Introduction

With the exponential growth of data, organizations need robust and scalable solutions to store, process, and analyze massive datasets. Apache Hadoop is a leading open-source framework that enables distributed storage and processing of big data. This course provides a comprehensive, hands-on approach to leveraging Hadoop and its ecosystem (HDFS, MapReduce, YARN, Hive, Spark, and HBase) for real-world big data analytics.

Participants will gain expertise in data ingestion, processing, querying, and optimization using Hadoop clusters and learn how to apply big data analytics to drive business insights.

Course Objectives

By the end of this course, participants will be able to:

• Understand big data concepts and the role of Hadoop in modern data analytics.
• Set up and manage a Hadoop cluster.
• Work with HDFS (Hadoop Distributed File System) for efficient storage.
• Implement MapReduce and YARN for distributed data processing.
• Use Hive, Pig, and Spark SQL for querying and transforming big data.
• Perform real-time data processing with Apache Spark on Hadoop.
• Optimize Hadoop performance and resource management.
• Integrate Hadoop with cloud platforms like AWS EMR, Google Dataproc, and Azure HDInsight.

Who Should Attend?

This course is ideal for:

• Data analysts & engineers working with large-scale datasets.
• Big data developers looking to master Hadoop and its ecosystem.
• BI & analytics professionals who need to process large volumes of structured/unstructured data.
• Cloud & DevOps engineers integrating Hadoop with cloud solutions.
• Researchers & data scientists leveraging Hadoop for advanced analytics.

Day-by-Day Course Breakdown

Day 1: Introduction to Big Data & Hadoop Ecosystem

Understanding Big Data & Hadoop Fundamentals

• Introduction to big data challenges and traditional database limitations.
• The role of Apache Hadoop in big data analytics.
• Hadoop ecosystem overview: HDFS, YARN, MapReduce, Hive, Pig, HBase, and Spark.

Setting Up a Hadoop Cluster

• Installing Hadoop in a single-node and multi-node cluster.
• Understanding HDFS architecture and commands.
• Hands-on lab: Uploading and managing data in HDFS.

Day 2: Hadoop Distributed Storage & Processing

Working with HDFS

• Data replication, block storage, and file organization.
• Performing CRUD operations on HDFS using CLI & Web UI.
• Hands-on lab: Building a data lake with HDFS.

Introduction to MapReduce & YARN

• Understanding MapReduce programming model.
• Optimizing MapReduce jobs for large-scale data processing.
• Hands-on lab: Developing and running a MapReduce job on Hadoop.

Day 3: Querying Big Data with Hive & Pig

Data Warehousing with Apache Hive

• Introduction to Hive architecture & SQL-based querying.
• Writing HiveQL queries for data analytics.
• Hands-on lab: Performing batch analytics with Hive.

Data Transformation with Apache Pig

• Understanding Pig scripts for data transformation.
• Optimizing data pipelines with Pig Latin scripts.
• Hands-on lab: Building an ETL pipeline with Pig on Hadoop.

Day 4: Real-Time Big Data Processing with Spark & HBase

Introduction to Apache Spark for Big Data Analytics

• Comparing MapReduce vs. Spark for big data processing.
• Writing Spark applications in PySpark & Scala.
• Hands-on lab: Running distributed Spark jobs on Hadoop.

NoSQL Data Storage with HBase

• Introduction to HBase for real-time big data storage.
• Hands-on lab: Storing and querying structured/unstructured data in HBase.

Day 5: Performance Optimization & Cloud Integration

Optimizing Hadoop Performance

• Hadoop tuning strategies: compression, partitioning, and indexing.
• Managing resources with YARN schedulers.
• Hands-on lab: Optimizing a Hive query for performance.

Deploying Hadoop on Cloud Platforms

• Working with AWS EMR, Google Dataproc, and Azure HDInsight.
• Running Hadoop jobs on cloud-based clusters.
• Hands-on lab: Processing big data on AWS EMR.

Capstone Project: End-to-End Big Data Analytics Workflow

• Participants will design, implement, and optimize a complete Hadoop-based big data analytics project.
• Data ingestion, processing, querying, and visualization.
• Final presentations and peer review.

Conclusion & Certification

At the end of the training, participants will receive a Certificate of Completion, validating their expertise in Big Data Analytics with Hadoop.

This course combines theory, hands-on labs, real-world case studies, and best practices to equip learners with modern big data analytics skills for enterprise applications.