What is your experience with Airflow and data grid?

I have used Airflow for over two years to orchestrate data pipelines that interact with various data grids such as Hive, Redshift, and Snowflake. In my last role, I designed DAGs that extracted data from on‑premise sources, transformed it using Spark jobs, and loaded it into a Snowflake data warehouse. I leveraged Airflow’s built‑in operators (e.g., HiveOperator, SnowflakeOperator) and created custom operators for complex logic. I also used XComs to pass metadata between tasks and implemented sensors to wait for external data availability. For monitoring, I set up Airflow’s UI dashboards and integrated alerts via Slack and PagerDuty. I configured retries, exponential backoff, and SLA checks to ensure reliability. Additionally, I used Airflow’s DAG versioning and backfilling capabilities to manage schema changes in the data grid without downtime. This experience taught me how to build scalable, maintainable pipelines that can adapt to evolving data sources and business requirements.