Intro to Data Engineering for Data Scientists: An overview of data infrastructure which is frequently asked during interviews
During my past years of conducting interviews for my employers, I have found that fresh data science graduates often don’t have a good understanding of the data engineering side of the world. They typically swim in the ocean of machine learning algorithms and pay little attention to their work upstream and downstream. Yet, the experience of working with data engineers is highly valued by many companies as it is critical in terms of deploying data science models smoothly and efficiently. Understanding the bigger picture is important. It helps you to get a sense of all the components that a company needs in order to solve business problems and the role that you and your work are playing in the organization.
This article will help you grasp the various elements of data infrastructure and also get familiar with some common tools and software that are used in each step.
Let’s get started.
Here are some basic terminologies that you will often hear data engineers mention. Let’s first clear these concepts out of the way:
Monolithic application: dev teams work on different functionalities of the same software
Microservices: Build each function as a standalone application and access each other through API
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