Posts
All the articles I've posted.
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How HDFS Avoids Understanding File Content
One of the defining features of Hadoop Distributed File System (HDFS) is that it doesn’t understand the contents of the files it stores . This is not a limitation—it's an intentional design choice
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How Spark and MapReduce Handle Partial Records in HDFS
When working with large-scale data processing frameworks like Apache Spark or Hadoop MapReduce, one common question arises: What happens when a record (e.g., a line of text or a JSON object) is split
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How HDFS Tracks Block Size and File Boundaries
When dealing with massive files, Hadoop Distributed File System (HDFS) doesn't read or store them as a whole. Instead, it splits them into large, fixed-size blocks . But how does it know where each
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How Metadata Works in HDFS and What It Stores
HDFS stores metadata separately from the actual file content to optimize performance and scalability. This metadata is managed entirely by the NameNode , which allows clients to quickly locate and
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The Architecture of HDFS: NameNode, DataNodes, and Metadata
HDFS (Hadoop Distributed File System) was built to support the reliable storage and access of large datasets distributed across commodity hardware. To make this possible, HDFS relies on a master/slave
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What Happens When HDFS Splits Files Mid-Word or Mid-Row?
HDFS is designed to store and process massive amounts of data efficiently. One of its key design decisions is to split files into large, fixed-size blocks , typically 128MB or 256MB. But what happens
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How HDFS Handles File Partitioning and Block Distribution
One of the key innovations behind the Hadoop Distributed File System (HDFS) is how it breaks down large files and distributes them across multiple machines. This mechanism, called partitioning and
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What is HDFS and Why Was It Revolutionary for Big Data?
In the early 2000s, the world was generating data at a scale never seen before—web logs, social media, sensors, and more. Traditional storage systems simply couldn't keep up with the volume, velocity,