Alex Merced

AI agents are becoming a new interface for how people discover, reason over, and act on enterprise data. But truly agentic systems require more than an LLM connected to a database.

In this talk, we’ll explore how to unlock the full potential of Iceberg for data analytics in Python. From working with PyIceberg for low-level Iceberg table operations to leveraging Apache Polaris for catalog management, we’ll cover the essential tools and libraries available for Python users.

Finally, the talk connects these ideas to the rise of agentic analytics, where both humans and AI agents rely on SQL as a shared reasoning layer over trusted enterprise data. Attendees will leave with a clear understanding of how “SQL for Everything” becomes a practical foundation for AI-ready...

Apache Iceberg promises open, high-performance data, but getting there is often complex. Teams face disruptive migrations, ongoing table maintenance, and growing pressure to support AI-driven analytics.

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.

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.

Building an effective Apache Iceberg lakehouse starts well before technology selection. It begins with internal research that clarifies business goals, data domains, access patterns, and stakeholder expectations.

The ability to integrate and unify diverse data sources is more critical than ever in the world of Agentic AI. Apache Iceberg provides a powerful foundation for building a central source of truth, ensuring reliable, scalable, and efficient data management.

As AI development shifts toward more agentic and composable workloads, the modern data lakehouse must rise to meet new demands — performance, interoperability, and intelligent access to resources. We’ll focus on three key technologies: Arrow Flight – A high-performance data transfer protocol that enables low-latency, columnar data sharing between engines, perfect for real-time AI pipelines.

Apache Iceberg is more than just a table format — it’s a rapidly evolving ecosystem with a strong foundation built on three critical pillars: the Table Specification, the Catalog Specification, and Table Services. Table Spec – Learn how Iceberg’s metadata-driven design enables powerful table features through a rich and evolving specification, including schema evolution, partitioning, and versionin

Apache Iceberg has become a cornerstone of modern data lakehouse architecture, but unlocking its full performance potential requires a thoughtful approach to table optimization. In this session, we’ll explore the essential techniques and best practices for accelerating query performance on Iceberg tables.

Alex Merced discusses the value proposition of a data lakehouse and the components of building a successful data lakehouse. We'll discuss the role of each component in delivering cost savings through increased performance and reduced storage and the options within each category.

The open data lakehouse offers those frustrated with the costs and complex pipelines of using traditional warehouses an alternative that offers performance with affordability and simpler pipelines.

Data lakehouse versioning is a critical technique for ensuring the accuracy and reliability of data in a data lakehouse. It allows you to track changes to data over time, which can be helpful for troubleshooting problems, auditing data, and reproducing experiments.

Organizations are increasingly moving towards data lakehouse architectures to combine the flexibility and scalability of data lakes with the management features of traditional data warehouses.

In today's data-driven world, organizations grapple with vast volumes of data coming from diverse sources. Managing, processing, and extracting value from this data efficiently is a critical challenge.

The open data lakehouse offers those frustrated with the costs and complex pipelines of using traditional warehouses an alternative that offers performance with affordability and simpler pipelines. In this talk, we'll be talking about technologies that are making the open data lakehouse possible.

This talk provides a concise overview of the Apache Iceberg ecosystem, a pivotal component in the evolution of open lakehouse architectures. Alex Merced will delve into the key aspects: • Querying Tools: Learn about tools for querying Apache Iceberg tables, enhancing data querying within the open lakehouse framework.

Data lakehouse versioning is a critical technique for ensuring the accuracy and reliability of data in a data lakehouse. It allows you to track changes to data over time, which can be helpful for troubleshooting problems, auditing data, and reproducing experiments.

Multi-table transactions have existed in data warehouses for some time, but with the open source Project Nessie, multi-table transactions and an innovative git-like experience become available to data lakehouses.

Regulatory requirements can make data engineering more complex than it otherwise would be. In this talk, we will discuss how to navigate hard deletions in an Apache Iceberg-based data lakehouse.

Data Lakes have been built with a desire to democratize data - to allow more and more people, tools, and applications to make use of data. A key capability needed to achieve it is hiding the complexity of underlying data structures and physical data storage from users.