Database develop. life cycle - Cloud databases

1. What is a Cloud Database?

A cloud database is a database that runs on a cloud computing platform rather than on local servers.

  • It can be relational (SQL) or non-relational (NoSQL).

  • Managed and hosted by a cloud service provider.

Examples:

  • SQL: Amazon RDS, Google Cloud SQL, Azure SQL Database

  • NoSQL: Amazon DynamoDB, MongoDB Atlas, Firebase

2. Types of Cloud Databases

  1. Relational Cloud Databases (SQL)

    • Structured data stored in tables with relationships.

    • Supports ACID transactions.

    • Example: Amazon RDS (PostgreSQL, MySQL), Google Cloud SQL.

  2. Non-Relational Cloud Databases (NoSQL)

    • Flexible schema, suitable for unstructured or semi-structured data.

    • Can scale horizontally easily.

    • Example: MongoDB Atlas, Firebase, Amazon DynamoDB.

  3. NewSQL Cloud Databases

    • Combines SQL features (ACID) with NoSQL scalability.

    • Example: Google Spanner, CockroachDB.

3. Advantages of Cloud Databases

  • Scalability: Easily scale up or out as data and user load grows.

  • Cost-Effective: Pay-as-you-go model; no need for heavy upfront hardware investment.

  • High Availability: Cloud providers ensure redundancy and failover.

  • Managed Services: Automatic backups, updates, and maintenance.

  • Global Access: Accessible from anywhere with an internet connection.

4. Disadvantages of Cloud Databases

  • Dependence on Internet: No connection → no access.

  • Vendor Lock-in: Switching providers can be difficult.

  • Security Concerns: Data stored off-premises; requires strong encryption and access control.

  • Latency: Slightly higher access times than local databases in some cases.

5. Key Considerations When Choosing a Cloud Database

  1. Data Structure: Relational vs Non-relational

  2. Scalability Needs: How much traffic and data growth is expected

  3. Availability & Disaster Recovery: SLA requirements

  4. Security & Compliance: Encryption, GDPR/HIPAA compliance

  5. Cost: Pay-as-you-go vs flat-rate models

  6. Integration: Compatibility with existing apps and analytics tools

6. Example Use Cases

  • E-commerce: Product catalogs, orders, user accounts → Cloud SQL or NoSQL

  • Social Media: User profiles, posts, messages → NoSQL (MongoDB Atlas, DynamoDB)

  • Banking / Finance: Transactions, accounts → Cloud SQL / NewSQL (Google Spanner)

  • IoT & Analytics: Sensor data, logs → NoSQL, scalable cloud storage