Top Most In-Demand Big Data Skills in 2024

Big Data, an ever-expanding collection of complex information, is crucial in today's technologically advancing era. Traditional tools can't manage its vastness and complexity. Big Data engineers, therefore, are vital for designing and maintaining data infrastructures. They handle tasks like creating data pipelines, optimizing processing systems, and ensuring data security and privacy. Their role is essential for businesses to analyze and leverage data for insightful decision-making.

Why is Big Data the Hottest Job in the Market?

According to research by the company Wikibon, the global Big Data market is expected to grow from $42B in 2018 to $103B in 2027 at a Compound Annual Growth Rate (CAGR) of 10.48 per cent. 83% more companies are creating Big Data infrastructures to handle a large amount of data, and there has been a 123% increase in Big Data engineer jobs since 2015. The applications of Big Data have been used across various industries, due to which the demand for Big Data Engineers continues to grow as companies realize the importance of analyzing big data in their business.

Key Big Data Skills Needed for Being an Expert Big Data Analyst

Programming

Programming languages like Python, Java, and Scala are highly used in big data as programming is used to analyze data and develop data processing systems with tools like Hadoop, Spark, Flink, etc.

Data Warehousing

One of the most important skills for big data engineers is data warehousing, designing and implementing data warehouses for integrating data from multiple sources, data preprocessing, creating a model of the available data for easy access, and ensuring the quality of data that is being stored.

Computational Frameworks

Computational frameworks are used to analyze a large amount of data.

Some Frameworks Used are:

• Apache Hadoop that uses MapReduce for parallel processing of a large amount of data. Java is the most used language with Hadoop.
• Apache Flink, which is used to stream data processing in real-time with low latency.
• Apache Spark for large-scale data processing with distributed processing and in-memory caching. Scala or Python is the programming language that is used with Spark.
• Other frameworks like NiFi, which is a data pipeline automation tool, Kafka, and Storm, which can be used for data stream processing, are also skills for big data engineers.

Quantitative Aptitude and Statistics

Statistics is a basic skill for big data engineers because it is behind various machine learning concepts, and it plays a significant role in evaluating the effectiveness of machine learning models, as well as in identifying and representing relationships within data for creating data models. Therefore, statistics is an essential big data skill to analyze and interpret large volumes of data accurately.

Business Knowledge

The reason for analyzing big data is to convert a large amount of raw data into useful information based on business knowledge that will give profits to the organization. Therefore, big data engineers must have a business perspective to achieve profits.

Data Visualization

Data visualization is a big data skill that is used to better understand the information on big data, like data quality, outliers, and patterns in data. We can also achieve better collaboration through data visualization with different types of charts and graphs, as raw data is hard to understand.

Big Data Skills in Huge Demand

Apache Hadoop

Apache Hadoop is used for processing a large amount of data through parallel processing of data using MapReduce. It manages its resources using the YARN resource manager and has a distributed storage system, Hadoop Distributed File System (HDFS). It is fault-tolerant, scalable, and also has advanced security features. Therefore, Hadoop is an essential skill for big data engineers.

NoSQL

NoSQL databases are used to store unstructured and complex data as key-value pairs. NoSQL databases provide features like flexibility, failure tolerance, optimized performance, and low cost. Many companies use NoSQL databases like MongoDB and Cassandra for storing large amounts of data.

Machine Learning

Machine Learning is a big data analytics skill that is used to predict or process data through algorithms like Clustering, Classification, Regression, or Natural language processing. A big data engineer must understand the basic concept of machine learning.

Apache Spark

Apache Spark is a big data skill that is used to process a large amount of data in real-time. It is built with a fault-tolerant system and in-memory processing capability, which is faster than Hadoop for processing data. Spark also supports SQL, graph processing, and Scala programming language.

Data Mining

Data mining is performed to understand and extract valuable insights from data through cleaning, transformation, and evaluation of data. We can use tools like Apache Mahout, RapidMiner, KNIME, and programming languages like R and Python to perform data mining.

Problem-Solving

A problem-solving mind is an important skill for big data engineers as complex and efficient models have to be created for processing a large amount of data.

SQL

SQL is an important skill for big data engineers to analyze and manipulate data with tools and databases like Apache Hadoop, Apache Spark, and NoSQL databases. SQL is used to gain valuable insights from large datasets and make data-driven decisions. Complex SQL queries have to be made to get insights and useful information from data.

Developing Your Big Data Skills

To develop your skills in the field of big data, you can do the following:

• Have a strong understanding of concepts like machine learning and working with tools like Hadoop and programming languages.
• You can take online courses or tutorials on platforms like Scaler to upgrade your skills. Reading blogs on big data can also help you to improve your knowledge of big data.
• The best way to learn will be to work on projects that involve processing and analyzing real-world data using tools and programming languages like Python or R.
• Share your interests in communities and connect with other learners and grow from asking questions in the community.
• Attend online conferences and meetings that talk about the latest trends and new techniques in big data.