data-engineering

To build an Airflow DAG that ingests Kafka events for Flink processing, define dependency management and event-driven scheduling to orchestrate the pipeline, then write results to the warehouse and enforce dbt checks. This ensures reliable scheduling and checkpointing for real-time workflows.

Ensuring exactly-once semantics in Kafka and Flink streaming pipelines requires configuring state management, checkpointing, and savepoint strategies. Proper windowing and TTL for state settings maintain data integrity during real-time stream processing and operational recovery.

The best way to implement automated data quality checks with dbt and Great Expectations is to apply schema tests and validation approaches during warehouse transformation. This enforces lineage tracking and automated validation within your ETL/ELT orchestration workflows.

Yes, you can use Dagster for partitioned incremental processing and lineage tracking in ETL orchestration. It supports dependency management, cron scheduling, and SLA monitoring to manage and validate data pipelines while ensuring timely data delivery.

Idempotency and SLA monitoring are needed for production data pipelines to prevent duplicate records and ensure timely data delivery. These operational checklist items maintain pipeline reliability alongside alerting, checkpointing, and savepoint strategies for streaming workflows.

Yes, this data engineering approach works for both real-time Kafka processing and warehouse transformations. It manages and validates data pipelines across orchestration and quality tools, ensuring exactly-once semantics for streams and dbt testing for warehouse scenarios.