How to Become a Big Data Engineer (Part I)
As a profession, Big Data Engineer enjoys high demand. It’s undoubtedly, a promising career choice for those aspiring and keen Big Data fans. However, before moving forward, it’s important to know just what Big Data Engineering involves.
In essence, this discipline pertains to any professional who works in the area of big data and analytics. There are many job titles given to this profession but typically, these individuals will work in some kind of data mining, big data platforms or analytics framework. The exact areas of expertise for each candidate will vary, though there is general agreement that all of these frameworks share a few core principles. While not every organization employing analytics professionals will employ all of these frameworks, most do, and therefore it’s crucial to be aware of your employer’s hiring practices in order to be prepared when making your debut.
Most of today’s big data engineers have an earned graduate degree in computer science or information systems. These professionals often hold key roles in financial institutions, defense intelligence agencies, online retail giants like Amazon and Overstock, telecommunications companies and insurance firms. In order to be qualified for such a role, graduates must be knowledgeable in the field of mathematics, physics and other relevant areas. These platforms require analysts to have strong software writing and analytical skills.
While the field of mathematics may seem to be the obvious choice for those seeking employment with big data analytics, recent graduates interested in a position within the IT field should consider a different path. Graduate programs in computer science allow graduates to specialize in one or two areas. For instance, computer science students may choose to focus their studies in networking, software or computer architecture. Those who prefer programming skills may opt to focus on systems, application or web programming.
Graduates interested in the position of a big data analyst often focus on one or two particular technologies. Companies often require analysts to implement high-tech data clusters. Some examples of such technologies include Hadoop, Spark, heterogeneous memory computing, and NoSQL. A data cluster is a collection of servers that work together to process large amounts of real time data. Examples of data clusters include Cockroach, Hermes, Hadoop nadir. Depending on what industry you’re entering, your career path may branch off into another realm of programming skills.
If your goal is to become a data engineer, you’ll likely begin your career in IT. IT departments throughout companies are brimming with qualified data scientists. The salary expected to be earned by data scientists is substantially higher than those earned by entry-level computer technicians or IT professionals. In addition, the job outlook for this field is expected to grow substantially over the next decade. In fact, data engineering is projected to be the most popular job in the United States in three years.
One of the best ways to learn how to become a big data engineer is to obtain an education at one of the many colleges and universities that offer computer science, math, computer engineering, or a related degree. Courses in computer science to give students the opportunity to learn the core skills necessary to be successful in the information technology field. For example, computer science courses teach students how to use databases, how to design efficient algorithms, how to optimize databases for speed and security, and how to implement security features in distributed systems. Database coursework includes designing and maintaining large scale database systems as well as small database systems. Algorithms are designed to solve problems using the most effective mathematical and logic.
Many graduate schools offer specific programs in the areas of computer science and engineering, including information technology, computer science, and engineering. These programs prepare students to enter one of several exciting careers in the Information Technology field. These include network and packet sniffers, networks, routers, servers, and database management systems. As data scientists, these professionals are responsible for finding new ways to collect and organize large volumes of data, analyzing it in order to provide valuable solutions to complex problems. In order to succeed in this career field, a student needs both hands-on experience and a solid education.