Big Data Hadoop Certification Training Course Syllabus

Overview: This project-driven Big Data Hadoop Certification Training Course provides a comprehensive introduction to the Hadoop ecosystem and modern data engineering practices. Designed for beginners, it spans approximately 12 hours of structured learning, combining theoretical concepts with hands-on labs. You'll progress from foundational Big Data principles to building secure, scalable data pipelines using HDFS, MapReduce, Hive, Pig, Spark, and orchestration tools. The course concludes with a capstone project integrating ingestion, processing, and analytics, preparing you for real-world data engineering challenges. Lifetime access ensures you can revisit materials at your own pace.

Module 1: Introduction to Big Data & Hadoop Ecosystem

Estimated time: 1 hour

• Understand Big Data characteristics (5 V’s)
• Learn Hadoop history and core design principles
• Explore the Hadoop ecosystem: Sqoop, Flume, Oozie
• Hands-on: Navigate a pre-configured Hadoop cluster
• Practice basic HDFS shell commands

Module 2: HDFS & YARN Fundamentals

Estimated time: 1.5 hours

• Study HDFS architecture: NameNode and DataNode roles
• Understand data replication and block size configuration
• Examine YARN components: ResourceManager and NodeManager
• Hands-on: Upload and download files in HDFS
• Simulate node failure and write YARN application skeletons

Module 3: MapReduce Programming

Estimated time: 2 hours

• Learn MapReduce job execution flow
• Implement Mapper and Reducer interfaces
• Use Writable data types and configure jobs
• Work with counters for job monitoring
• Hands-on: Develop and run WordCount and Inverted Index jobs

Module 4: Hive & Pig for Data Warehousing

Estimated time: 1.5 hours

• Understand Hive architecture and metastore
• Write SQL-like queries and use partitioning and indexing
• Create and run Pig Latin scripts for ETL
• Develop Pig UDFs (User Defined Functions)
• Hands-on: Query HDFS data with Hive and process with Pig

Module 5: Real-Time Processing with Spark on YARN

Estimated time: 2 hours

• Explore Spark architecture and execution model
• Compare RDD, DataFrame, and Dataset APIs
• Use Spark SQL for structured data processing
• Introduction to Spark Streaming fundamentals
• Hands-on: Build batch and streaming Spark applications

Module 6: Data Ingestion & Orchestration

Estimated time: 1 hour

• Use Sqoop for RDBMS to HDFS imports/exports
• Configure Flume sources and sinks for log data
• Define workflows using Apache Oozie
• Hands-on: Automate MySQL to HDFS ingestion
• Schedule a multi-step Oozie workflow

Module 7: Cluster Administration & Security

Estimated time: 1.5 hours

• Edit Hadoop configuration files
• Set up high availability for NameNode
• Implement Kerberos authentication
• Introduction to Ranger and Knox for security
• Hands-on: Configure HA NameNode and secure HDFS with Kerberos

Module 8: Performance Tuning & Monitoring

Estimated time: 1 hour

• Tune memory and parallelism settings
• Analyze job performance using YARN UI
• Monitor clusters with Ambari
• Hands-on: Optimize Spark executor configurations
• Review MapReduce job metrics

Module 9: Capstone Project – End-to-End Big Data Pipeline

Estimated time: 2 hours

• Ingest clickstream data using Sqoop and Flume
• Process data with Spark and Hive
• Visualize analytical results
• Deliver a deployable, integrated pipeline

Prerequisites

• Basic understanding of Linux command line
• Familiarity with programming concepts (Java/Python preferred)
• Basic knowledge of SQL and databases

What You'll Be Able to Do After

• Design and implement scalable Hadoop-based data storage solutions using HDFS
• Develop and optimize MapReduce jobs for batch processing
• Use Hive and Pig for efficient data warehousing and ETL
• Build real-time data processing pipelines with Spark
• Secure and administer enterprise Hadoop clusters with high availability