Graziano Montanaro
Currently working as a Machine Learning Engineer at Intella.
Interested in end-to-end Machine Learning pipelines, MLOps, Data Engineering and deploying production-ready scalable systems.
Previous experience in the field of Computer Vision where I’ve been able to publish a co-authored paper during graduation. During my academic experience at the University of Bari I’ve worked on the ISO 25010 certified project e-GLU BOX: a platform for usability tests made for PA. The application was developed both as a web application using Laravel and as a mobile app using Flutter.
Technical skills:
- Python, Pandas, Polars, Numpy, Pyspark, Dash, Prefect
- Delta Lake, Iceberg, DuckDB, Databricks, Azure
- Pytorch, Keras, Tensorflow, Sklearn
- MLFlow, SHAP, Docker
- Flutter, Laravel
Domains of interest:
- Artificial Intelligence, Machine Learning, Data Science, Time Series Forecasting, Computer Vision, Data Engineering
Session
Delta Lake and Apache Iceberg solved data lake chaos but introduced a new one: layers of JSON metadata scattered across object storage. DuckLake is an open table format that challenges the status quo by storing all catalog metadata in a plain SQL database rather than a sprawling file system.
In this talk we will explore DuckLake's architecture, which couples the low-cost storage of S3 with the transactional guarantees of a standard SQL database. Using only Python and DuckDB — no Spark cluster required — we will demonstrate how to build a fully ACID-compliant lakehouse, complete with schema management, time travel, and concurrent writers.
We will also candidly discuss where DuckLake falls short: the single-writer bottleneck, the scale inflection point where a distributed catalog becomes necessary, and the ecosystem maturity trade-offs of adopting a newer format over established giants.
Audience: data engineers, analytics engineers, and data scientists working with lakehouse architectures or curious about lightweight alternatives to Iceberg/Delta.
Takeaways: understand the "metadata-in-database" pattern, learn to deploy a serverless multi-user lakehouse with the Python ecosystem, and gain a pragmatic framework for choosing between lightweight and enterprise-grade approaches.