The specialization "Big Data Processing Using Hadoop" is intended for post-graduate students seeking to develop advanced skills in big data processing and management using the Hadoop ecosystem. Through four detailed courses, you will explore key technologies such as HDFS, MapReduce, and advanced data analysis tools like Hive, Pig, HBase, and Apache Spark. You’ll learn how to set up, configure, and optimize these tools to process, manage, and analyze large-scale datasets. The program covers fundamental concepts such as YARN and MapReduce architecture, and progresses to practical applications such as Hive query execution, Pig scripting, NoSQL management with HBase, and high-performance data processing with Spark.

By the end of the specialization, you will be capable of designing and deploying big data solutions, optimizing workflows, and leveraging the power of Hadoop to address real-world challenges. This specialization prepares you for roles such as Data Engineer, Big Data Analyst, or Hadoop Developer, making you a highly competitive candidate in the fast-growing big data field, ready to drive innovations in industries such as data science, business analytics, and machine learning.

Applied Learning Project

The specialization “Big Data Processing Using Hadoop” equips postgraduate students with in-depth knowledge of big data technologies through self-reflective readings and theoretical exploration. Covering essential tools like HDFS, MapReduce, Hive, Pig, HBase, and Apache Spark, the program delves into concepts such as YARN architecture, query optimization, NoSQL data management, and high-performance computing. Learners will critically analyze the implementation of these technologies, reflecting on their applications in solving real-world big data challenges. By the end of the program, students will be prepared for roles like Data Engineer, Big Data Analyst, or Hadoop Developer, driving innovations in data science and analytics.