Have you integrated your data engineering workflows with other platform tools, such as Airflow, Databricks, or Snowflake, and how did you manage those integrations?

During my data‑engineering career I have integrated pipelines with several platform tools. I’ve used Apache Airflow to orchestrate end‑to‑end workflows, scheduling ETL jobs that pull data from Snowflake, S3, and Kafka. In a recent project I leveraged Databricks notebooks to process large Spark jobs and then pushed the results to Snowflake via the Databricks‑Snowflake connector. For real‑time ingestion I set up Kafka Connect to stream change‑data capture events into S3, where a Lambda function triggers a Glue job for transformation. I also integrated with Snowflake’s native tasks and streams to automate data refreshes. On the monitoring side I used Grafana and Prometheus to collect metrics from the pipelines. These integrations allowed me to build a cohesive data platform that combined batch, streaming, and analytical workloads while keeping the codebase modular and maintainable.