Big Data refers to extremely large datasets that grow rapidly and come from multiple sources. Traditional systems struggle to process such massive and complex data efficiently. Hadoop provides a distributed framework to store and process Big Data at scale.
- Designed for distributed storage and parallel data processing
- Handles structured, semi-structured and unstructured data
- Fault-tolerant and scalable across clusters of machines
Understanding Big Data
This section builds the foundation required to understand why Hadoop was created.
Fundamentals of Hadoop
This section introduces Hadoop as a solution to Big Data challenges.
Installation and Environment Setup
This section guides you through installing Hadoop and configuring your environment.
- Install Hadoop in Linux
- Installing and Setting Up Hadoop in Windows
- Installing Single Node Cluster Hadoop on Windows
- Configuring Eclipse with Apache Hadoop
Hadoop Ecosystem Tools
Hadoop consists of core components that manage storage, processing and resource allocation.
- Core components: Hadoop Distributed File System(HDFS), YARN, MapReduce
- Storage Tools: HBase
- Data Processing: Spark, Flink
- Data Query & Analysis: Hive, Pig, Presto
- Data Ingestion: Sqoop, Kafka
- Coordination Tool: Zookeeper
Understanding Cluster, Rack and Schedulers
This section explains how Hadoop organizes machines and manages tasks efficiently.
- Hadoop Cluster
- Cluster, Properties and Types
- Rack and Rack Awareness
- Hadoop Schedulers
- Different Modes of Operation
Understanding HDFS
HDFS is Hadoop’s distributed file system designed for large-scale storage.
Understanding MapReduce
MapReduce is Hadoop’s programming model for processing big data.
- Architecture
- Mapper
- Reducer
- Job Execution Flow
- Data Flow in MapReduce
- Job Initializations
- Job run on MapReduce
- Task Completion
MapReduce Programs
This section provides practical examples of MapReduce programs.
Hadoop Streaming & File System Commands
This section covers Hadoop Streaming along with essential Hadoop file system commands that help in running MapReduce programs and managing data in HDFS efficiently.