Data Engineering
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 wheredata 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 harmoniously, data engineers and data scientists can deliver consistently valuable insights.
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Required data engineering skills and responsibilities
Data engineering requires a broad set of skills ranging from programming to database design and system architecture. Here are just a few:
Data engineers are focused on providing the right kind of data at the right time. A good data engineer will anticipate data scientists’ questions and how they might want to present data. Data engineers ensure that the most pertinent data is reliable, transformed, and ready to use. This is a difficult feat, as most organizations rarely gather clean raw data.
To work their magic, most data engineers must be proficient in Python, SQL, and Linux. Data engineers may also need skills in cluster management, data visualization, batch processing, and machine learning. Data engineers use these processing techniques to massage data into a format that facilitates hundreds of queries.
While data engineers may not be directly involved in data analysis, they must have a baseline understanding of company data to set up appropriate architecture. Creating the best system architecture depends on a data engineer’s ability to shape and maintain data pipelines. Experienced data engineers might blend multiple?big data processing technologies?to meet a company’s overarching data needs.