David Anderson

Have questions about stream processing, stateful architecture, or the future of Apache Flink®? Join an interactive panel of core Flink Committers for an Ask Me Anything (AMA) session.

This session provides a comprehensive introduction to Apache Flink for developers and architects who seek to build streaming solutions that are resilient, efficient, and maintainable. I will move through three critical layers of Flink development: 1. Establish a solid foundation based on well-engineered data products You will learn best practices for: - Managing formats and schemas for the long term. - Ensuring data integrity and implementing error handling. - Handling the nuances of watermarking and late-data strategies. 2. Compose solutions from event streaming patterns I will encourage you how to decompose complex problems using reusable components based on design patterns: - Deduplication: removing duplicate events - Correlation: linking related events across streams (e.g., orders and their shipments) - Aggregation: computing real-time analytics - Enrichment: adding context to event streams - Pattern matching: detecting sequences or anomalies 3. Insist on operational excellence Finally, I ground the technical theory in operational reality, and discuss the fundamentals that will help ensure that your application scales without breaking the bank or the cluster. You will learn…

AI agents have introduced governance requirements that most data platforms were not originally designed to meet. Even well-architected lakehouses are still catching up. Humans can navigate ambiguity, file a ticket, or ask a colleague when access is unclear. Agents work with whatever is in front of them, which makes ungoverned data not an inconvenience but a production risk. This session explores how Apache Iceberg, Apache Polaris, and Apache Ossie (incubating), a new open specification for semantic interchange entering the Apache Incubator, give organizations a vendor-neutral foundation for enforcing data sovereignty without lock-in. The semantic layer has emerged as the connective tissue between raw lakehouse access and governed, business-ready data, and open catalog standards are increasingly the mechanism through which organizations keep control as data volumes, regulatory complexity, and agentic workloads all grow simultaneously. Attendees will leave with concrete patterns for enforcing sovereignty across federated environments and extending fine-grained access controls to agentic workloads.

Join this session to ask long-term Flink community members Robert, Timo and David whatever you want about Flink and the stream processing space. - How does Flink SQL allow you to query historic data? - What's the recommended way of deploying Flink to start learning it? - How do I best contribute a fix I found in production? We are happy to provide our perspective on those and more questions in the session.

This session provides a comprehensive introduction to Apache Flink for developers and architects who seek to build streaming solutions that are resilient, efficient, and maintainable. I will move through three critical layers of Flink development: 1. Establish a solid foundation based on well-engineered data products You will learn best practices for: - Managing formats and schemas for the long term. - Ensuring data integrity and implementing error handling. - Working with streams of immutable records vs. streams with updates. - Handling the nuances of watermarking and late-data strategies. 2. Compose solutions from event streaming patterns Rather than writing monolithic scripts, I will show you how to decompose complex problems using reusable components based on these design patterns: - Deduplication: removing duplicate events - Correlation: linking related events across streams (e.g., orders and their shipments) - Aggregation: computing real-time analytics - Enrichment: adding context to events from reference data - Pattern matching: detecting sequences or anomalies in event streams 3. Insist on operational excellence Finally, I ground the technical theory in operational…

Apache Flink has grown to be a large, complex piece of software that does one thing extremely well: it supports a wide range of stream processing applications with difficult-to-satisfy demands for scalability, high performance, and fault tolerance, all while managing large amounts of application state. Flink owes its success to its adherence to some well-chosen design principles. But many software developers have never worked with a framework organized this way, and struggle to adapt their application ideas to the constraints imposed by Flink's architecture. After helping thousands of developers get started with Flink, I've seen that once you learn to appreciate why Flink's APIs are organized the way they are, it becomes easier to relax and accept what its developers have intended, and to organize your applications accordingly. The key to demystifying Apache Flink is to understand how the combination of stream processing plus application state has influenced its design and APIs. A framework that cares only about batch processing would be much simpler than Flink, and the same would be true for a stream processing framework without support for state. In this talk I will explain how…

Apache Flink has grown to be a large, complex piece of software that does one thing extremely well: it supports a wide range of stream processing applications with difficult-to-satisfy demands for scalability, high performance, and fault tolerance, all while managing large amounts of application state. Flink owes its success to its adherence to some well-chosen design principles. But many software developers have never worked with a framework organized this way, and struggle to adapt their application ideas to the constraints imposed by Flink's architecture. After helping thousands of developers get started with Flink, I've seen that once you learn to appreciate why Flink's APIs are organized the way they are, it becomes easier to relax and accept what its developers have intended, and to organize your applications accordingly. The key to demystifying Apache Flink is to understand how the combination of stream processing plus application state has influenced its design and APIs. A framework that cares only about batch processing would be much simpler than Flink, and the same would be true for a stream processing framework without support for state. In this talk I will explain how…

Serverless empowers developers to build complex applications by shifting infrastructure and operational responsibility to AWS. Platform engineering plays an important role to ensure consistency and compliance and reduce the complexity of building in the cloud.

Join the conversation with Mike Gualtieri (Forrester VP & Principal Analyst), Ben Gamble (Ververica Field CTO), Gunnar Morling (Decodable Software Engineer), and David Anderson (Apache Flink Committer and Confluent Software Practice Lead) at #FlinkForward Berlin as they discuss what #AI means for the streaming data industry.

How can we help prevent technology in SMBs from becoming a cost overhead to enabling growth? Organizations need to sense and respond to change, including migrating to the cloud and serverless.

The Table API is one of the most actively developed components of Flink in recent time. Inspired by databases and SQL, it encapsulates concepts many developers are familiar with.

This talk is for everyone looking for an overall introduction to Apache Flink and the big ideas that make it special. Along the way we'll explain what use cases can be tackled, show you how to choose an API, and share the resources we've found most helpful for getting started.

Enterprises are embracing serverless to drive continuous innovation, faster time to market, and developer agility as part of broader modernization efforts. While individual teams are building prototypes, many are enabling cross-organizational support through their Cloud Center of Excellence (CCoE).