NoSQL Databases Overview

NOSQL Databases

• NoSQL (non-relational databases) are designed to store, retrieve, and manage non-tabular data.
• They are schema-less or flexible in schema design.
• They can handle various data types including structured, semi-structured (JSON), and unstructured (text, images, logs).
• They offer horizontal scalability.

When to Use NoSQL Databases

• When scalability is necessary, easily handling large data volumes with horizontal scaling is a key benefit.
• Flexibility is needed and dynamic schema design enables quick iteration and changes.
• When performance is critical, optimized models and access patterns are advantageous.
• Suited for handling big data and real-time analytics in big data and real-time applications.

Types of NoSQL Databases: Document Stores

• Document stores are used for managing content, like websites.
• Documents within a collection can have different fields or structures.
• No strict schema enforcement allows flexibility in storing varied data formats within a single collection.
• Useful for storing products with varying attributes by category.
• Example use: A news website stores articles as documents with fields like title, body, author, tags, and publication date.
• MongoDB is an example of a document store database. The flexible schema enables easy addition of new fields (e.g. multimedia content) without altering existing documents.

Key-Value Stores

• Used for caching mechanisms and session management, including real-time bidding systems.
• Example use: Online shopping platforms use Redis to manage user sessions, storing session data (cart contents, authentication tokens) using a unique session ID as the key.
• Redis is an example of a key-value store database. Quickly accessing and updating session information ensure a smooth shopping experience.

Graph Databases

• Designed for efficiently storing, managing, and querying highly interconnected data.
• Represents data as nodes (entities) connected by relationships (edges).
• Suitable for scenarios requiring analysis of relationships between data points, like social networks and recommendation systems.
• Example use: Social media platforms use Neo4j to manage social graphs, including friendships, messages, and likes.

Neo4j Products

• Neo4j Database: The core product, available in community and enterprise editions. This is a graph database.
• Neo4j Aura: A fully managed cloud service for running and deploying Neo4j databases that manages infrastructure, ensuring scalability, security, and reliability.
• Neo4j Desktop: A desktop application for a development environment, enabling users to create, manage, and explore local Neo4j databases.
• Neo4j Bloom: A graph visualization and exploration tool that helps users uncover insights and explore graph data intuitively using natural language search and interactive visualizations.
• Neo4j Graph Data Science Library: A collection of algorithms and tools for analyzing graph data (centrality, community detection, similarity analysis). Enables advanced graph analytics.
• Neo4j AuraDB for Google Cloud: A fully managed graph database service on GCP (Google Cloud Platform).