Overview
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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.
Syllabus
- Course 1: Big Data and Hadoop Foundations and Setup
- Course 2: HDFS Architecture and Programming
- Course 3: YARN MapReduce Architecture and Advanced Programming
- Course 4: Data Analysis Using Hadoop Tools
Courses
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The course "Big Data and Hadoop Foundations and Setup" offers a comprehensive introduction to the world of Big Data and Hadoop, providing foundational knowledge crucial for navigating modern data-driven environments. You’ll explore the limitations of traditional data processing technologies and understand how Hadoop addresses these challenges with its robust architecture and ecosystem. Through detailed modules, you will gain a deep understanding of Big Data concepts, the role of Data Science and Big Data Analytics, and the trends shaping the Big Data revolution. The course demystifies Hadoop's subprojects and distributions, giving you the tools to differentiate between them and apply their features to real-world problems. What sets this course apart is its hands-on approach. You'll install, configure, and run Hadoop in a Linux environment, building the technical proficiency needed to process large-scale data effectively. Whether you’re looking to enhance your career in Data Science or understand Big Data’s transformative impact on businesses, this course equips you with the skills to succeed.
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The course "Data Analysis Using Hadoop Tools" provides a thorough and hands-on introduction to key tools within the Hadoop ecosystem, such as Hive, Pig, HBase, and Apache Spark, for data processing, management, and analysis. Learners will gain practical experience with Hive's SQL-like interface for complex data querying, Pig Latin scripting for data transformation, and HBase's NoSQL capabilities for efficient big data management. The course also covers Apache Spark's powerful in-memory computation capabilities for high-performance data processing tasks. By the end, participants will be equipped with the skills to leverage these technologies within the Hadoop platform to address real-world big data challenges. What makes this course unique is its comprehensive approach to integrating various Hadoop tools into a cohesive workflow. You'll not only learn how to use each tool individually but also understand how to effectively combine them to optimize data processing and analysis. Through hands-on exercises and examples, you'll gain the confidence and skills to tackle complex data challenges and extract valuable insights from big data. Whether you're looking to enhance your data analysis capabilities for work or want to deepen your knowledge of Hadoop and big data tools, this course offers valuable skills that will help you succeed.
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The course “HDFS Architecture and Programming” offers a comprehensive understanding of the Hadoop Distributed File System (HDFS) architecture, components, and advanced programming techniques. You will gain practical experience in setting up and configuring Hadoop for Java development, while mastering key concepts such as file and directory CRUD operations, data compression, and serialization. By the end of the course, you will be proficient in using HDFS to handle large-scale data processing, enabling you to build scalable, high-availability solutions. What sets this course apart is its hands-on approach, where you will work directly with HDFS, writing client programs and applying advanced techniques such as using Sequence and Map Files for specialized data storage. Whether you're new to Hadoop or looking to refine your existing skills, this course equips you with the tools and knowledge to become proficient in HDFS programming, making you a valuable asset in the field of Big Data.
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The course "YARN MapReduce Architecture and Advanced Programming" provides an in-depth understanding of YARN and MapReduce architectures, focusing on their components and capabilities. Students will explore the MapReduce programming model and learn essential optimization techniques such as combiners, partitioners, and compression to improve job performance. The course covers Mapper and Reducer parallelism in MapReduce, along with practical steps for writing and configuring MapReduce jobs. Advanced topics such as multithreading, speculative execution, and input/output formats are also explored. By the end of the course, participants will have hands-on experience in optimizing and writing efficient MapReduce jobs, preparing them to apply best practices in real-world scenarios. This course is unique as it not only covers the foundational aspects of YARN and MapReduce but also delves into optimization strategies, offering learners the tools to enhance data processing efficiency. Whether you're new to MapReduce or looking to deepen your knowledge, this course provides valuable insights for mastering large-scale data processing.
Taught by
Karthik Shyamsunder