Pipeline Design

Learn to build data pipelines that don't break at 3 AM and won't make you hate your life.

Philosophy: Simple, Reliable, Observable

Good pipeline design is about: - Simplicity: Easy to understand and modify - Reliability: Handles failures gracefully - Observability: You know what's happening

Pipeline Patterns

Pattern 1: Batch ETL

The classic approach - extract, transform, load.

When to use: - Daily/weekly reporting - Historical data processing - Complex transformations

Tools: Airflow, dbt, Spark

Pattern 2: Streaming ETL

Real-time data processing for immediate insights.

When to use: - Real-time dashboards - Fraud detection - Live recommendations

Tools: Kafka, Flink, Kinesis

Pattern 3: Lambda Architecture

Combine batch and stream processing.

When to use: - Need both real-time and historical views - Complex analytics requirements - High-volume data

Pattern 4: Kappa Architecture

Stream-first approach with reprocessing capability.

When to use: - Primarily real-time requirements - Simpler than Lambda - Event-driven architecture

Design Principles

1. Idempotency

Your pipeline should produce the same result if run multiple times.

# Bad: Appends data every run
INSERT INTO daily_sales VALUES (...)

# Good: Upsert pattern
INSERT INTO daily_sales VALUES (...) 
ON CONFLICT (date) DO UPDATE SET ...

2. Error Handling

Plan for failures because they will happen.

Dead letter queues
Retry strategies
Circuit breakers
Graceful degradation

3. Monitoring & Alerting

Know when things break before your users do.

Data freshness checks
Volume anomaly detection
Schema evolution monitoring
SLA tracking

4. Scalability

Design for tomorrow's data volume, not today's.

Partitioning strategies
Horizontal scaling
Resource optimization
Cost management

Hands-On Projects

Project 1: E-commerce Analytics Pipeline

Build a complete pipeline processing: - User events from web/mobile - Transaction data from databases - Product catalog updates - Real-time recommendations

Project 2: IoT Data Processing

Handle sensor data at scale: - Time-series data ingestion - Real-time anomaly detection - Historical trend analysis - Predictive maintenance alerts

Process social media data: - API data ingestion - Sentiment analysis pipeline - Trend detection - Influence scoring

Every pattern and principle here comes from real production systems. Learn from successes and failures in the field.