NoSQL Database

NoSQL databases are designed for flexible, scalable data storage that goes beyond the traditional table-based structure of relational databases. They handle unstructured, semi-structured, and structured data, making them ideal for big data and real-time web applications.

When to Use NoSQL vs. SQL

• Use NoSQL: When dealing with large volumes of unstructured data, requiring high scalability, or needing flexible data models (e.g., social media, IoT).
• Use SQL: When data integrity, complex queries, and transactions are crucial (e.g., banking systems, traditional enterprise applications).

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Types of NoSQL Databases

Document-Based

Store data as documents, usually in JSON or BSON format, which can contain nested structures.

• Example: MongoDB

Key-Value Stores

Store data as key-value pairs, similar to a dictionary, providing fast data retrieval.

• Example: Redis

Column-Family Stores

Organize data into rows and columns, but each row can have a different number of columns, making them suitable for large-scale, distributed systems.

• Examples: Apache Cassandra, ScyllaDB

Graph Databases

Use graph structures with nodes, edges, and properties to represent and store data, ideal for data with complex relationships.

• Example: Neo4j

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Basic Operations and Queries

Document-Based Example (MongoDB)

• Insert:
  

  db.books.insert({ title: "1984", author: "George Orwell", price: 8.99 });
  

• Query:
  

  db.books.find({ author: "George Orwell" });
  

Key-Value Store Example (Redis)

• Set:
  

  SET user:1 "John Doe"
  

• Get:
  

  GET user:1
  

Column-Family Store Example (Cassandra)

• Insert:
  

  INSERT INTO books (id, title, author, price) VALUES (1, '1984', 'George Orwell', 8.99);
  

• Query:
  

  SELECT * FROM books WHERE author = 'George Orwell';
  

Graph Database Example (Neo4j)

• Create Node:
  

  CREATE (a:Author {name: "George Orwell"})
  

• Create Relationship:
  

  MATCH (a:Author {name: "George Orwell"}), (b:Book {title: "1984"})
  CREATE (a)-[:WROTE]->(b)
  

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Case Studies and Examples

• Document-Based: MongoDB is used by companies like eBay to store and manage large amounts of data for its auction platform.
• Key-Value Store: Redis is employed by Twitter for caching and managing real-time tweet data.
• Column-Family Store: Apache Cassandra powers Netflix’s global streaming service, handling vast amounts of user data.
• Graph Database: Neo4j is utilized by LinkedIn to manage and analyze social connections and professional networks.

Exploring NoSQL databases opens up new possibilities for handling diverse and dynamic data, making them essential tools in modern data management.