Hadoop Architecture_ Understanding HDFS, MapReduce, and YARN.pptx
- 1. Hadoop Architecture: Understanding HDFS, MapReduce, and YARN In today's data-driven world, organisations generate vast amounts of data daily. Managing and processing this massive volume of structured and unstructured data efficiently requires a robust framework. Apache Hadoop, an open-source distributed storage and processing system, has emerged as a powerful solution for handling big data. Its architecture is built on three key components: HDFS (Hadoop Distributed File System), MapReduce, and YARN (Yet Another Resource Negotiator). This blog explores these fundamental elements and their roles in Hadoop’s ecosystem. Hadoop Distributed File System (HDFS) HDFS serves as the storage component of Hadoop, intended to store and organise extensive datasets across numerous machines. It follows a master-slave architecture, where the NameNode serves as the master, and DataNodes function as the slaves. Key Features of HDFS: 1. Scalability: Through the addition of more nodes to the cluster, you can scale HDFS horizontally, making it suitable for growing data needs. 2. Fault Tolerance: In HDFS, data is replicated across multiple nodes, ensuring that a failure in one node does not result in data loss. 3. High Throughput: HDFS is optimised for large sequential read and write operations, making it ideal for big data applications. 4. Data Locality: It brings computation closer to the data, reducing network congestion and improving performance. MapReduce: The Processing Engine MapReduce is the data processing framework within Hadoop that enables parallel computation of large datasets. It follows a two-step process: Map and Reduce. How MapReduce Works: 1. Map Phase: In Map, the input data is divided into chunks, and each chunk in this phase is processed independently by the mapper function. It transforms the data into key-value pairs. 2. Shuffle and Sort: The key-value pairs generated in the intermediate stage are mixed and organised before they are sent to the reducer.
- 2. 3. Reduce Phase: The reducer aggregates the key-value pairs and produces the final output. Benefits of MapReduce: ● Parallel Processing: Multiple tasks run concurrently, significantly reducing processing time. ● Fault Tolerance: If a node fails, the task is reassigned to another node, ensuring uninterrupted execution. ● Efficient Resource Utilisation: It distributes workloads effectively across the cluster. YARN: Resource Management in Hadoop YARN (Yet Another Resource Negotiator) is the resource management layer of Hadoop, introduced in Hadoop 2.0. It decouples resource management and job scheduling, improving the overall efficiency of the system. Components of YARN: 4. ResourceManager: The master daemon that allocates resources to applications based on demand. 5. NodeManager: Runs on each node and monitors resource usage while reporting back to the ResourceManager. 6. ApplicationMaster: Manages the execution of individual applications and negotiates resources with the ResourceManager. Why YARN is Essential: ● Multi-Tenancy: Supports multiple frameworks like Apache Spark, Flink, and Hive, allowing diverse applications to run on Hadoop. ● Better Cluster Utilisation: Dynamically allocates resources based on demand, preventing resource wastage. ● Scalability and Flexibility: Handles large-scale processing efficiently across various applications. Why Learn Hadoop as Part of a Data Science Course? Hadoop is essential in big data, highlighting its importance as a skill for future data scientists. Many organisations leverage Hadoop for data storage and processing, especially in Data Science Course data-intensive applications. Enrolling in a equips professionals with hands-on experience in Hadoop and its ecosystem, ensuring they stay ahead in the competitive field of data science.
- 3. The Best Data Science Course in Pune The program covers Hadoop, HDFS, MapReduce, YARN, and other essential big data technologies. With expert trainers, real-world projects, and placement assistance, provides an industry-aligned learning experience that prepares professionals for high-paying roles in data science and big data analytics. Conclusion Understanding Hadoop’s architecture—HDFS for storage, MapReduce for processing, and YARN for resource management—is crucial for handling big data efficiently. With increasing data-driven applications across industries, learning Hadoop through a Data Science Course can significantly boost career prospects. Join Data Science Course in Pune today and gain expertise in cutting-edge big data technologies. Contact Us: Name: Data Science, Data Analyst and Business Analyst Course in Pune Address: Spacelance Office Solutions Pvt. Ltd. 204 Sapphire Chambers, First Floor, Baner Road, Baner, Pune, Maharashtra 411045 Phone: 095132 59011