Introduction to Big Data and Hadoop Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview (80-120 words) describing structure and time commitment.
Module 1: Understanding Big Data
Estimated time: 1 hour
- Big Data definition and evolution
- Characteristics: Volume, Variety, Velocity, and Veracity
- Data types: Structured, semi-structured, and unstructured
- Real-world Big Data examples across industries
Module 2: Hadoop Architecture
Estimated time: 2 hours
- HDFS architecture: NameNode and DataNode roles
- YARN for resource management and job scheduling
- Data replication and fault tolerance mechanisms
- Cluster scalability and distributed storage principles
Module 3: MapReduce Basics
Estimated time: 2 hours
- MapReduce programming model overview
- Map, Shuffle, Sort, and Reduce phases
- Job lifecycle in a Hadoop cluster
- Distributed computation and fault recovery
Module 4: Working with HDFS
Estimated time: 1 hour
- HDFS command-line interface
- File storage, block size, and data locality
- Replication factor and data distribution
- Practical HDFS operations: upload, list, retrieve
Module 5: Interacting with Hadoop Clusters
Estimated time: 1.5 hours
- Hadoop cluster setup and configuration
- Accessing clusters via terminal
- Navigating directories and inspecting configurations
- Validating cluster health and node roles
Module 6: Spark Overview
Estimated time: 1 hour
- Introduction to Apache Spark
- Spark vs MapReduce: performance and architecture
- RDDs and DataFrames basics
- Running Spark jobs on Hadoop clusters
Module 7: Ecosystem Tools Introduction
Estimated time: 1 hour
- Hive for SQL-like querying
- Pig for data flow scripting
- HBase for NoSQL storage
- Flume and Sqoop for data ingestion
Module 8: Best Practices & Review
Estimated time: 0.5 hours
- Fault tolerance strategies in Hadoop
- Performance tuning fundamentals
- Real-world use cases and deployment insights
- Comprehensive quiz to reinforce learning
Prerequisites
- Basic understanding of Linux command line
- Familiarity with fundamental programming concepts
- No prior Big Data experience required
What You'll Be Able to Do After
- Explain core Big Data characteristics and use cases
- Navigate and manage data in HDFS effectively
- Understand and apply Hadoop architecture components
- Run and interpret basic MapReduce and Spark jobs
- Identify and describe key Hadoop ecosystem tools