As the demand for data products grows, data engineering teams face mounting pressure to deliver more and even faster, often becoming bottlenecks. Astro IDE changes the game. Astro IDE is an AI-powered code editor built for Apache Airflow. It helps data teams go from idea to production in minutes—generating production-ready DAGs, enabling in-browser testing, and integrating directly with Git. In this session, see how Astro IDE accelerates DAG creation, debugging, and deployment so data engineering teams can deliver more, 10x faster.

As the demand for data products grows, data engineering teams face mounting pressure to deliver more and even faster, often becoming bottlenecks. Astro IDE changes the game. Astro IDE is an AI-powered code editor built for Apache Airflow. It helps data teams go from idea to production in minutes—generating production-ready DAGs, enabling in-browser testing, and integrating directly with Git. In this session, see how Astro IDE accelerates DAG creation, debugging, and deployment so data engineering teams can deliver more, 10x faster.

In the rapidly evolving field of data engineering and data science, efficiency and ease of use are crucial. Our innovative solution offers a user-friendly interface to manage and schedule custom PySpark, PySQL, Python, and SQL code, streamlining the process from development to production. Using Airflow at the backend, this tool eliminates the complexities of infrastructure management, version control, CI/CD processes, and workflow orchestration.The intuitive UI allows users to upload code, configure job parameters, and set schedules effortlessly, without the need for additional scripting or coding. Additionally, users have the flexibility to bring their own custom artifactory solution and run their code. In summary, our solution significantly enhances the orchestration and scheduling of custom code, breaking down traditional barriers and empowering organizations to maximize their data’s potential and drive innovation efficiently. Whether you are an individual data scientist or part of a large data engineering team, this tool provides the resources needed to streamline your workflow and achieve your goals faster than ever before.

Vayu is a conversational copilot for Apache Airflow, developed at Prevalent AI to help data engineers manage, troubleshoot, and fix pipelines using natural language. Deployments often fail silently due to misconfigurations, missing connections, or runtime issues impossible to identify in unit tests. Vayu tackles these via a troubleshooting agent that inspects logs, metrics, configs, and runtime state to find root causes and suggest fixes saving engineers significant troubleshooting time. It can also apply approved fixes to DAG code and commit them to your version control system. Key Capabilities: Troubleshooting Agent: Inspects logs, configs, variables, and connections to find root causes and suggest fixes. Pipeline Mechanic Agent: Suggests code-level fixes e.g., missing connections or bad imports and, once approved, commits them to version control. DAG Manager Agent: Understands DAG logic, suggests improvements, and can trigger DAGs conversationally. Architecture: Built with open-source tools including Google ADK as the orchestration layer and a custom Airflow MCP server based on the FastMCP framework. LLMs never access Airflow directly. The full codebase will be open-sourced.