NoSQL databases offer schema flexibility and scalability, suitable for diverse data structures and high-volume data.
Notes
Eric Evans
The term “NoSQL” does not mean “no SQL” but “Not Only SQL”.
NoSQL databases are non-relational databases that provide flexible data models and high scalability, contrasting with traditional relational databases.
NoSQL databases emerged to address limitations of relational databases in handling large volumes of unstructured or semi-structured data and the need for horizontal scalability. Unlike relational databases that enforce rigid schemas and use SQL for querying, NoSQL databases offer flexible data models (document, key-value, graph, column-family) and different query mechanisms. This flexibility makes them well-suited for modern applications with diverse data requirements and high traffic loads.
TakeAways
- 💾 NoSQL databases offer alternatives to traditional relational databases.
- Provide flexible data models (document, key-value, graph, column-family).
- Designed for high 🕸️ Scalability and performance.
- Do not rely on SQL as the primary query language.
- 💡 NoSQL databases are often categorized by their data model: document (e.g., 🧩 MongoDB), key-value (e.g., ⚡ Redis), graph (e.g., 💾 Neo4j), and column-family (e.g., Cassandra).
- 🔍 NoSQL databases are often chosen for use cases involving big data, real-time data processing, and cloud-native applications.
Process
- ⚙️ Choose a NoSQL database based on your application’s data model and requirements.
- 🔗 Connect to the database from your application using appropriate drivers or client libraries.
- 📄 Design your data model based on the chosen NoSQL database type.
- 📑 Implement data access patterns using the database’s specific query language or API.
- 🚀 Scale the database horizontally as needed to handle growing data volumes and traffic.
Thoughts
- 🚫 Non-Relational: NoSQL databases do not use the relational model of tables with fixed schemas.
- 💾 Data Models: Offer diverse data models (document, key-value, graph, column-family) to fit various use cases.
- 🌐 Scalability: Designed for horizontal scalability to handle massive datasets and high throughput.
- ⚡️ Performance: Can offer better performance for specific use cases compared to relational databases.
- 🧩 Flexibility: Provide greater flexibility in data structure and schema evolution.
- 🧰 Diverse Use Cases: Suitable for a wide range of applications, including web applications, mobile apps, big data analytics, and real-time data processing.