Data engineering traverses a wide range of technical capabilities that can be applied in many data science careers. Some popular ones are data and machine learning scientists and data engineers. Becoming a professional in these fields is in high demand as the world of data expands around itself every second. It is a promising area of expertise with high pay, but is data engineering a proper fit for you?
Data engineering is the process of creating parameters for a set of data in need of organization. A data engineer is usually responsible for providing the data pipeline for this process. Moreover, one must be cognizant of machine learning and ETL: extraction, transformation, and loading. Each step is performed after the extraction of data from multiple data sources (e.g. compressed files) to transfer into a sort of holding center. Responsibilities include managing empty values, duplicates, and any other errors in the model. Fortunately, this involves working alongside software like Spark and Hadoop to make the motions easier to manage. The entire point of this process is to develop algorithms and manage resources that push businesses toward their financial goals.
The pay is undoubtedly high. As of February 5, 2021, over 2,400 former and current engineers recorded on Glassdoor that their individual salaries average around 102k a year. Of course, you will need the technical skills necessary to succeed. Usually, a bachelor's, as well as years of mastery in business data analytics, can propel your career prospects forward. Start-up engineers can excel with a technical graduation program, data consulting, or a junior data engineering position. You will at least need to pick up code, as well as specialize in languages like Python and Scala. These programs remove much of the tediousness found in the ETL process. Even with these intuitive tools, data engineering can be monotonous at times. Much of it involves repetitive methods like filing data into tables, as well as having a keen eye for identifying errors. It is fair to say that it requires someone who will remain persistent in all the areas automation fails in.
In reference to the future of data engineering, many data engineers are wary of the influence of automation. The possibility of a future of fully-automized industries and the abrupt acquisition of new skills and responsibilities may be difficult to adjust to. Many in this field also live with the dread of becoming obsolete in their field of work. Some remain optimistic, believing that the career path will never fade because every database requires human oversight.
How can you even consider data science if the future is uncertain? Well, if you have a steadfast desire to one day work in the data science field, there are many avenues in which to invest your time. You may be tempted to seek the help of a consultant firm or online coding services like Python. Consultant firms sweep up new programmers, train them, and set them with other companies. Python services deliver courses that you can feel comfortable using anywhere. It is difficult to focus on education when you must socially distance yourself in a classroom setting; if you need comradery, a virtual chat of fellow learners exists for you. And if you are insecure about where to take your programming skills, share them online with the community. You will be met at the level you are, even if you are just starting out. Maybe you are very knowledgeable and want to hone certain skills? If you have a specific topic of choice that you are curious about, feel free to explore Python, because it is convenient, easily accessible, and intuitive to your learning needs.
An abundance of information can be found throughout the internet and is often incohesive. Courses built by professional Python developers that aids in reigning in the discoveries of tomorrow. Even if you are unsure about which path to pursue, you can still foster the technical skills necessary without spending so much on a science degree to improve yourself.
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