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Title: No SQL

CO addressed: CO1

Course: Big Data Analytics

Presented by: Mr. P. Kiran Kumar

Department: ISE

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NoSQL (Not Only SQL):

What is NoSQL?

NoSQL (Not Only SQL) is a type of non-relational database designed to store and manage large volumes of structured, semi-structured, and unstructured data. It is built to handle modern applications that require high scalability, flexibility, and fast performance.

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• NoSQL stands for Not Only SQL.
• It does not rely on traditional tables and fixed schemas like relational databases.
• It stores data in flexible formats such as documents, key-value pairs, columns, or graphs.
• It is widely used in Big Data, cloud computing, social media, IoT, and real-time web applications.

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History

The term NoSQL was first coined by Carlo Strozzi in 1998.

Today, NoSQL refers to a family of modern databases that support distributed storage and horizontal scaling.

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Features of NoSQL

1. Open Source

Many NoSQL databases are open source, meaning anyone can download, use, and modify them. This reduces licensing costs and encourages community-driven improvements.

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Example: MongoDB Community Edition and Redis.

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2. Non-Relational

Unlike SQL databases, NoSQL databases do not store data in tables. They support multiple data models such as:

• Documents (MongoDB)
• Key-Value pairs (Redis)
• Column Families (Bigtable)
• Graphs (Amazon Neptune)

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This makes them suitable for storing different types of data.

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3. Distributed

NoSQL databases distribute data across multiple servers (nodes). If one server fails, the others continue to serve requests.

Benefits:

• High Availability
• Faster Data Access
• Better Reliability

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4. Schema-less

A NoSQL database does not require a predefined structure.

Example:

Employee 1

{

"Name": "Rahul",

"Age": 25

}

Both documents can exist in the same collection, making it easy to add new fields without changing the database design.

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5. Cluster Friendly

NoSQL databases can run on clusters containing many servers.

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If one server goes down:

• Other servers continue working.
• The application remains available.
• Downtime is minimized.

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6. Designed for Modern Web Applications

Modern applications generate huge amounts of data every second. NoSQL databases are designed to handle this scale efficiently.

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Examples:

• Facebook – User posts and comments
• Netflix – Viewing history and recommendations
• Amazon – Product catalog and customer reviews
• Instagram – Photos, likes, and followers

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Where is NoSQL Used?

Applications of NoSQL Databases

1. Big Data Analytics

• Stores and processes massive volumes of data generated from multiple sources.
• Supports real-time analysis and decision-making.
• Commonly used with Big Data technologies like Hadoop and Spark.

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2. Social Media Platforms

• Manages user profiles, posts, comments, likes, messages, and follower relationships.
• Handles millions of user interactions every second.
• Supports continuously growing and changing data.
• Examples: Facebook, Instagram, X (Twitter), LinkedIn.

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3. E-Commerce Applications

• Stores product catalogs, customer information, shopping carts, and product reviews.
• Allows quick updates to inventory and pricing.
• Provides personalized product recommendations.
• Examples: Amazon, Flipkart.

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4. Real-Time Web Applications

• Processes live data with very low latency.
• Used in applications where instant response is required.

Examples:

• Ride booking (Uber, Ola)
• Food delivery (Swiggy, Zomato)
• Online gaming
• Live chat applications

5. Log and Event Data Storage

• Stores server logs, application logs, and machine-generated data.
• Helps monitor system performance and detect errors.
• Supports security analysis and troubleshooting.

6. Internet of Things (IoT)

• Stores continuous data generated by sensors and smart devices.
• Handles high-speed data streams from thousands of connected devices.
• Used in smart homes, healthcare, and industrial automation.

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Characteristics of NoSQL Databases

1. Non-Relational Database

• NoSQL databases do not use traditional tables with rows and columns.
• Data is stored as documents, key-value pairs, column families, or graphs.
• Suitable for handling complex and rapidly changing data.

2. Distributed Architecture

• Data is distributed across multiple servers (nodes) in a cluster.
• Supports horizontal scaling by simply adding more servers.
• Ensures high availability, load balancing, and fault tolerance.

3. CAP Theorem

Instead of focusing only on ACID transactions like SQL databases, NoSQL databases are designed based on the CAP Theorem, which states that a distributed database can guarantee only two of the following three properties at the same time:

• Consistency (C): Every user sees the same, most recent data.
• Availability (A): Every request receives a response, even during failures.
• Partition Tolerance (P): The system continues to operate even if communication between servers is interrupted.

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4. Flexible (Schema-less) Design

• No predefined schema is required before storing data.
• Different records in the same collection can have different fields.
• Makes it easy to modify applications without redesigning the database.

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5. Horizontal Scalability

• Capacity is increased by adding more servers instead of upgrading one server.
• Enables handling of billions of records and millions of user requests.
• Ideal for cloud-based and Big Data applications.

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6. High Performance

• Optimized for fast read and write operations.
• Supports real-time applications with low latency.
• Efficiently processes large datasets.

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Types of NoSQL Databases

NoSQL databases are classified into four major types, based on how they store and organize data. Each type is optimized for different kinds of applications.

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1. Document Database

• Stores data as documents in formats such as JSON, BSON, or XML.
• Each document contains data and its structure together.
• Flexible schema allows documents in the same collection to have different fields.
• Best suited for applications with dynamic or frequently changing data.

Example: MongoDB

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Applications:

• E-commerce product catalogs
• Content Management Systems (CMS)
• User profiles
• Blogging platforms

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2. Key-Value Database

• Stores data as Key → Value pairs.
• Each key is unique and is used to retrieve its corresponding value quickly.
• Very fast for read and write operations.
• Simple and highly scalable.

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Examples: Redis, Oracle Coherence

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Applications:

• Session management
• Shopping carts
• Gaming leaderboards

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3. Column-Family Database

• Stores data in columns instead of rows.
• Related columns are grouped into column families.
• Optimized for handling very large datasets and analytical queries.
• Efficient for reading specific columns without scanning the entire row.

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Example: Bigtable

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Applications:

• Big Data analytics
• Banking systems
• Data warehousing
• Time-series data

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4. Graph Database

• Stores data as nodes (entities) and edges (relationships).
• Designed to manage highly connected data efficiently.
• Makes relationship-based queries much faster than relational databases.

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Example: Amazon Neptune

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Applications:

• Social networking
• Recommendation systems
• Fraud detection
• Network analysis

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Why NoSQL?

As data volumes and application requirements have grown, traditional relational databases have faced limitations in scalability and flexibility. NoSQL databases were developed to overcome these challenges by providing better performance, scalability, and support for diverse data types.

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Why Choose NoSQL?

1. Horizontal Scalability (Scale-Out Architecture)

• NoSQL databases use horizontal scaling, where new servers can be added to increase storage and processing capacity.
• Unlike SQL databases, there is no need to upgrade to a single, expensive server.
• Suitable for applications with millions of users.

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2. Handles Large Volumes of Data

• Efficiently stores structured, semi-structured, and unstructured data.
• Can manage terabytes or petabytes of data generated by modern applications.
• Ideal for Big Data environments.

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3. Dynamic Schema

• Data can be inserted without defining a fixed schema.
• New fields can be added anytime without modifying existing records.
• Supports rapid application development and easy maintenance.

4. Auto-Sharding

• Automatically distributes data across multiple servers (shards).
• Balances storage and query load among available servers.
• New servers can be added without affecting application performance.

Benefit: Faster processing and better scalability.

5. Replication

• Creates multiple copies of data across different servers.
• If one server fails, another server continues to provide the data.
• Ensures high availability, fault tolerance, and disaster recovery.

6. High Performance

• Optimized for fast read and write operations.
• Supports real-time applications with millions of concurrent users.
• Reduces response time even with very large datasets.

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Advantages of NoSQL Databases

Advantages of NoSQL

1. Easy to Scale (Horizontal Scaling)

• NoSQL databases can easily scale by adding more servers.
• Supports cloud-based environments with minimal downtime.
• Can handle increasing users and data without affecting performance.
• Example: A social media application can add more servers as the number of users grows.

2. No Predefined Schema

• Does not require a fixed table structure before storing data.
• Different records can have different fields.
• Makes application development faster and more flexible.
• Example:
• A customer record can include only Name and Email, while another customer record may also include Phone Number and Address.

3. Cost-Effective

• Can run on low-cost commodity hardware instead of expensive high-end servers.
• Most NoSQL databases are open source, reducing software licensing costs.
• Lower maintenance and operational expenses.

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4. High Performance

• Optimized for high-speed read and write operations.
• Supports thousands or even millions of database operations per second.
• Suitable for real-time applications such as online shopping and live streaming.

5. High Availability and Fault Tolerance

• Data is replicated across multiple servers.
• If one server fails, another server immediately provides the data.
• Ensures continuous service with minimal downtime.

6. Supports Sharding and Replication

• Sharding: Splits data across multiple servers to improve performance.
• Replication: Creates multiple copies of data for backup and reliability.
• Improves scalability and disaster recovery.

7. Handles All Types of Data

• Stores structured, semi-structured, and unstructured data.
• Suitable for text, images, videos, sensor data, JSON documents, and log files.
• Ideal for Big Data applications

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Sharding and Replication in NoSQL

1. Sharding

Sharding is the process of dividing a large database into smaller parts (called shards) and storing them on different servers.

• Each server stores only a portion of the data.
• Data and query load are distributed across multiple servers.
• New servers can be added as the database grows.

Advantages of Sharding

• Improves database performance.
• Supports horizontal scaling.
• Reduces server workload.
• Handles large datasets efficiently.

Example

• Suppose an online shopping website has 12 million customer records.
• Instead of storing all records on one server:
• Server 1: Customers A – F, Server 2: Customers G – M, Server 3: Customers N – Z

Each server stores only part of the data, making searches much faster.

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2. Replication

Replication is the process of creating multiple copies of the same data and storing them on different servers.

If one server fails, another server immediately provides the data.

Advantages of Replication

• High Availability
• Fault Tolerance
• Disaster Recovery
• Data Backup
• Improved Read Performance

Example

• An online banking application stores customer data on:
• Primary Server
• Secondary Server
• Backup Server

If the primary server crashes, users continue accessing data from the secondary server without interruption.

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NewSQL:

NewSQL is a modern class of relational databases that combines the scalability of NoSQL with the ACID transaction support of traditional SQL databases.

It was developed to overcome the limitations of both SQL and NoSQL databases by providing high performance, horizontal scalability, and strong transaction consistency.

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Why NewSQL?

Traditional SQL databases provide:

• ACID transactions
• Strong consistency
• Limited horizontal scalability

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NoSQL databases provide:

• High scalability
• High availability
• Limited ACID support

NewSQL combines the strengths of both SQL and NoSQL.

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Characteristics of NewSQL

1. Relational Data Model

• Stores data in tables with rows and columns.
• Uses relationships between tables like traditional SQL databases.

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2. SQL Interface

• Uses standard SQL for querying and managing data.
• Existing SQL applications can be migrated more easily.

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3. ACID Compliance

• Supports Atomicity, Consistency, Isolation, and Durability.
• Ensures reliable and secure transactions.

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4. Horizontal Scalability

• Can scale by adding more servers to the cluster.
• Handles growing workloads without affecting performance.

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5. High Performance

• Optimized for processing thousands or millions of transactions per second.
• Suitable for large-scale OLTP (Online Transaction Processing) systems.

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6. Distributed Architecture

• Data is distributed across multiple servers.
• Provides high availability and fault tolerance.

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Advantages of NewSQL

• Combines SQL reliability with NoSQL scalability.
• Supports ACID transactions.
• Uses standard SQL language.
• Handles high transaction volumes.
• Provides better performance for modern enterprise applications.
• Suitable for cloud and distributed environments.

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