Alex Monahan

Data lakehouses are open, scalable, and interoperable, but most are not ready for agents. We’ll show you how to query Iceberg and DuckLake using both DuckDB and MotherDuck with the agent of your choice.

Structured Query Language (or SQL for short) is a programming language to manage data in a database system and an essential part of any data engineer’s tool kit. In this tutorial, you will learn how to use SQL to create databases, tables, insert data into them and extract, filter, join data or make calculations using queries.

Discover how to cut complexity of your dbt data pipelines with serverless DuckDB while improving performance and drastically reducing costs. This session covers practical strategies for cutting complexity and expenses in data flows while enjoying a more ergonomic and frictionless workflow.

Structured Query Language (or SQL for short) is a programming language to manage data in a database system and an essential part of any data engineer’s tool kit. In this tutorial, you will learn how to use SQL to create databases, tables, insert data into them and extract, filter, join data or make calculations using queries.

A Data Warehouse (DW) is a powerful tool to manage your scientific data, training data, logs, or any other type of relational data. Most Data Warehouses are cloud-based and built to scale to petabyte workflows, but might not be optimal for smaller workloads that need a fast iteration cycle.

Data management systems and data scientists have a troubled relationship: Common systems such as Postgres or Spark are hard to set up and maintain, hard to transfer data into and out of, and hard to integrate into Python workflows.

DuckDB is a novel analytical data management system. DuckDB supports complex queries, has no external dependencies, and is deeply integrated into the Python ecosystem.