Open Source Framework for storing and processing big data on clusters of commodity hardware

In today's data-driven world, managing and processing large volumes of data has become a major challenge for organisations. This is where Hadoop Distributed File System (HDFS) comes into play. HDFS is a distributed file system that is designed to store and manage large datasets across multiple machines.
In this blog, we will explore the key features of HDFS and how it works..

Architecture of HDFS:

The HDFS architecture consists of two main components: NameNode and DataNode. The NameNode manages the metadata of the file system, including the directory structure, file names, and permissions. The DataNode stores the actual data.

HDFS works by dividing large files into smaller blocks, typically 128MB or 256MB in size. These blocks are then distributed across multiple machines in the Hadoop cluster. Each block is replicated across several machines to ensure fault tolerance.

The NameNode keeps track of the location of each block and the replication factor. It also manages the allocation of new blocks and the deletion of old blocks.

Data processing with HDFS:

HDFS is designed to work seamlessly with other Hadoop components, such as MapReduce and YARN. MapReduce is a programming model used for processing large datasets, while YARN is a resource management system that allocates resources to running applications.

When processing data with Hadoop, the MapReduce framework reads data from HDFS and processes it in parallel across multiple machines. The results are then written back to HDFS.

Benefits of HDFS:

One of the main benefits of HDFS is its scalability. HDFS is designed to handle large volumes of data, and it can scale horizontally by adding more machines to the cluster.

HDFS is also fault-tolerant. Because data is replicated across multiple machines, HDFS can continue to function even if some machines fail.

Another benefit of HDFS is its cost-effectiveness. HDFS runs on commodity hardware, which is much cheaper than specialized hardware.

Conclusion:

HDFS is a powerful distributed file system that is designed to store and manage large volumes of data. Its architecture allows for horizontal scalability, fault tolerance, and cost-effectiveness. With its ability to work seamlessly with other Hadoop components, such as MapReduce and YARN, HDFS has become a critical component of many big data architectures.