A Data Pipeline is an automated workflow that moves and transforms data from source systems to destination systems. It is the foundational infrastructure of data engineering — without pipelines, data stays trapped in operational systems and never reaches analysts, dashboards, or ML models.
How Data Pipelines Work
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┌──────────┐ ┌───────────┐ ┌──────────┐ ┌──────────────┐
│ Sources │──→ │ Extract │──→ │Transform │──→ │ Load │
│ │ │ │ │ │ │ │
│ Databases│ │ APIs │ │ Clean │ │ Data │
│ APIs │ │ CDC │ │ Validate │ │ Warehouse │
│ Files │ │ Streaming │ │ Enrich │ │ Data Lake │
│ SaaS │ │ │ │ Aggregate│ │ Feature Store│
└──────────┘ └───────────┘ └──────────┘ └──────────────┘
Types of Data Pipelines
Batch Pipelines
- Process data in scheduled intervals (hourly, daily)
- Best for: Reports, analytics, historical analysis
- Tools: Airflow, dbt, Spark, AWS Glue
Streaming Pipelines
- Process data in real-time as it arrives
- Best for: Fraud detection, real-time dashboards, alerts
- Tools: Kafka, Flink, Spark Streaming, Kinesis
Hybrid Pipelines (Lambda/Kappa)
- Combine batch and streaming for different latency needs
- Lambda: Separate batch + speed layers
- Kappa: Single streaming layer serves all needs
Pipeline Patterns
ETL (Extract, Transform, Load)
Transform data before loading into the warehouse:
`
Source → Transform (staging) → Load (warehouse)
Best when you need data quality guarantees before loading.ELT (Extract, Load, Transform)
Load raw data first, transform inside the warehouse:
Source → Load (warehouse raw) → Transform (warehouse clean)
Best when your warehouse has powerful compute (Snowflake, BigQuery).Reverse ETL
Move data from the warehouse back to operational tools:
Warehouse → CRM, Marketing tools, Product databases
Key Infrastructure Components
| Component | Purpose | Tools |
|-----------|---------|-------|
| Ingestion | Extract data from sources | Fivetran, Airbyte, Kafka Connect |
| Orchestration | Schedule and monitor pipelines | Airflow, Dagster, Prefect |
| Transformation | Clean and model data | dbt, Spark, Dataform |
| Storage | Store processed data | Snowflake, BigQuery, S3 + Iceberg |
| Quality | Validate data integrity | Great Expectations, dbt tests, Soda |
| Observability | Monitor pipeline health | Monte Carlo, Datadog, Atlan |
Best Practices
1. Idempotency: Running a pipeline twice produces the same result
2. Incremental Processing: Only process new/changed data
3. Schema Evolution: Handle schema changes gracefully
4. Testing: Unit test transforms, integration test end-to-end
5. Alerting: Get notified on failures, delays, and data quality issues
6. Documentation: Document data lineage and business logic