Apache Hadoop: An open-source framework for distributed storage and processing of large datasets, designed for big data analytics and applications.
Advantages
- Scalability: Easily scales to handle vast amounts of data.
- Fault Tolerance: Resilient to hardware failures.
- Cost-Effective: Utilizes commodity hardware for cost savings.
- Parallel Processing: Distributes data processing across nodes.
- Ecosystem: Offers a wide range of tools for various big data tasks.
Disadvantages
- Complexity: Setup and configuration can be complex.
- Programming Model: Requires knowledge of MapReduce and related paradigms.
- Latency: Real-time processing is a challenge.
- Storage Overhead: Replication for fault tolerance consumes storage.
- Resource Intensive: Demands significant computational resources.
Components
- Hadoop Distributed File System (HDFS): Distributed file storage.
- MapReduce: Programming model for processing data.
- YARN (Yet Another Resource Negotiator): Resource management.
- Hadoop Common: Shared utilities and libraries.
- Hadoop Ecosystem: Additional projects like Hive, Pig, and Spark.
Development tools
- Hadoop Command-Line Tools: Utilities for managing Hadoop clusters.
- Hadoop Streaming: For writing MapReduce programs in any language.
- Hive: A data warehousing and SQL-like query language.
- Pig: A high-level scripting platform for data analysis.
- Apache Spark: A fast and general-purpose data processing engine for big data.