god-data-engineering

To design a batch and streaming data pipeline, you orchestrate workloads across Spark and Flink with Airflow, applying dbt for modeling and Delta Lake for storage to ensure scalable, production-grade data platforms.

Delta Lake schema evolution works with dbt by applying versioned schema management and automated data quality tests, ensuring that evolving table structures maintain governance and reproducible deployment in your data warehouse.

Yes, you can use Airflow to orchestrate CDC pipelines with Kafka by coordinating batch and streaming workloads, enforcing observability, and managing data quality checks across your end-to-end data engineering architecture.

The best way to ensure data quality and observability in a data lake is to implement governance checks, automated testing, and schema management using tools like Delta Lake or Iceberg within your data pipeline architecture.

Yes, you need CI/CD to manage reproducible data pipeline deployments because it enforces production-grade workflows, threat checks for anti-hallucination, and safe schema evolution strategies across your scalable data platform.

You should use Flink instead of Spark for streaming data pipelines when your architecture design requires dedicated stream processing, complementing Spark's batch capabilities within an Airflow-orchestrated data platform.