Data Engineering: A Guide to the Who, What, and Ho

In the modern world, it is tough to think of any industry that has not been revolutionized by data science. Although many may not understand the intricacies of the data science discipline, they have enough exposure to know that data science is a growing field. People open their email to find personalized discounts, turn to Siri for immediate answers to their questions, and depend on their bank to identify and mitigate any potential fraud activity.

While we are enjoying the fruits of data science’s labor, there are other players working diligently behind the scenes. These employees are responsible for creating the data pipelines and warehouses that enable data scientists to write and optimize algorithms in order to enhance our everyday lives.

Who are these supporting actors? Data engineers.

What is data engineering?
Conclusions drawn from big datasets are only as valuable as its data integrity. Without an architecture that can structure and format growing and changing datasets, data scientists are unable to make accurate predictions. This is where data engineering comes into play.

Data Engineering is the act of collecting, translating, and validating data for analysis. In particular, data engineers build data warehouses to empower data-driven decisions. Data engineering lays the foundation for real-world data science application. Working harmonious.
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