The concept behind this is that a business user could ask natural language questions, such as “What are our current best-selling products in Belgium?” and the system would automatically translate that into a Flink SQL query, deploy it, and retrieve the results. This is exactly what Airy is proposing. It transforms English into the query language, making data exploration highly accessible for non-technical users. By eliminating the need for SQL expertise, this tool empowers users to interact with data dynamically and efficiently, leveraging the underlying power of Flink for real-time analytics.

You can see below the high level architecture of this very interesting copilot.

The current challenges?

• Hallucinations: LLMs sometimes generate incorrect or fabricated responses (hallucinations), which can lead to faulty business insights if the model misinterprets a query or produces invalid SQL. Ensuring accuracy through tighter model integration with datasets is mandatory to minimize these errors.
• Privacy: Handling sensitive business data, such as customer or financial information, raises privacy concerns. Strict data governance, including anonymization and role-based access control, is essential to prevent the exposure of confidential information through LLM-generated queries.
• Security: Allowing LLMs to autonomously generate queries introduces security risks, such as query injection or unintended system vulnerabilities. Validating and securing queries before execution can mitigate these risks, along with monitoring for any unauthorized or harmful actions.

Personal note: When asked about managing data access policies from the Copilot, the response was along the lines of, “That responsibility should lie with the underlying data platform, not us.” I respectfully disagree with this perspective