Data-Persistence-Strategies
Data persistence strategies involve the methods and technologies used to store, retrieve, and manage data in a manner that ensures its longevity and reliability across system restarts, failures, and upgrades.
Goal
The goal is to select and implement effective data storage solutions that align with the application's requirements, ensuring data integrity, availability, and performance.
Context
There are different data storage options available but they are not all equal in terms of performance, security, and maintainability. By defining the data persistence strategy, teams can ensure that the chosen storage solutions meet the application's specific needs.
Types
| Type | Description | Use Cases | Examples |
|---|---|---|---|
| Relational Databases | Structured data storage using tables with fixed schemas. Ideal for complex queries and transactions. | Financial systems, e-commerce platforms, and customer relationship management (CRM) systems. | MySQL, PostgreSQL, Microsoft SQL Server. |
| NoSQL Databases | Flexible data models for document, key-value, wide-column, or graph data. Suitable for scalable, high-performance needs. | Real-time analytics, content management systems, and IoT applications. | MongoDB, Cassandra, Redis, Neo4j. |
| In-Memory Databases | Stores data in the main memory to facilitate rapid access and processing. Best for time-sensitive operations. | Caching, session management, and real-time analytics. | Redis, Memcached, Hazelcast. |
| File Systems | Persistent storage for data that doesn't fit well into databases, such as logs, multimedia files, or large data sets. | Media streaming, content management, and archival systems. | Amazon S3, Google Cloud Storage, Azure Blob Storage. |
Inputs
| Artifact | Description |
|---|---|
| User Stories | Detailed descriptions of the functionality, performance criteria, and interfaces needed for each component. |
Outputs
| Artifact | Description | Benefits |
|---|---|---|
| Data Schema | The structure that defines the organisation of data within the chosen storage solution. | Ensures data consistency and integrity. Supports efficient data access and manipulation. |
| Data Access Layer | Code that manages communication between the application and the database or storage mechanism. | Decouples the application logic from data storage concerns. Improves maintainability and scalability. |
Anti-patterns
- Over-Reliance on One Type of Storage: Using a single storage solution for all data needs, ignoring the benefits of diversifying data storage options.
- Ignoring Data Migration and Evolution: Failing to plan for data schema changes, leading to issues with data integrity and application updates.