Apache Hadoop is an open-source framework designed for the distributed storage and processing of large data sets across clusters of computers. It is designed to scale up from a single server to thousands of machines, each offering local computation and storage. Hadoop is primarily built on the principles of fault tolerance, scalability, and distributed computing.
Key Components of Hadoop:
- Hadoop Distributed File System (HDFS):
- HDFS is the storage layer of Hadoop. It splits large files into blocks (typically 128 MB or 256 MB) and stores them across multiple nodes in the cluster.
- It ensures data redundancy by replicating each block (default replication factor is 3) to avoid data loss in case of node failure.
- HDFS provides high throughput and is optimized for large read and write operations.
- MapReduce:
- MapReduce is the processing engine of Hadoop. It breaks down data processing tasks into two main phases: the “Map” phase and the “Reduce” phase.
- In the Map phase, data is processed in parallel across the nodes. In the Reduce phase, the results from the Map phase are aggregated.
- This model enables efficient, scalable data processing, making it suitable for tasks like large-scale log analysis, data mining, and machine learning.
- YARN (Yet Another Resource Negotiator):
- YARN is the resource management layer in Hadoop. It manages the cluster’s resources and schedules jobs across the available nodes.
- It provides a way to allocate resources dynamically, enabling multiple applications to run concurrently on the same Hadoop cluster without interfering with one another.
- Hadoop Common:
- This component includes the necessary libraries, utilities, and APIs required to run the other components of Hadoop. It provides the underlying infrastructure to support HDFS, MapReduce, and YARN.
Hadoop Ecosystem:
Over time, a rich ecosystem of tools and projects has been built around Hadoop to extend its capabilities. Some of these include:
- Hive: A data warehouse built on top of Hadoop that allows SQL-like querying of large datasets.
- HBase: A distributed NoSQL database for real-time access to large amounts of sparse data.
- Pig: A high-level platform for creating MapReduce programs using a scripting language known as Pig Latin.
- Spark: A fast, in-memory data processing engine that can work in tandem with Hadoop, offering faster analytics than MapReduce.
- Oozie: A workflow scheduler system to manage Hadoop jobs.
- Flume and Sqoop: Tools for data ingestion into Hadoop from various sources like databases, log files, etc.
Use Cases:
Hadoop is used in various industries for a wide range of applications, such as:
- Big Data Analytics: Processing massive amounts of structured and unstructured data to gain insights.
- Data Warehousing: Storing large volumes of data for querying and analysis.
- Machine Learning: Training machine learning models on big data.
- Log Processing: Analyzing logs for insights into application performance or user behavior.
Hadoop’s ability to scale and handle massive datasets makes it one of the go-to platforms for big data processing, especially in environments that require high availability and fault tolerance.
