Machine Learning Engineers, Data Scientists, and Big Data Engineers rank among the top emerging jobs on LinkedIn.

Forbes

Data Engineering

Data engineering is the process of developing and building systems for collecting, storing, and analyzing data. It is a vast field with several applications in various industries. Firms have collected huge amounts of data, and they require data infrastructure and personnel to sort and analyze the information.

This resulted in the demand for big data engineers who work to design systems, which collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. The main objective is to make data accessible so that the firms can take help of it for evaluating and optimizing their business overall performance.

Job role of a big data engineer

It is the opinion of several data professionals that data engineering as a profession has been around for well over a decade, or even several, since relational databases came to market led by major Original Equipment Manufacturers (OEM’S) in the 1970’s. This included Microsoft SQL Server, IBM DB2, and Oracle. However, the reality is that data engineering has evolved immensely since the early years with the onset of Big Data, digital transformation, and more sophisticated data science practices like ML and AI.

Now data volumes, variety, and velocity are much greater than what they used to be, which has led data engineering professionals away from using traditional ETL tools to developing and adopting new tools and processes to manage the data revolution. These modern tools now support cloud computing, data infrastructure, data warehousing, data mining, data modeling, data crunching, metadata management, data testing, and governance, among others.

Data engineers help data scientists and data analysts find the right data, make it available in their environment, make sure the data is trusted and that sensitive data is masked, ensure they spend less time on data preparation and operationalize data engineering pipelines.

Batch or real-time, data engineering systems and outputs are the backbones of successful data analytics and data science. Data pipelines that move data from one place to another have become the nervous system of the modern company and subsequently data reliability and quality the beating heart.

Prerequisites to be a big data engineer

Let’s understand the prerequisites to be in this growing profession like education, skillset, technologies, certification, and more that boosts the career to grab a top position in a reputed organization.

Earn an undergraduate degree

The most essential aspect to start a career as a big data engineer is to have a bachelor’s degree, as the job demands a good knowledge of several basic concepts. One can do a degree in any of the following:

• Computer Science
• Software Engineering
• Information Technology

Have a good sense of programming

The field of data engineering requires coding skills. One must have a programming background. They must have a keen interest in data as well as finding patterns in data. One can enhance their knowledge of the programming languages by doing the best big data engineer certification. The important programming languages one must have knowledge of are as follows:

• Python – It is a popular programming language used for data analysis, modeling, and pipelines. And it is also very easy to learn due to its simple syntax.
• R- It is mainly used by data scientists and analysts to perform tasks that are related to data analytics. This is developed by the statisticians and has a steep learning curve.
• Java- This is majorly used in machine learning sequences, data architecture frameworks, and building data sorting algorithms.
• Scala- This is widely used in data processing libraries like Kafka – an open-source processing software platform. Scala is more concise and totally relies on a static-type system.

Learn latest technologies

The individuals must have a good knowledge of the latest technologies that are essential to perform the day-to-day tasks. Following are the important tools that the data engineers use:

• Apache Hadoop
• Apache Spark
• Apache Hive
• Apache Beam
• Apache Cassandra
• Apache Oozie
• Apache NiFi
• Apache Flink
• Apache HBase
• Apache Impala
• Apache Kafka
• Apache Crunch
• Apache Apex
• Apache Storm
• Heron
• Hue

Get professional certifications

There are several industry-recognized best big data engineer certifications, which an individual can do to improve their skills before starting their career in this field.  Certification offers excellent knowledge and guidance by giving exposure to real-time projects. One such certification is associate data engineer certification called ABDE™ from Data Science Council of America (DASCA).

The Associate Big Data Engineer (ABDE™) is the best big data engineer certification. It acts as a proof that a person has taken a big step in mastering the field of data engineering. . The skills and knowledge one can attain by doing this certification will set them ahead of the competition.