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.
