215824116_JABEZ_DBMS - bi215824116 M.Sc. Bioinformatics.pptx

Hadoop

1. Introduction to Hadoop Hadoop is an open-source software framework for storing and processing large datasets . It provides massive storage for any kind of data, enormous processing power, and the ability to handle virtually limitless concurrent tasks or jobs

2. Hadoop Architecture Components The architecture consists of HDFS, MapReduce Scalability Hadoop's architecture is designed to scale from single servers to thousands of machines. Flexibility The modular architecture allows for easy expansion and compatibility with different systems.

3. Hadoop Distributed File System (HDFS) 1 Fault Tolerance HDFS replicates data across multiple nodes for fault tolerance. 2 Data Locality Data is stored in close proximity to the computation, reducing network traffic. 3 Scalability HDFS can seamlessly scale to petabytes of data on commodity hardware.

4. MapReduce Data Processing Map and Reduce tasks process large datasets in parallel. Scalability MapReduce provides a scalable and fault-tolerant framework for data processing. Efficiency It allows for high-throughput computation and processing of big data.

5. Hadoop Ecosystem Integration Hadoop ecosystem includes various tools for ingestion, processing, and analysis. Extensibility Provides an open environment for integrating new technologies and components. Community Active community support and continuous development of new ecosystem projects.

6. Conclusion Hadoop revolutionized the field of big data and continues to be a driving force in data analytics and distributed computing. Its rich ecosystem, versatility, and scalability make it a pivotal tool for modern data-driven businesses .