Big Data Processing Using Hadoop

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

• Gain expertise in Hadoop ecosystem components like HDFS, YARN, and MapReduce for big data processing and management across various tasks
• Learn to set up, configure, and utilize tools like Hive, Pig, HBase, and Spark for efficient data analysis, processing, and real-time management
• Develop advanced programming techniques for MapReduce, optimization methods, and parallelism strategies to handle large-scale data sets effectively
• Understand the architecture and functionality of Hadoop and its components, applying them to solve complex data challenges in real-world scenarios

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work