Alex Merced is the Head of Developer Relations at Dremio, where he educates, entertains, and enlightens audiences about modern data lakehouse architecture and open-source innovation. He runs DataLakehouseHub.com, a resource hub for engineers and practitioners navigating the growing lakehouse ecosystem. Alex is a regular speaker at global conferences such as Data Council, Data Day Texas, OSA Con, Nerdearla, Øredev, Confluent's Currents, StreamNative's Data Streaming Summit, and Dremio's Subsurface. His talks often center on open table formats, metadata management, and the future of cloud-native data platforms. He is the author of three books: Apache Iceberg: The Definitive Guide (O'Reilly) Apache Polaris: The Definitive Guide (O'Reilly) Architecting an Apache Iceberg Lakehouse (Manning) Beyond writing and speaking, Alex contributes to the open-source community through several projects, including: SencilloDB – a lightweight, in-process document database in JavaScript Pangolin Catalog – a Rust-based lakehouse catalog implementation dremioframe / iceframe – Python libraries for working with Dremio and Apache Iceberg He continues to advocate for open data architectures and supports the growing community of developers working at the intersection of analytics and open-source technology.
Apache Iceberg has become a foundational table format for modern lakehouse architectures, but effective adoption depends on strong developer tooling. This talk explores the growing Python ecosystem around Iceberg, focusing on three complementary tools: PyIceberg, iceframe, and iceberg-cli. We begin with PyIceberg and its role as the core Python library for interacting with Iceberg catalogs, schemas, snapshots, and metadata. We then introduce iceframe, a DataFrame-style API that enables Python developers to express Iceberg queries and transformations in a more ergonomic, analytical workflow without abandoning Iceberg's core semantics. Finally, we examine iceberg-cli, an enhanced command-line interface that goes beyond basic inspection to support operational tasks, debugging, and day-to-day Iceberg table management. Attendees will leave with a clear mental model of how these tools fit together, when to use each one, and how Python-first workflows can accelerate development, experimentation, and operations in Iceberg-based lakehouses.
