Moving Beyond Lambda: The Unified Apache Beam Model for Simplified Data Processing

At the heart of modern data architectures, the Lambda architecture has been a widely used approach for handling both batch and real-time data streams. However, as organizations seek more efficient and scalable solutions to keep up with exploding data volumes, the limitations of Lambda architectures become evident. David Regalado, Engineering VP at a stealth-mode startup and Google Cloud Champion Innovator, recently discussed how Apache Beam’s unified model offers a way forward, enabling data engineers to simplify complex workflows while reducing several operational challenges. You can watch the complete talk here. The article below is a summary, and the video is excerpted from the complete talk.

The Pitfalls of Lambda Architecture

Lambda architecture is often a go-to solution for combining batch and stream processing. Its structure allows data to be captured and fed into both batch and streaming systems in parallel, with transformation logic applied twice — once in the batch layer and once in the speed layer — before results are combined at query time.

This approach, however, can introduce several complexities as engineers attempt to scale their data management capabilities:

1. Duplicated Data and Logic: Lambda architectures require that data is processed twice, once in each layer, leading to duplicate logic and the potential for inconsistencies.
2. Dual Execution Paths: Maintaining two separate execution paths for batch and streaming processes can be costly and complex, especially when managing multiple frameworks.
3. Code Divergence: As batch and streaming systems evolve independently, code developed for one can diverge from the other, creating new challenges and requiring constant monitoring.
4. Extensive Skill Requirements: Managing Lambda architecture requires knowledge across multiple frameworks, from Hadoop and Spark to Flink and Storm, making it more difficult to build and maintain a team with the necessary expertise.

As Regalado puts it, “Lambda architecture feels like a constant game of whack-a-mole,” with engineers caught up in the chase to manage different technologies rather than focusing on core business goals. 

“Lambda architecture feels like a constant game of whack-a-mole.”
— David Regalado, Engineering VP

Regalado argues that the answer lies in a more unified approach with Apache Beam.

Apache Beam: A Unifying Solution for Data Processing

Apache Beam is designed to address these problems through a unified programming model that allows developers to write data processing pipelines that handle both batch and streaming data. By abstracting away the complexities of managing separate systems, Beam provides major benefits, including:

1. Unified Batch and Streaming: Apache Beam simplifies development by allowing a single model to handle batch and streaming data.
2. Portability: Apache Beam is platform-agnostic, meaning pipelines can run on any supported engines, such as Apache Spark, Flink, or Google Cloud Dataflow, freeing developers from worrying about the underlying infrastructure.
3. Flexible SDK Support: Beam offers SDKs in multiple programming languages, including Python, Java, Go, and SQL, allowing teams to work in languages that best suit their needs.

Regalado describes Apache Beam’s unified model as “a Theory of Everything for data processing” that revolutionized modern data engineering.

“Just as physics frameworks help us understand the universe, Beam unifies the way we process batch and streaming data.”
— David Regalado, Engineering VP

The Evolution of Apache Beam

Apache Beam’s journey begins with Google’s MapReduce, a foundational model for distributed data processing. First introduced in a 2004 research paper, MapReduce outlined a general framework for processing large datasets across multiple machines. By abstracting low-level foundational functions, such as moving data between nodes and ensuring fault tolerance, MapReduce transformed how organizations processed data at scale.

Following the MapReduce paper’s publication, the open source community, and in particular developers at Yahoo!, responded with Hadoop, a framework that implemented MapReduce’s principles for distributed processing. This adaptation inspired a wave of big data innovations, leading to tools like Apache Spark, which expanded upon MapReduce with advanced capabilities for in-memory processing and real-time computation.

However, as data needs scaled, the limitations of MapReduce for handling both batch and streaming data become clear. Google responded internally by developing Flume, and later Google Dataflow, which combined ideas from MapReduce with newer abstractions, emphasizing real-time processing. Dataflow introduced a programming model designed to handle batch and streaming data through a single API, laying the groundwork for what would become Apache Beam.

In 2016, Google donated the Dataflow model to the Apache Software Foundation, and it was rebranded as Apache Beam. Graduating as a top-level Apache project that year, Apache Beam introduced a new programming paradigm that unified batch and streaming data processing. It provided a high-level abstraction layer, allowing developers to write flexible data pipelines that could run on various backends such as Apache Spark, Flink, and Google Cloud Dataflow.

Apache Beam’s programming model answers four key questions about data processing:

1. What results to calculate,
2. When in event time to process data,
3. When in processing time to emit results, and
4. How to handle refinements of output.

By addressing these questions, Beam enabled teams to shift from a dual-layer Lambda architecture to a unified pipeline approach, handling both bounded (batch) and unbounded (streaming) datasets seamlessly.

“Just like MapReduce changed distributed processing, Beam transforms the way we unify batch and streaming.”
— David Regalado, Engineering VP.

Key Features of Apache Beam

Apache Beam has many standout features that make it clear why companies are adopting it as a core part of their data processing stack:

1. Simplified Data Processing: Apache Beam abstracts away the complexities of data infrastructure, so developers can focus on business logic without worrying about whether the data is batch or stream-based.
2. Flexible Timing Controls for Real-Time Data: Apache Beam separates the concept of event time, the time an event occurred, from processing time, when the system processes the event. This distinction allows teams to handle real-time data streams that might include delays, improving accuracy and timelines.
3. Multi-Language Pipelines: Apache Beam supports multiple programming languages, which makes it ideal for distributed teams that use different languages, allowing each team to work together seamlessly on the same project.
4. Built on Open-Source Principles: Apache Beam’s open source nature allows the community to contribute new features and resolve issues directly, keeping it aligned with developers’ evolving needs.

“Beam’s flexibility means you can run your pipeline on any supported engine, without worrying about the infrastructure.”
— David Regalado, Engineering VP

Real-World Use Cases for Apache Beam

Several major companies have already incorporated Apache Beam into their data processing workflows:

1. LinkedIn: LinkedIn handles trillions of events daily, and has integrated Apache Beam into its data pipeline to cut down processing time and costs.
2. Financial Institutions: Apache Beam’s unified approach to batch and stream processing has been particularly useful for large banks and other financial firms that need to process data in real time while maintaining efficiency.

“The need for diverse skill sets reduces dramatically with Apache Beam. You don’t have to worry about switching frameworks; you can focus on delivering value to the business.” 
— David Regalado, Engineering VP

Apache Beam: A Path Forward

The demand for unified data processing models is only set to increase in the future. Apache Beam’s unified framework simplifies workflows, enabling developers to create efficient and adaptable data pipelines. For organizations dealing with the complexities of managing multiple systems, Regalado believes Apache Beam is the answer: “With Apache Beam, engineers get their time back. No more whack-a-mole game. You can focus on solving business problems, not infrastructure.”

For teams currently grappling with Lambda architecture’s challenges, Apache Beam offers a cohesive, scalable, and future-proof solution that can unify batch and streaming data processing into a single model. By adopting Beam, companies can not only reduce operational overhead but also build a robust foundation for data innovation.