The process in which we design a system, build a system, so as to get data, store the data, and analyze the data is Data Engineering. The field of data engineering is so broad that it cut across about every industry. Do you know firms have the ability to collect huge amounts of data, and they need the right people and technology to ensure the data is in a highly usable state by the time it reaches the data scientists and analysts?

We can also say that data engineering involves designing, maintaining, optimizing data systems, and selecting the right databases, storage systems, cloud architecture, or cloud platforms.

Very importantly, fields of machine learning and deep learning can’t succeed without data engineers to process and channel that data. Examples of firms that need data engineers are Amazon, Facebook, IBM, Airbnb, AT&T, capital one, and many more.

WHAT DO DATA ENGINEERS DO?

Data engineers extract and collect raw data from multiple sources, transform and store data in table form. Data engineer ensures that the data is highly available, consistent, secure, and recoverable. The ultimate goal of a data engineer is to make data accessible so that organizations can use it to evaluate and optimize their performance.

DIFFERENCES BETWEEN DATA ENGINEERS, ANALYSTS AND SCIENTISTS

Data engineer ensures the data is highly available, consistent, secure, and recoverable. The data engineers work with other data professionals to ensure data matches their needs.

Data analysts and data scientists make use of the data that data engineers provide.