data-pipelines

Airflow orchestrates ETL data pipelines by structuring DAGs and task dependencies, while dbt handles ELT transformations, incremental loading, and data quality checks to ensure robust pipeline resilience.

Implement data quality checks in an ELT workflow by applying robust error handling strategies and validation patterns during the transformation phase, ensuring data integrity and pipeline resilience throughout the loading process.

Choose ETL over ELT when your data pipeline requires transforming data before loading it into the target system, whereas modern ELT loads raw data first and uses tools like dbt to transform it directly within the data warehouse.

Yes, this guidance covers batch, streaming, and hybrid processing requirements for data pipelines, providing patterns and best practices to design workflows that address diverse data ingestion and transformation needs.

Set up incremental loading strategies in a data pipeline by applying patterns that ingest only new or modified records from the source system, reducing processing overhead and maintaining data quality checks during daily warehouse loads.