Let’s take a closer look at what big data entails.
Big data engineering is a field of study concerned with accumulation of large data sets, especially from new sources methods for analysing, methodically extracting information that are too large or complicated for typical data-processing application software to handle and struggles to keep up. The three Vs of big data, i.e. variety, volume, and velocity, define big data.
Now that we have a good understanding of what big data is, let’s look at what a big data engineer does. A big data engineer interacts with vast data processing systems and databases. They sift through massive amounts of data to uncover relevant sets for analysis which companies then use to forecast behaviour. One of the many responsibilities of a big data engineer is to analyse businesses to assist them evaluate their performance, analyse market demographics, and forecast future changes and trends. Big data engineers offer valuable insights which are then utilised by companies, governments and other industries and fields.
To Become A Big Data Engineer, You’ll Need These Skills
Big Data engineering is a difficult field to master. Big Data engineers are educated in real-time data processing, offline data processing methodologies, and large-scale machine learning applications. Let’s look at the skills you need to become a big data engineer.
1. Machine Learning
One of the important tools for big data engineers is machine learning as it helps them to quickly organise and handle massive amounts of data. In addition, because machine learning algorithms equip themselves by analysing data sets, big data is a component of their development. Despite the broad breadth of big data engineering, machine learning and data mining play a significant part in the field.
Having prior Hadoop analytics experience will be advantageous for a big data engineer. It is one of the most popular Big Data engineering tools that you should be familiar with. Hadoop is a set of open-source tools that can simultaneously process enormous data volumes across thousands of servers and devices. Examples of Apache Hadoop-based technologies can be HDFS and MapReduce.
Not only is Java one of the early and leading programming languages but because of its efficiency and object-oriented design it is one of the most extensively used coding languages. It aids in the development of data sorting algorithms and machine learning algorithms making it crucial for big data engineers. This is a language that engineers should be familiar with.
Another must-have skill for big data engineers is Python. It is also another popular programming language because of its flexibility, efficiency, strong high-level language with automatic memory management and it is simple to understand. Python has a sizable community and a huge library base. As a result, engineers should not only be familiar with Python and how to build tools with it, but also be involved in contributing to and using Python libraries.
5. Apache Spark
Last but not least, you must have experience with real-time processing frameworks such as Apache Spark. The necessity of the hour is for real-time processing and rapid actions as you’ll be dealing with massive amounts of data as a Big Data Engineer. An analytics engine such as Apache Spark, which can handle batch and real-time processing is one of the last skills which a big data engineer must have.
In India, Praxis is the first to provide full-time data science and data engineering programs. Praxis is motivated by the goal of developing tools that would help India transition to a data-driven, tech-driven digital environment. Praxis helps to achieve this goal by providing academicians and industry experts who will assist in the development of the necessary skills.