Hadoop HDFS | HDFS Explained: Master-Slave Architecture, Name Node, Data Node & Block Size in Hadoop

In this lecture, we focus on HDFS (Hadoop Distributed File System)—the backbone of Hadoop’s data storage capabilities. Understanding the inner workings of HDFS is crucial for any data engineer or big data professional.

We’ll explore the Master-Slave Architecture of HDFS, which involves the NameNode and DataNode components, and how data is stored and managed across distributed systems. We’ll also cover key concepts like NameNode Federation and the role of the Secondary NameNode in maintaining system integrity. Additionally, we explain why the default block size in HDFS is 128MB and how it enhances performance and efficiency in distributed data storage.

HDFS Master-Slave Architecture: Understand the roles of NameNode (master) and DataNode (slave) in data storage and management.

NameNode Federation: Learn how NameNode Federation improves scalability and performance by allowing multiple NameNodes.

Secondary NameNode: Discover the role of the Secondary NameNode in handling periodic snapshots of the NameNode’s metadata.

128MB Block Size: Understand why HDFS uses a default block size of 128MB, and how it helps in reducing seek time and managing large datasets.

By the end of this lecture, you’ll have a deep understanding of HDFS architecture and its role in efficiently storing and processing big data. This session is essential for mastering Hadoop’s distributed storage capabilities.

HDFS
Hadoop Distributed File System
Master-Slave Architecture
NameNode
DataNode
NameNode Federation
Secondary NameNode
HDFS Block Size
128MB Block Size in HDFS
HDFS Architecture
Distributed File Systems
Hadoop Storage
HDFS Metadata
Data Storage in Hadoop
Big Data Storage
HDFS Performance
Distributed Computing
Hadoop Data Management

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