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Appendix: NoSQL Databases

Unlike traditional relational databases (SQL), which use structured tables and schemas, NoSQL databases use a variety of data models that allow for more dynamic and flexible data storage and retrieval.

Features​

Schema Flexibility​

Non-relational databases like MongoDB and DynamoDB do not require a fixed schema, allowing you to store data in a flexible, dynamic format. This is particularly useful for applications with rapidly changing requirements.

Horizontal Scalability​

These databases are designed to scale out by adding more servers (nodes) to the cluster, making them suitable for handling large volumes of data and high traffic loads.

Variety of Data Models​

Non-relational databases support various data models, including key-value, document, column-family, and graph models, allowing you to choose the best fit for your application's needs.

Distributed Architecture​

Both MongoDB and DynamoDB are designed for distributed environments, providing high availability and fault tolerance.

High-Performance Queries​

These databases often provide fast read and write operations, especially for large datasets, by leveraging in-memory caching and efficient indexing.

Types​

Key-Value Stores​

Data is stored as a collection of key-value pairs.

Examples: Redis, Amazon DynamoDB.

Use Cases: Caching, session management, and storing user profiles.

Document Stores​

Data is stored in documents, typically in JSON or BSON format.

Examples: MongoDB and CouchDB.

Use Cases: Content management systems, catalogs, and user data storage.

Column-Family Stores​

Data is stored in columns rather than rows, optimized for read and write operations on large datasets.

Examples: Apache Cassandra and HBase.

Use Cases: Real-time analytics, time-series data, and recommendation engines.

Graph Databases​

Data is stored as nodes, edges, and properties, representing complex relationships between data points.

Examples: Neo4j and Amazon Neptune.

Use Cases: Social networks, recommendation engines, and fraud detection.

Considerations​

Advantages​

  • Flexibility: Easily handle changes in data structures without requiring schema modifications.

  • Scalability: Efficiently manage large volumes of data by scaling horizontally.

  • Performance: Provide fast data access and high throughput for specific workloads.

  • Diverse Use Cases: Support various data models to fit different application needs.

Disadvantages​

  • Limited ACID Transactions: Often sacrifice consistency for availability and partition tolerance.

  • Complex Querying: Lack the powerful querying capabilities of SQL, making complex queries more challenging.

  • Data Duplication: May require denormalization, leading to potential data duplication and increased storage costs.

  • Learning Curve: Different data models and querying languages can present a learning curve for developers accustomed to SQL.