A file system is the method of storing and organizing data on a storage device

A DFS storage device ensures redundancy and high availability by copying data to multiple locations through replication.

Simple, fast access data storage, non relational in nature, fast read/write capability

Multiple smaller file are generally combined into single file to enable optimum storage and processing

Do not provide out-of-the-box redundancy and fault tolerance.

Less ideal for long-term storage of data that accumulates over time.

Manually sharded – complicates data processing when data from multiple shards is required.

Schema-based, not suitable for semi- & un-structured data

Data need to be checked against schema constraints – creates latency.

This is a transaction management approach that uses pessimistic concurrency controls to maintain consistency by applying record locks.

ensures that all operations will always succeed or fail completely

ensures that the database will always remain in a consistent state by ensuring that only data that conforms to the constraints of the database schema.

ensures that the results of a transaction are not visible to other operations until it is complete.

is splitting a big dataset into smaller parts called shards.

Each shard is stored on a different server or machine.

Every server holds only the data assigned to it.

All shards have the same structure and together they make up the full dataset

stores multiple copies of a dataset, replicas, on multiple nodes

Provides scalability and availability due to the fact that the same data is replicated on various nodes.

Fault tolerance is achieved since data redundancy ensures that data is not lost when an individual node fails.

In master-slave replication, data is written to a master node and then copied to multiple slave nodes.

Write requests (insert, update, and delete) go to the master, while read requests can be handled by any slave.

Each node, a peer, is equally capable of handling reads and writes.

Good for semi structured, unstructured and structured data

One replica on local node, another replica on a remote rack, Third replica on local rack, Additional replicas are randomly placed

Data placement exposed so that computation can be migrated to data

data not sent through namenode, client acces data directly from datanode

Throughput of file system scales nearly linearly with the number of nodes.

file divided into one or more blocks, and each block is stored different slave machines depend on replication factor

stores and manages the file system namespace, that is information about blocks of files like block locations, permissions,

It manages the file system structure and grants access permissions.

HDFS splits large files into blocks stored on data nodes.

The NameNode keeps track of which blocks on which nodes form the complete file, and it handles all file operations like reads, writes, and replication on data nodes.

They hold blocks of files and are resilient.

The NameNode manages access and replication of blocks across multiple DataNodes.

This replication system works efficiently when all nodes are grouped into racks.

The NameNode uses a "rack ID" to organize and track DataNodes in the cluster.

This ensures fault-tolerance and minimizes latency for reading and writing data.

By choosing the closest rack, latency is reduced. This setup increases data availability, reliability, and improves network bandwidth

the first on the same node,

the second on a different rack, and