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Advanced Features of the Hadoop Ecosystem

Get an in-depth look into the Hadoop Ecosystem and its components in this hands-on, free online course.

Publisher: Proton Expert Systems and Solutions
The Hadoop Ecosystem is a vital part of the Big Data Analytics Industry. Learn how to use its features and components in this free online course. You will explore Sqoop, Hive databases, Spark ecosystem, Flume, Apache Pig, Scala and even Cloudera. The course combines theory with practice to provide you with practical knowledge through hands-on activities. Ready to take your knowledge of Hadoop to the next level? Enrol now!
Advanced Features of the Hadoop Ecosystem
  • Duration

    1.5-3 Hours
  • Students

  • Accreditation






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In a world where organizations rely on fast, informed decision making, Big Data Analytics exists to make sense of huge amounts of information to extract meaningful insights. Big Data plays an important role in every field from health, economics, banking, as well as in government - new opportunities and challenges continue to emerge, to deal with the massive amount of data. The Hadoop Ecosystem, with its open source components, is designed to answer these needs: to store, process, evaluate, analyze and mine the data. Unlike traditional systems, Hadoop handles multiple types of workloads consisting of different types of data, with massive parallel processing using industry-standard hardware, earning it a place of great importance in this system, and knowledge of its components is vital.

Hadoop stores the data in the Hadoop Distributed File System (HDFS), a distributed file system designed to run on standard hardware. HDFS is highly fault-tolerant and provides high throughput access to application data and is suitable for applications that have large data sets. This course illustrates how different types of data can be stored on HDFS and how to process this data using various components of the Hadoop ecosystem. Cluster computing frameworks like MapReduce have been widely adopted for large-scale data analytics. Resilient Distributed Datasets (RDDs) enable efficient data reuse in a broad range of applications. RDDs are fault-tolerant, parallel data structures that let users explicitly persist intermediate results in memory, control their partitioning to optimize data placement, and manipulate them using a rich set of operators.

Are you interested in Big Data? Would you like to further your understanding of the Hadoop Software and Ecosystem? This course is for database and dataware house developers, Big Data developers and architects, data scientists, analysts and any technical personnel who are interested in learning and exploring the features of Big Data and its tools. With demonstrative lessons guiding you step-by-step and theory to back it up, the course follows with hands-on sessions to get practical experience in Sqoop, Hive, Spark, Flume, Apache Pig and Cloudera. So if you are looking to increase your knowledge of the advanced features of Hadoop, start this free online course today!

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