PyData Amsterdam
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Talks & recordings
5 connected sessions
The QueryGraph Stack
Alexy Khrabrov's PyData Amsterdam 2026 lightning talk on the QueryGraph stack, an open-source layer built on Sail, LakeSail's Rust implementation of Spark: pip install pysail, Python UDFs running from Rust without crossing the JVM boundary, and a partner ecosystem of Rust and Python startups.
Christophe Blefari, nao Labs — Interview with Alexy
Alexy Khrabrov talks with Christophe Blefari, co-founder of nao Labs, after his PyData Amsterdam 2026 keynote on the history of analytics from the warehouse to the lakehouse and today's agentic systems, including a live demo of talking to data in DuckDB. They discuss what a semantic layer should be, with unambiguous, human-readable definitions of metrics and dimensions rather than a pile of SQL queries; a two-layer approach where an agent falls back from the strict semantic layer to broader context; and nao, an open-source analytics agent that lets everyone in a company chat with its data while data people act as context engineers, with bring-your-own model and database.
Matt Topol, Columnar, on ADBC — Interview with Alexy
Alexy Khrabrov talks with Matt Topol, co-founder of Columnar and PMC member of Apache Arrow, Apache Iceberg, and the new Apache Magpie, about ADBC, the Arrow-native replacement for ODBC and JDBC that keeps data columnar end to end. They discuss dbc, Columnar's package manager for signed ADBC driver binaries, the ADBC community extension for DuckDB, Spark Connect and Sail returning Arrow natively, dbt building its adapters on ADBC, why agent protocols need a binary channel rather than JSON, and how Apache Magpie's skills help open-source maintainers use AI responsibly.
Modernizing Spark: Performance Boost without Rewrite
Shehab Amin of LakeSail and Santosh Pingale of Adyen on accelerating Spark workloads via Spark Connect, Arrow, and Rust without rewriting code, presented at PyData Amsterdam 2026.
Ritchie Vink, Polars — Interview with Alexy
Alexy Khrabrov talks with Ritchie Vink, founder of Polars, at PyData Amsterdam 2026, Ritchie's home game. They discuss why Polars was written in Rust six years ago and why Rust's compile-time guarantees now make it a strong language for AI-assisted coding; how database research, with lazy evaluation, a query optimizer, a consistent relational data model, and strict column types, shaped Polars in contrast to pandas; Polars as a Python-first library that catches type errors before a query runs; a growing focus on SQL for agents; and the goal of being the fastest engine at any scale, including distributed.
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