Data Pipeline Design

Data Pipeline Design

When to Use

When building or reviewing data movement between systems — ingestion, transformation, and delivery to consumers.

Core Jobs

1. Choose the Pattern

Batch (ETL/ELT):

• Data moves on schedule (hourly, daily)
• Use when: latency tolerance > 1 hour, source systems can't stream, cost matters
• Tools: Airflow, dbt, Spark, AWS Glue

Streaming:

• Data moves continuously (seconds to milliseconds)
• Use when: real-time dashboards, fraud detection, event-driven systems
• Tools: Kafka, Flink, Kinesis, Pub/Sub

Hybrid: batch for historical backfill, streaming for current data (Lambda architecture)

2. Design the Pipeline Stages

Source → Ingest → Validate → Transform → Load → Serve

For each stage define:

• What enters, what exits (schema)
• Error handling (dead-letter queue or retry)
• Latency requirement
• Volume (rows/sec at peak)

3. Handle Failures

• Idempotent transforms: re-running produces same result
• Checkpointing: resume from last successful point
• Dead-letter queues: capture failed records for inspection
• Alerting: pipeline lag > N minutes → page on-call

4. Document the Lineage

• Source → destination for each field
• Transformation logic (not just code — business intent)
• Owner per pipeline segment

Key Outputs

• Pipeline architecture diagram
• Schema definitions (source and target)
• Failure handling spec
• Lineage documentation

Anti-Patterns

• No idempotency — reruns create duplicates
• Streaming everything when batch suffices (costs 10x more)
• No data quality checks at ingestion
• Pipelines with no owner