NoSQL Databases in Big Data (MongoDB and Cassandra)
Traditional relational databases struggle with scalability, flexibility, and
performance when handling massive volumes of unstructured and semi-structured data.
NoSQL databases were designed to overcome these limitations.
In Big Data systems, NoSQL databases provide high availability, horizontal scalability,
and fast read-write performance. This chapter introduces NoSQL concepts with a focus
on MongoDB and Cassandra.
⭐ What is NoSQL?
NoSQL (Not Only SQL) databases are non-relational databases designed to store and
process large-scale data efficiently. They support flexible schemas and distributed
architectures.
📌 Why NoSQL Databases are Needed
- Handles massive data volumes
- Supports unstructured and semi-structured data
- Horizontal scalability
- High availability and fault tolerance
- Low-latency read and write operations
📌 Types of NoSQL Databases
- Key-Value Stores: Redis, DynamoDB
- Document Databases: MongoDB
- Column-Family Stores: Cassandra, HBase
- Graph Databases: Neo4j
⭐ MongoDB (Document-Based NoSQL Database)
MongoDB is a document-oriented NoSQL database that stores data in JSON-like
documents called BSON. It is widely used for applications that require flexibility
and rapid development.
📌 Features of MongoDB
- Schema-less document structure
- High performance and scalability
- Easy integration with applications
- Rich querying capabilities
📌 MongoDB Example
// Insert document
db.users.insertOne({
name: "John",
age: 30,
skills: ["Python", "Big Data"]
})
// Find documents
db.users.find({ age: { $gt: 25 } })
📌 MongoDB Use Cases
- Content management systems
- Real-time analytics
- Mobile and web applications
⭐ Apache Cassandra (Column-Family NoSQL Database)
Apache Cassandra is a highly scalable and distributed NoSQL database designed
for handling large amounts of data across multiple nodes with no single point
of failure.
📌 Features of Cassandra
- Peer-to-peer architecture
- Linear scalability
- High availability
- Optimized for write-heavy workloads
📌 Cassandra Data Model
- Keyspace
- Tables
- Rows and columns
- Partition keys and clustering keys
📌 Cassandra Example
CREATE TABLE users (
user_id UUID PRIMARY KEY,
name text,
age int
);
INSERT INTO users (user_id, name, age)
VALUES (uuid(), 'Alice', 28);
📌 Cassandra Use Cases
- Time-series data storage
- IoT applications
- Messaging platforms
- High-volume transactional systems
📌 MongoDB vs Cassandra
- MongoDB: Flexible schema, rich queries
- Cassandra: High write throughput, massive scalability
📌 Real-Life Applications
- Netflix streaming data
- Social media platforms
- Online gaming systems
- E-commerce personalization
📌 Project Title
Scalable Big Data Storage Using MongoDB and Cassandra
📌 Project Description
In this project, you will design a Big Data storage solution using MongoDB
for flexible document storage and Cassandra for high-speed, distributed
data access. This project demonstrates how NoSQL databases are used in
modern Big Data architectures.
📌 Summary
NoSQL databases play a critical role in Big Data systems by enabling scalable,
flexible, and high-performance data storage. MongoDB and Cassandra address
different Big Data needs and are widely adopted in enterprise applications.
This chapter completes your Big Data Technologies course.
