@Giorgio Yes indeed that's also a good thought to integrate. I will keep in mind to think about when I draft AIP and message about this a bit more :) Yes please join. We have great demos packed on this topic :)
@kaxil , Yes that's a great blog post from the wren AI and leveraging the Apache DataFusion as a query engine to connect to different data sources. Pavan On Tue, Sep 30, 2025 at 7:37 PM Giorgio Zoppi <[email protected]> wrote: > Hey Pavan, > Some notes: > 1. LLM can be also very useful in detecting root causes of your error while > developing and design a pipeline. I explain me better, we'd in the past > several > Spark processes, when it is all green is ok, but when on fails, it will be > nice to have a tool integrated to ask why. > 2. Ideally such operator could be a ModelContextProtocolOperator and you > would not need nothing else that put an LLM as parameter with that > operator, > and just call for tools, execute query, and so on. This would be more > powerful, because you create an abstraction between devices, databases, > server and so on, so each source of data can be injected on the pipeline. > 3. Good job! Looking forward to see the presentation. > Best Regards, > Giorgio > > Il giorno mar 30 set 2025 alle ore 14:51 Pavankumar Gopidesu < > [email protected]> ha scritto: > > > Hi everyone, > > > > We're exploring adding LLM-powered SQL operators to Airflow and would > love > > community input before writing an AIP. > > > > The idea: Let users write natural language prompts like "find customers > > with missing emails" and have Airflow generate safe SQL queries with full > > context about your database schema, connections, and data sensitivity. > > > > Why this matters: > > > > > > Most of us spend too much time on schema drift detection and manual data > > quality checks. Meanwhile, AI agents are getting powerful but lack > > production-ready data integrations. Airflow could bridge this gap. > > > > Here's what we're dealing with at Tavant: > > > > > > Our team works with multiple data domain teams producing data in > different > > formats and storage across S3, PostgreSQL, Iceberg, and Aurora. When data > > assets become available for consumption, we need: > > > > - Detection of breaking schema changes between systems > > > > - Data quality assessments between snapshots > > > > - Validation that assets meet mandatory metadata requirements > > > > - Lookup validation against existing data (comparing file feeds with > > different formats to existing data in Iceberg/Aurora) > > > > This is exactly the type of work that LLMs could automate while > > maintaining governance. > > > > What we're thinking: > > > > ```python > > > > # Instead of writing complex SQL by hand... > > > > quality_check = LLMSQLQueryOperator( > > > > task_id="find_data_issues", > > > > prompt="Find customers with invalid email formats and missing phone > > numbers", > > > > data_sources=[customer_asset], # Airflow knows the schema > > automatically > > > > # Built-in safety: won't generate DROP/DELETE statements > > > > ) > > > > ``` > > > > The operator would: > > > > - Auto-inject database schema, sample data, and connection details > > > > - Generate safe SQL (blocks dangerous operations) > > > > - Work across PostgreSQL, Snowflake, BigQuery with dialect awareness > > > > - Support schema drift detection between systems > > > > - Handle multi-cloud data via Apache DataFusion[1] (Did some experiments > > with 50M+ records and results are in 10-15 seconds for common > > aggregations) > > > > for more info on benchmarks [2] > > > > Key benefit: Assets become smarter with structured metadata (schema, > > sensitivity, format) instead of just throwing everything in `extra`. > > > > Implementation plan: > > > > Start with a separate provider (`apache-airflow-providers-sql-ai`) so we > > can iterate without touching the Airflow core. No breaking changes, works > > with existing connections and hooks. > > > > I am presenting this at Airflow Summit 2025 in Seattle with Kaxil - come > > see the live demo! > > > > Next steps: > > > > If this resonates after the Summit, we'll write a proper AIP with > technical > > details and further build a working prototype. > > > > Thoughts? Concerns? Better ideas? > > > > > > [1]: https://datafusion.apache.org/ > > > > [2]: > > > > > https://datafusion.apache.org/blog/2024/11/18/datafusion-fastest-single-node-parquet-clickbench/ > > > > Thanks, > > > > Pavan > > > > P.S. - Happy to share more technical details with anyone interested. > > > > > -- > Life is a chess game - Anonymous. >
