Database develop. life cycle - Choosing the right DBMS (SQL, NoSQL, NewSQL)

1. SQL Databases (Relational DBMS)

  • Examples: MySQL, PostgreSQL, Oracle, SQL Server

  • Model: Tables (rows & columns), strong schema, ACID transactions

  • Best for:

    • Structured data with clear relationships

    • Complex queries (JOINs, aggregations)

    • Applications needing high data integrity (banking, ERP, HR systems)

  • Pros: Mature, reliable, strong consistency, rich query language (SQL)

  • Cons: Vertical scaling (harder to scale horizontally for very large datasets)

2. NoSQL Databases

  • Types: Document (MongoDB), Key-Value (Redis), Column (Cassandra), Graph (Neo4j)

  • Model: Flexible schema, designed for scalability and performance

  • Best for:

    • Unstructured/semi-structured data (JSON, logs, IoT data)

    • High-volume real-time apps (social media, sensor data, chat apps)

    • Use cases where scalability > consistency

  • Pros: Schema flexibility, horizontal scaling, optimized for specific workloads

  • Cons: Weaker ACID guarantees (eventual consistency in many systems), limited query complexity compared to SQL

3. NewSQL Databases

  • Examples: Google Spanner, CockroachDB, VoltDB

  • Model: Relational like SQL, but designed for scalability of NoSQL

  • Best for:

    • Systems needing both ACID guarantees and massive scalability

    • Global, distributed applications (fintech, e-commerce at scale)

    • Hybrid workloads: transactions + analytics

  • Pros: Combines relational structure with high scalability, distributed architecture

  • Cons: Less mature, fewer vendors, more complex setup

4. Choosing in the Data Development Cycle

When selecting a DBMS, ask:

  1. Data Structure:

    • Structured, relational → SQL

    • Semi/unstructured → NoSQL

    • Hybrid, distributed → NewSQL

  2. Workload Type:

    • OLTP (transactions, banking, HR) → SQL / NewSQL

    • Big data, IoT, real-time → NoSQL

    • Global-scale distributed transactions → NewSQL

  3. Scalability Needs:

    • Small/medium scale → SQL

    • Massive scale (billions of records, high throughput) → NoSQL/NewSQL

  4. Consistency vs Availability:

    • Strong consistency needed → SQL / NewSQL

    • High availability & partition tolerance (CAP theorem) → NoSQL

  5. Query Complexity:

    • Complex joins, analytics → SQL

    • Simple lookups, key-value, document queries → NoSQL

Example Decisions

  • Banking system: SQL (PostgreSQL, Oracle) → needs strong ACID transactions.

  • Social media platform: NoSQL (Cassandra, MongoDB) → massive scalability, flexible schema.

  • Global e-commerce (Amazon-scale): NewSQL (Google Spanner, CockroachDB) → distributed, consistent, and scalable.

Summary

  • SQL → Structured, consistent, transactional.

  • NoSQL → Scalable, flexible, real-time big data.

  • NewSQL → Combines both: scalable + relational + ACID.