Hadoop Fundamentals

The Hadoop Fundamentals course is a foundational training program designed to provide participants with a comprehensive introduction to the Hadoop ecosystem, focusing on its core components and practical applications. This course offers a balanced blend of theoretical insights and hands-on exercises, ensuring that participants gain both conceptual understanding and practical skills. Whether you are new to Hadoop or seeking to solidify your knowledge, this course equips you to navigate and leverage the Hadoop stack effectively.

By the end of this course, participants will:

• Gain an overview of the Hadoop ecosystem, including its core technologies: HDFS, MapReduce, Hive, and YARN.
• Develop practical skills in distributed storage and querying with HDFS and Hive.
• Understand foundational concepts of distributed data processing and storage.
• Learn to create and manage data workflows using HiveQL.

This course is designed for:

• Developers and Data Engineers aiming to build foundational skills in Hadoop.
• Architects looking to understand the role of Hadoop in modern data solutions.
• Database Administrators seeking to expand their knowledge into big data technologies.
• Students and professionals beginning their journey in big data analytics.

Course Highlights

1. Overview of the Hadoop Ecosystem: Gain insights into how HDFS, MapReduce, YARN, Hive, and other components interact to support big data processing.
2. Practical Hands-On Experience: Work with HDFS to manage distributed storage and HiveQL to query and analyze data.
3. Efficient Data Processing: Learn to structure and manage data pipelines for scalability and reliability.
4. Real-World Applications: Apply your knowledge to solve practical big data problems.

Course Modules

1. Introduction to the Hadoop Ecosystem (1h theory): Overview of key technologies, their roles, and interactions.
2. HDFS: Distributed Storage (2h – 1h theory, 1h practice): Architecture, replication, and commands; hands-on file management via shell and Hue interface.
3. Hive: Querying Big Data (5h – 2h theory, 3h practice): Hive architecture, table metadata, HiveQL queries, and file formats (CSV, Parquet). Practice includes creating tables, executing queries, and using Hue and Tez UI.

Participants completing this course will:

• Understand the foundational components of Hadoop and its applications.
• Be proficient in managing distributed data storage using HDFS.
• Execute complex SQL-like queries on large datasets using HiveQL.
• Have the skills to tackle practical big data challenges in real-world scenarios.

This course bridges the gap between theoretical understanding and real-world application. With a focused scope and hands-on exercises, participants will develop confidence and competence in using Hadoop’s core technologies, preparing them for further exploration in big data or direct application in professional settings, large-scale distributed data, and executing efficient data queries.

Upon completion of the "Hadoop Fundamentals" course, trainees will be able to:

• Effectively navigate and manage Hadoop’s core components, including HDFS, MapReduce, YARN, Hive, and Spark.
• Implement data processing pipelines using MapReduce, HiveQL, and Spark SQL.
• Utilize HDFS and HBase for efficient data storage and retrieval.
• Process real-time data streams with Spark Streaming and Flink.
• Monitor and optimize Hadoop applications through various user interfaces.

• Overview of the Hadoop Ecosystem (1h theory)
  Introduction to the core components of the Hadoop stack: HDFS, MapReduce, YARN, Hive, and more.

• HDFS: Hadoop Distributed File System (2h: 1h theory, 1h practice)
  Overview of HDFS architecture, replication, and commands.
  Practice: Connecting to a cluster, managing files via shell and Hue interface.

• Introduction to Hive (5h: 2h theory, 3h practice)
  Hive architecture, table metadata, file formats, and HiveQL.

Practice: Creating tables, reading/writing CSV and Parquet files, executing SQL queries with aggregation and joins using Hue, Beeline, and Tez UI.