Serverless DataOps Pipelines on AWS: Implementing Airflow 3.0 DAG Versioning with Snowflake and Python for Reproducible Pipelines

In the age of fast-paced data ecosystems, the need to be responsive to the business through agile, reproducible, scalable data pipelines have compelled DataOps to emerge as one of the fundamental practices within modern data engineering. DataOps expands the DevOps notion to data operations by facilitating continuous operations, automation testing, and collective output of information operations. With businesses moving to cloud-native systems, reproducibility and inexpensive orchestration is becoming the most necessary solution. In this paper, a robust pipelined architecture based entirely on serverless architecture is analyzed and suggested as a DataOps pipeline with Apache Airflow 3.0 versioning capabilities combined with Amazon Web Services (AWS) serverless services, namely, AWS Lambda and Managed Workflows for Apache Airflow (MWAA). It uses Snowflake as the cloud-native data warehouse, and pytest and Great Expectations, which are Python-based, as its pipeline logic and data validation. Also, GitHub Actions allows using an automated CI/CD system to deploy DAG deployments, improve code quality, and guarantee version control integration. The proposed architecture shows the great improvement of operation including cycle of adaptation of faster deployment, enhanced reproducibility by tracking of DAGs, and the lesser amount of infrastructure maintenance, and data inspection of quality. An example case study of a simulated enterprise (20232024) shows that the integration made the work of deployment faster by more than 80% without any downtime due to updates and covered almost all tests. The above results highlight the significance of integrating the serverless and Airflow 3.0 potential to solve real-life DataOps problems.