Développez l'apprentissage automatique prédictif avec Flink | Atelier du 18 déc. | S'inscrire
Apache Beam is a flexible programming SDK for building data processing pipelines that can handle batch processing, stream processing, and parallel processing in one. Its unified model allows developers to define and execute abstract data workflows to be deployed on one of any number of different data processing engines, such as Apache Flink, Apache Spark, Google Cloud Dataflow, and Kafka.
Built by the original creators of Apache Kafka, Confluent powers scalable, continuous, fault-tolerant data stream processing, real-time integration, streaming analytics, governance, and more to modernize your data infrastructure.
Apache Beam's programming model is based on data transforms, which can be optimized and combined to create efficient and scalable workflows. The benefits abstracting stream processing through Beam is unclear: the requirement to run the same stream processing job on multiple frameworks is extremely rare, so the real benefits of this abstraction are slim after considering the costs of adopting Beam as a separate framework.
Beam aims to provide a framework independent logical model for data processing and stream processing. One use case for Beam might be to specify data processing pipelines for real-time streaming analytics, but this can be done without Beam in the processing framework of choice. Beam might be chosen by an organization seeking to standardize its data processing and not require its developers to have specific expertise in a specific framework such as Spark or Flink.
A company that runs a social media platform may choose to use Beam on top of Spark, Flink, or Kafka to specify the processing of real-time data streams from various sources, such as user activity logs, clickstreams, and social media feeds. The intention behind this choice might be to allow the developers for that company to focus on their processing logic rather than platform-specific idiosyncrasies.
Apache Beam offers a unified programming model that allows developers to write batch and streaming data processing pipelines that can run on various processing engines such as Apache Flink, Apache Spark, and Google Cloud Dataflow. It’s easy to deploy Apache Beam pipelines with Confluent Cloud as discussed in this talk, which discusses using Confluent Cloud as a source:
Apache Beam is a unified programming model and SDK for building batch and streaming data processing pipelines. It provides a set of APIs that can be used to build data pipelines in a variety of programming languages, including Java, Python, and Go. Beam involves the following components:
The Apache Beam project hosts a fantastic tutorial and execution environments for getting started with Beam quickly and testing different aspects of data flows.
Apache Beam aims to provide a unified model for batch and streaming data processing. This means that you can use the same Beam code to process data that is either coming in as a stream or that has already been collected into a batch. This can save you time and effort, as you don't need to learn two different sets of APIs.
Apache Beam supports a variety of execution engines, including Apache Spark, Google Cloud Dataflow, and Apache Flink. This offers you the flexibility to choose the execution engine that best meets your needs.
Theoretically, Apache Beam code can be run on any execution engine without modification. This means that you can develop your code once and then run it on any platform that supports Apache Beam. This can save you time and money, as you don't need to develop and maintain separate versions of your code for different platforms.
Apache Beam can scale to process large amounts of data. This is because Apache Beam uses a distributed architecture that can be scaled out to multiple machines. This can help you to process data more quickly and efficiently.
Apache Beam is extensible with a variety of plugins and libraries. This means that you can add new features and functionality to Apache Beam to meet your specific needs. For example, you can add support for new data sources or new data processing operations.
Confluent democratizes stream processing by operating Confluent Cloud, a fully managed, multi-cloud data streaming platform with 120+ pre-built integrations, including Apache Beam.
However, Apache Beam is neither necessary nor required for effective stream processing with Confluent. Rather than requiring developers to adhere to a Beam-specific API model, Confluent users can specify their stream processing jobs in SQL, a much simpler, more standard, and more universally adopted language.
While Beam jobs can run with Confluent, more sophisticated stream processing can also be accomplished by directly choosing Kafka Streams, Apache Flink, or Apache Spark.