Highly rated on Google
4.9/5
Course Duration
6 weeks
Learning Mode
Offline
Placement's*
100%
Description:
The Hadoop Essentials course provides participants with a comprehensive understanding of Apache Hadoop, a powerful open-source framework for distributed storage and processing of large-scale data sets. Participants will learn about the core components of the Hadoop ecosystem, including Hadoop Distributed File System (HDFS) for storage and MapReduce for parallel processing. Through hands-on exercises and practical examples, students will gain proficiency in setting up Hadoop clusters, storing and processing data using HDFS and MapReduce, and performing basic data analysis tasks.
Key Topics:
- Introduction to Big Data and Hadoop
- Hadoop Architecture and Ecosystem
- Hadoop Distributed File System (HDFS) Concepts and Operations
- Hadoop MapReduce Framework
- Setting up a Hadoop Cluster
- Data Ingestion and Extraction Techniques
- Data Processing with MapReduce
- Data Analysis with Hive and Pig
- Introduction to Apache Spark
- Hadoop Security and Administration
Prerequisites: Basic understanding of Linux operating system and familiarity with programming concepts such as Java or Python is recommended but not mandatory.
Who Should Attend:
- Data engineers, analysts, and scientists interested in learning about Hadoop and its applications for big data processing.
- IT professionals seeking to enhance their skills in big data technologies and distributed computing.
- Business professionals looking to gain insights from large datasets using Hadoop and related tools.
Outcome:
Upon completion of the course, participants will have a solid understanding of Hadoop fundamentals and be able to set up and manage Hadoop clusters, store and process data using HDFS and MapReduce, and perform basic data analysis tasks. They will be equipped with the skills necessary to work with big data systems and leverage Hadoop for large-scale data processing and analysis.