Introduction

In today’s data-driven world, organizations generate massive volumes of structured and unstructured data. Apache Hadoop is a leading open-source framework designed to store, process, and analyze big data efficiently across distributed clusters. Hadoop has become a key component of modern data architectures, including Data Lakes, AI/ML workflows, and cloud-based analytics.

This 5-day hands-on training course provides an in-depth introduction to Hadoop’s ecosystem, architecture, and key components such as HDFS, YARN, MapReduce, and Apache Hive. Participants will learn how to build, manage, and optimize scalable and fault-tolerant big data solutions using Apache Hadoop and its associated tools.

Objectives

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

• Understand Apache Hadoop’s architecture and its role in modern data management.
• Deploy and configure a Hadoop cluster (on-premise or in the cloud).
• Work with HDFS (Hadoop Distributed File System) for large-scale data storage.
• Use MapReduce for distributed data processing.
• Query and analyze data using Apache Hive and Apache Impala.
• Optimize performance with YARN resource management and best practices.
• Integrate Hadoop with cloud platforms and modern analytics frameworks.
• Explore next-generation Hadoop alternatives, such as Apache Spark and Data Lakehouse architectures.

Who Should Attend?

This course is ideal for:

• Data Engineers building scalable big data pipelines.
• Data Architects designing distributed data solutions.
• BI & Analytics Professionals working with big data technologies.
• Database Administrators (DBAs) managing large-scale storage and processing.
• Cloud Engineers & DevOps Teams integrating Hadoop with cloud services.
• IT Managers & CTOs strategizing enterprise data initiatives.

Day 1: Introduction to Apache Hadoop & Big Data Fundamentals

• Understanding Big Data Challenges & Solutions

  • Characteristics of Big Data (Volume, Velocity, Variety, Veracity)
  • Traditional vs. Distributed Data Processing
  • Why Hadoop? The evolution of Hadoop in modern data ecosystems

• Apache Hadoop Ecosystem Overview

  • Core components: HDFS, YARN, MapReduce
  • Related technologies: Apache Hive, HBase, Pig, Impala, Flink, and Spark
  • Hadoop vs. Cloud-native Big Data Platforms

• Setting Up a Hadoop Cluster

  • Single-node vs. multi-node clusters
  • Installing Hadoop (On-premise, AWS EMR, Azure HDInsight, or Google Dataproc)
  • Understanding Hadoop’s configuration files and cluster management tools

• Hands-on Lab:

  • Deploying a basic Hadoop cluster and exploring HDFS

Day 2: Hadoop Distributed File System (HDFS) & Data Storage

• Introduction to HDFS

  • Understanding HDFS architecture
  • Blocks, Namenodes, Datanodes, and Replication
  • Writing and reading data in HDFS

• Managing Data in HDFS

  • HDFS commands for file operations
  • Best practices for storing structured and unstructured data
  • Integrating HDFS with cloud storage (S3, Azure Blob, GCS)

• Data Ingestion into Hadoop

  • Importing data with Apache Sqoop (RDBMS to Hadoop)
  • Ingesting streaming data with Apache Flume and Kafka
  • Best practices for batch vs. real-time data ingestion

• Hands-on Lab:

  • Storing and retrieving data in HDFS
  • Using Sqoop to import data from MySQL/PostgreSQL to Hadoop

Day 3: Processing Big Data with MapReduce & YARN

• Introduction to MapReduce

  • How MapReduce works: Mapper, Reducer, Combiner
  • Writing MapReduce jobs in Java/Python
  • Comparing MapReduce with Apache Spark

• YARN: Resource Management in Hadoop

  • How YARN schedules and manages cluster resources
  • Configuring YARN for optimized performance
  • Monitoring jobs using YARN Resource Manager

• Optimizing Performance in MapReduce

  • Tuning MapReduce jobs for efficiency
  • Working with distributed cache and compression
  • Alternatives to MapReduce: Apache Spark for faster processing

• Hands-on Lab:

  • Writing and executing a basic MapReduce job
  • Managing and tuning workloads in YARN

Day 4: Querying & Analyzing Data with Apache Hive & Impala

• Introduction to Apache Hive

  • Hive architecture and components
  • Writing SQL-like queries with HiveQL
  • Optimizing queries using partitioning and bucketing

• Apache Impala for Real-time Analytics

  • Difference between Hive and Impala
  • Running low-latency queries on Hadoop data
  • Connecting Hive & Impala to BI tools like Tableau & Power BI

• Data Warehousing on Hadoop

  • Hive Metastore and schema evolution
  • Data integration with Apache HBase
  • Optimizing Hive for cloud-based analytics

• Hands-on Lab:

  • Querying structured data using Hive and Impala
  • Visualizing Hadoop data with a BI tool

Day 5: Advanced Hadoop & Future Trends

• Security & Governance in Hadoop

  • Authentication and access control (Kerberos, Ranger)
  • Data encryption and audit logging in Hadoop
  • Managing multi-tenant environments

• Integrating Hadoop with AI/ML & Cloud Technologies

  • Using Apache Spark for ML workflows
  • Hadoop and Data Lakehouse architectures (Delta Lake, Iceberg, Hudi)
  • Cloud-native alternatives: AWS EMR, Google Dataproc, Azure Synapse

• Future of Hadoop & Emerging Technologies

  • The shift from Hadoop to Spark and modern data platforms
  • The rise of serverless big data architectures
  • Best practices for migrating legacy Hadoop workloads to cloud solutions

• Final Project: End-to-End Big Data Pipeline

  • Design and implement a real-world big data solution using Hadoop
  • Apply best practices in data storage, processing, and analytics

• Course Wrap-Up & Certification

  • Review of key concepts
  • Q&A and discussions on real-world use cases
  • Certification of completion