About Apache Beam
Apache Beam is an open-source, unified model for defining both batch and streaming data-parallel processing pipelines. Using one of the open-source Beam SDKs, you can build a program that defines the pipeline. The pipeline can then be executed by one of Beam’s supported distributed processing backends, which include Apache Flink, Apache Spark, and Google Cloud Dataflow. Apache Beam is particularly useful for embarrassingly parallel data processing tasks, in which the problem can be decomposed into many smaller bundles of data that can be processed independently and in parallel. You can also use Beam for Extract, Transform, and Load (ETL) tasks and pure data integration. These tasks are useful for moving data between different storage media and data sources, transforming data into a more desirable format, or loading data onto a new system.
What is Beam Summit?
The goal of Beam Summit has been to connect a community of professionals around the world who use, contribute to, and are learning Apache Beam. The 2026 overall theme for focus is “Expanding the Data Ecosystem” which is about how Apache Beam empowers users to seamlessly integrate and process data from disparate sources, including real-time streams, batch repositories, and emerging data platforms to power applications in areas like ML.
This annual conference provides a space to share use cases, performance and resource optimizations, discuss pain points, and talk about the benefits of implementing Apache Beam in organizations. The event brought together the Apache Beam community to discuss the project’s status, its technical advances, and its future.
Some of the focus areas are:
Unified Data Processing with ML Integration: Leveraging Beam’s unified model to simplify the development of both batch and stream processing pipelines, and exploring how to embed ML models directly in those pipelines for real-time insights.
Agentic Architectures: Using Beam to orchestrate agentic workflows or using agents to interact with and improve Beam pipelines.
Connecting Disparate Systems with Modern Data Lakehouses: Showcasing practical examples of integrating Beam with various data sources like databases, cloud storage, Kafka message queues, APIs, Apache Iceberg for efficient and reliable data lake management.
Real-time ML-Driven Data Insights: Exploring the use of Beam, alongside Kafka, for building low-latency, real-time data applications, such as fraud detection, anomaly detection, personalized recommendations, with real time ML inferencing.
Scalability and Performance with Optimized Storage: Addressing the challenges of scaling Beam pipelines to handle massive datasets and high-velocity streams, with a focus on how Iceberg facilitates optimized data storage and retrieval.
Ecosystem and Community with Modern Data Tools: Highlighting the vibrant Beam ecosystem, including contributions from various organizations and the active community driving its development, with focus on tools like Kafka and Iceberg.
Emerging Trends (AI/ML and Lakehouse Architectures): Discussing the future of data processing and how Beam is evolving to meet the demands of new technologies, such as advanced AI/ML integration, serverless computing, and the growing importance of data lakehouse architectures using technologies like Iceberg.