Skip to content

Database Fundamentals

Master the databases that power modern data systems - from traditional SQL to cutting-edge time-series databases.

The Database Landscape

Choosing the right database is crucial. Here's a practical guide to when to use what:

🗃️ Relational Databases (SQL)

Best for: Transactions, complex queries, data integrity - PostgreSQL: The Swiss Army knife of databases - MySQL: Fast and reliable for web applications - SQLite: Perfect for development and small applications

📊 Analytical Databases

Best for: Analytics, reporting, data warehousing - ClickHouse: Blazing fast for time-series and analytics - BigQuery: Serverless analytics at scale - Snowflake: Cloud data warehouse with separation of compute/storage

🔄 NoSQL Databases

Best for: Flexible schemas, horizontal scaling - MongoDB: Document database for JSON-like data - Cassandra: Distributed database for high availability - DynamoDB: Serverless NoSQL on AWS

⚡ In-Memory Databases

Best for: Caching, real-time applications - Redis: The go-to cache and pub/sub system - Memcached: Simple distributed caching

📈 Time-Series Databases

Best for: IoT data, monitoring, financial data - InfluxDB: Purpose-built for time-series data - TimescaleDB: PostgreSQL extension for time-series

Database Design Principles

Normalization vs Denormalization

Understanding when to normalize and when to denormalize:

Normalize when: - Data integrity is critical - Storage cost is high - Write-heavy workloads

Denormalize when: - Query performance is critical - Read-heavy workloads - Analytics use cases

Indexing Strategies

Indexes are your friend (but choose wisely):

-- Primary indexes for unique identification
CREATE UNIQUE INDEX idx_user_email ON users(email);

-- Composite indexes for multi-column queries
CREATE INDEX idx_order_date_status ON orders(order_date, status);

-- Partial indexes for filtered queries
CREATE INDEX idx_active_users ON users(created_at) 
WHERE status = 'active';

Partitioning

Split large tables for better performance:

  • Horizontal partitioning: Split by rows (e.g., by date)
  • Vertical partitioning: Split by columns
  • Functional partitioning: Split by feature/service

Hands-On Tutorials

Tutorial 1: PostgreSQL Deep Dive

  • Advanced SQL techniques
  • Query optimization
  • Index strategies
  • JSON support in PostgreSQL

Tutorial 2: Building a Data Warehouse

  • Star vs snowflake schemas
  • Slowly changing dimensions
  • ETL vs ELT patterns
  • Performance optimization

Tutorial 3: NoSQL with MongoDB

  • Document modeling
  • Aggregation pipelines
  • Sharding strategies
  • Replica sets

Tutorial 4: Time-Series with InfluxDB

  • Schema design for time-series
  • Retention policies
  • Continuous queries
  • Grafana integration

Performance Optimization

Query Optimization

  • Explain plans and execution strategies
  • Common anti-patterns to avoid
  • Index usage optimization
  • Query rewriting techniques

Hardware Considerations

  • CPU vs I/O bound workloads
  • Memory sizing for databases
  • Storage types and their impact
  • Network considerations

Database fundamentals never go out of style. Master these concepts and you'll be valuable in any data role.