SQL vs NoSQL

SQL databases excel at structured data with complex relationships.

SQL Strengths

1. ACID Transactions
   - Financial data
   - Inventory management
   - Order processing

2. Complex Queries
   - Multi-table JOINs
   - Aggregations
   - Window functions
   - Subqueries

3. Data Integrity
   - Constraints (UNIQUE, FOREIGN KEY)
   - Schema enforcement
   - Data validation

4. Mature Ecosystem
   - Tooling
   - Backup/restore
   - Monitoring
   - Community support

SQL Use Cases

Use Case	Why SQL
Banking	ACID for transactions
E-commerce	Complex product relationships
User management	Structured user data
Content management	Structured content
Analytics	Complex queries
Inventory	Data integrity

SQL Decision Criteria

Choose SQL when:
✓ Data is structured and tabular
✓ Relationships are important
✓ ACID transactions required
✓ Complex queries needed
✓ Schema is stable
✓ Data integrity critical
✓ Team knows SQL

Examples:
- Banking system
- E-commerce platform
- HR system
- Patient records

SQL Anti-Patterns

Avoid SQL when:
✗ Schema changes frequently
✗ Data is unstructured/semi-structured
✗ Write throughput very high
✗ Need extreme horizontal scaling
✗ Simple key-value access pattern

SQL Scaling Considerations

SQL Scaling Path:

1. Vertical scaling (first)
   - Upgrade instance
   - Add RAM, CPU

2. Read replicas
   - Distribute reads
   - Async replication

3. Connection pooling
   - Reuse connections
   - Reduce overhead

4. Sharding (last resort)
   - Complex but scalable
   - Distributed transactions

NoSQL databases optimize for specific workloads and data models.

NoSQL Strengths

1. Horizontal Scaling
   - Add nodes linearly
   - No vertical limits

2. Flexible Schema
   - Dynamic fields
   - Schema-less or schema-on-read

3. High Throughput
   - Optimized for specific access patterns
   - Distributed architecture

4. High Availability
   - No single point of failure
   - Multi-datacenter replication

NoSQL Use Cases

Use Case	NoSQL Type
Session storage	Key-value (Redis)
User profiles	Document (MongoDB)
Time-series data	Column-family (Cassandra)
Social network	Graph (Neo4j)
Caching	Key-value (Redis)
Product catalog	Document (MongoDB)
IoT data	Column-family (Cassandra)

NoSQL Decision Criteria

Choose NoSQL when:
✓ Data is semi-structured or unstructured
✓ Schema changes frequently
✓ High write throughput needed
✓ Horizontal scaling required
✓ Simple access patterns
✓ Eventual consistency acceptable
✓ Need specific data model

Examples:
- Social media feed
- IoT sensor data
- Real-time analytics
- Gaming leaderboards

NoSQL Tradeoffs

Tradeoffs:
- No complex JOINs
- Weaker consistency (eventual)
- Limited query flexibility
- Data duplication (denormalization)
- Newer technology (less mature tooling)

NoSQL Scaling Path

NoSQL Scaling:

1. Horizontal scaling (built-in)
   - Add nodes
   - Auto-rebalance

2. Replication
   - Multiple copies
   - High availability

3. Partitioning
   - Data distribution
   - Linear scaling

NoSQL Anti-Patterns

Avoid NoSQL when:
✗ Data is highly relational
✗ Complex queries required
✗ Strong ACID needed
✗ Data consistency critical
✗ Team lacks NoSQL experience

Decision Framework

Step 1: Data Model
- Structured → SQL
- Semi-structured → Document
- Key-value → Key-Value
- Relationships → Graph

Step 2: Consistency
- Strong → SQL
- Eventual → NoSQL

Step 3: Scale
- Vertical → SQL
- Horizontal → NoSQL

Step 4: Query Complexity
- Complex → SQL
- Simple → NoSQL

Many systems use both SQL and NoSQL together.

Polyglot Persistence

Using multiple database types for different needs:

System: E-commerce Platform

- User data → PostgreSQL (ACID, relationships)
- Product catalog → MongoDB (flexible schema)
- Sessions → Redis (fast access)
- Search → Elasticsearch (full-text search)
- Recommendations → Neo4j (graph relationships)

Hybrid Architecture

┌─────────────────────────────────────────┐
│             Application                 │
└──────────────┬──────────────────────────┘
               │
       ┌───────┼───────┬──────────┐
       │       │       │          │
   ┌───▼──┐ ┌──▼───┐ ┌─▼──┐  ┌───▼──┐
   │PostgreSQL│MongoDB│Redis│  │Neo4j │
   │(users) │(products)│(sessions)│(social)│
   └────────┘└────────┘└────┘  └──────┘

Data Synchronization

Challenge: Keeping data consistent across databases

Solutions:
1. CDC (Change Data Capture)
   - PostgreSQL → Kafka → MongoDB

2. Dual Writes
   - Application writes to both databases

3. Event Sourcing
   - Events stored, projections to different DBs

NewSQL

NewSQL: SQL + NoSQL benefits

Examples:
- CockroachDB: Distributed SQL
- Google Spanner: Global SQL
- TiDB: MySQL-compatible distributed

Features:
- ACID transactions
- Horizontal scaling
- SQL interface
- Distributed architecture

Hybrid Best Practices

1. Choose the right tool for each job: Don't force one database
2. Manage data synchronization: Use CDC or event sourcing
3. Monitor all databases: Unified observability
4. Team expertise: Consider what your team knows
5. Start simple: Add NoSQL only when needed

Decision Checklist

For each component:
□ What is the data model?
□ What are the consistency requirements?
□ What is the scale (reads/writes)?
□ What are the query patterns?
□ What is the team's expertise?
□ What is the operational overhead?