SQL vs NoSQL

One-liner: SQL databases store data in structured tables with strict schemas; NoSQL databases trade strict consistency for flexibility and horizontal scalability.

📌 SQL (Relational Databases)

Core Properties

• Structured — Data in tables with rows and columns
• Schema — Fixed structure, must be defined upfront
• ACID — Atomicity, Consistency, Isolation, Durability
• Relations — Foreign keys, JOINs across tables
• Query Language — SQL (standardized)

Examples

When to Use SQL

• ✅ Complex queries with JOINs
• ✅ Strong consistency required (banking, payments)
• ✅ Data is relational by nature (users → orders → products)
• ✅ ACID transactions needed
• ✅ Data structure is well-known and unlikely to change

📌 NoSQL (Non-Relational Databases)

Core Properties

• Flexible schema — Add/remove fields without migrations
• Horizontally scalable — Designed to scale out
• Eventual consistency — Usually (some offer strong consistency)
• Optimized for specific access patterns

Types of NoSQL

1. Document Store

// MongoDB, CouchDB, Firestore
{
  "_id": "user_123",
  "name": "Rahul",
  "address": { "city": "Delhi", "pin": "110001" },
  "tags": ["premium", "verified"]
}

Best for: User profiles, product catalogs, CMS

2. Key-Value Store

Redis, DynamoDB, Memcached
"session:abc123" → { userId: 42, expiry: ... }
"counter:pageviews" → 1000000

Best for: Sessions, caching, real-time counters, leaderboards

3. Column-Family Store

Cassandra, HBase
Row key → { column1: val, column2: val, column3: val }

Best for: Time-series data, IoT, write-heavy workloads, Netflix viewing history

4. Graph Database

Neo4j, Amazon Neptune
Nodes (Person) → Edges (FOLLOWS) → Nodes (Person)

Best for: Social networks, fraud detection, recommendation engines

⚖️ The Comparison

🔄 ACID vs BASE

ACID (SQL)

BASE (NoSQL)

🏗️ Choosing Between SQL and NoSQL

Decision Tree:

Is the data relational?
├── Yes → SQL (start here)
└── No ↓
    Do you need flexible schema?
    ├── Yes → Document (MongoDB)
    └── No ↓
        Is it write-heavy time-series?
        ├── Yes → Column (Cassandra)
        └── No ↓
            Is it graph-traversal heavy?
            ├── Yes → Graph (Neo4j)
            └── No → Key-Value (Redis/DynamoDB)

🌍 Real-World Usage Patterns

Most large systems use both — polyglot persistence!

🔑 Key Takeaways

• Default to SQL for new projects — better tooling, stronger guarantees
• Move to NoSQL when you hit specific scale/flexibility walls
• Most production systems use both (polyglot persistence)
• NoSQL doesn't mean "no schema" — you still have implicit structure