retailers analyzing customer buying patterns to determine whether they may be getting married or having a baby, and are amazed and/or shocked at how much they really know about us — and we’re amazed that we receive coupons for diapers when we haven’t even told anyone that we’re pregnant yet.
Without data engineers, most of this analysis would be difficult or even impossible. There is simply too much data being created for the old methods to remain relevant. Data engineering is a fundamental part of the new world of big data, not only increasing the amount of data collected, but also ensuring that is clean, consistent, and high quality. It’s not always a visible part of the data science process — and undoubtedly can be frustrating — but without it, businesses would never be able to keep up with the influx of data or obtain reliable results from their analysis.
A world without data engineers? Not possible
Data engineers are also a vital part of maintaining compliance in the face of increasing regulatory requirements regarding the collection and use of data. By showing your data process from an engineering standpoint, you can more fully comply with auditor requests and provide the necessary information accurately.
The increasing complexity of the world of Big Data means that gaining insights requires more than a set of rudimentary algorithms and a basic understanding of analytical principles. Stratifying the roles ensures that every aspect of the process is managed accurately and appropriately, playing on the strengths and abilities of various disciplines. Data engineers will continue to be an important part of this process, developing and implementing the new technologies that will form our data-driven future.