One of the advantages of living in a decentralized, globalized economy is that you have instant access to almost all kinds of information. The disadvantage, however, arises in the way you handle this information.
The rapid proliferation of data and its ease of accessibility has put businesses in a precarious position. On one hand, they have the opportunity to sift through mountains of data and derive useful insights from them that can further propel them towards success. On the other hand, they face a risk of being unable to manage and handle this data properly, which could result in them being overwhelmed and left behind as the competition races ahead of them. And another
Data Engineering is a way firms can solve this problem they face. Data Engineering is a practice that involves extracting, storing and handling data systems in such a manner that data scientists can easily access and understand it, and draw useful insights that drive smart decision-making. The role of Data Engineers within an organization primarily revolves around collecting data from different sources, and then formatting it so that it becomes accessible, reliable and consistent for data scientists. Without achieving this important task, the success of any business organization becomes uncertain.
Before we delve deep into the nitty-gritties of data engineering, it is important to establish a distinction between it and data engineering. Though the two terms sound similar, and though there does exist some overlap between the duties of a data scientist and data engineer, the two roles differ significantly for the following reasons:
Now, let us see how Data Engineering plays a pivotal role in a business’s success: