data-engineer

To build an ETL pipeline with Airflow, you design a DAG to ingest daily data from multiple sources, apply transformations, and load it into a data warehouse like Snowflake. The workflow includes implementing data quality checks and idempotent operations.

The best way to design a scalable data warehouse schema is to implement star or snowflake schemas. This approach ensures efficient querying and maintainability while focusing on data governance and cost optimization for cloud data services.

Incremental processing in Spark works by processing only new or changed data rather than the entire dataset. It implements schema-on-read/write and idempotent operations to ensure data lineage, scalability, and maintainability across pipelines.

Yes, you can use Kafka to build real-time streaming architectures. It integrates with data lineage and monitoring systems to ensure data is processed efficiently and reliably, maintaining scalability and data quality across streaming pipelines.

Choosing schema-on-read or schema-on-write depends on your processing needs. Schema-on-read offers flexibility for raw data ingestion, while schema-on-write enforces data quality early, both optimizing cost and governance for cloud data services.

An Airflow DAG may fail during data transformation if idempotent operations are not implemented correctly or data quality checks fail. Ensuring proper schema-on-read/write and monitoring for data lineage helps maintain pipeline reliability.