FIELD REPORT · DATA
Data Pipeline Architecture: ETL/ELT Best Practices
Build robust data pipelines. Apache Airflow, dbt, and cloud-native solutions.
- PUBLISHED
- September 18, 2025
- UPDATED
- May 15, 2026
- READ TIME
- 1 MIN
- AUTHOR
- ONE FREQUENCY
KEY FACTS
- Topic
- data, etl, pipeline
- Published
- September 18, 2025
- Last updated
- May 15, 2026
- Read time
- 1 min
- Word count
- 73
Modern data pipelines power analytics and ML. Build robust, scalable solutions.
Architecture Patterns
- Batch processing
- Stream processing
- Lambda architecture
- Kappa architecture
Technology Stack
- Orchestration: Airflow/Prefect
- Processing: Spark/Beam
- Transformation: dbt
- Storage: Data lakes/warehouses
Best Practices
- Idempotent operations
- Data quality checks
- Schema evolution
- Monitoring and alerting
Performance
- Partitioning strategies
- Incremental processing
- Caching layers
- Cost optimization
SOURCES
Cited and consulted.
- 01Martin Fowler — Data Mesh Principlesmartinfowler.com
- 02DAMA DMBOK — Data management body of knowledgedmbok.org
- 03Apache Airflow Documentationairflow.apache.org
- 04dbt Labs Documentationdbt-labs.com
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