An article on building an AI agent to interact with Apache Airflow using PydanticAI and Gemini 2.0, providing a structured and reliable method for managing DAGs through natural language queries.
- Agent interacts with Apache Airflow via the Airflow REST API.
- Agent can understand natural language queries about workflows, fetch real-time status updates, and return structured data.
- Sample DAGs are implemented for demonstration purposes.
- Agent interacts with Apache Airflow via the Airflow REST API.
- Agent can understand natural language queries about workflows, fetch real-time status updates, and return structured data.
- Sample DAGs are implemented for demonstration purposes.
A seven-week structured self-paced study guide for learning Apache Iceberg and its ecosystem, created after the author realized its increasing relevance in the data industry.
Apache Iceberg is emerging as a cornerstone for data lakes and lakehouses in the modern data stack, drawing parallels to the rise of Hadoop a decade ago. This article explores these similarities, highlighting both the opportunities and challenges that Iceberg presents for data engineering.
- standardization, governance, simplified troubleshooting, and reusability in ML application development.
- integrations with vector databases and LLM providers to support new applications -
provides tutorials on integrating
- integrations with vector databases and LLM providers to support new applications -
provides tutorials on integrating
