Apache Beam vs Google Cloud Dataflow

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Apache Beam

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Apache Beam vs Google Cloud Dataflow: What are the differences?

<Apache Beam vs Google Cloud Dataflow>

1. **Integration with Multiple Processing Engines**: Apache Beam is a unified model that allows you to run your data processing pipelines on different processing engines such as Apache Flink, Apache Spark, and Google Cloud Dataflow. On the other hand, Google Cloud Dataflow is a fully managed service provided by Google Cloud Platform that specifically runs Apache Beam pipelines on its infrastructure, offering scalability, monitoring, and easy integration with other GCP services.

2. **Pricing Model**: Apache Beam is an open-source project and can be run on any cloud provider or on-premises without any additional cost. In contrast, Google Cloud Dataflow has a pay-as-you-go pricing model where you are charged based on the resources used and the processing power required for your pipelines, making it a more cost-effective solution for large-scale data processing projects.

3. **Managed Service Benefits**: While both Apache Beam and Google Cloud Dataflow support parallel processing, fault tolerance, and event-time processing, Google Cloud Dataflow provides additional benefits as a fully managed service such as automatic scaling, integration with other GCP services like BigQuery and Pub/Sub, and built-in monitoring and logging capabilities, reducing the operational overhead for managing the infrastructure. Apache Beam, on the other hand, requires more manual configuration and management of the underlying infrastructure.

4. **Data Source Connectivity**: Google Cloud Dataflow offers seamless integration with Google Cloud Storage, Bigtable, Datastore, and other GCP services, making it easier to ingest and process data from these sources. Apache Beam, being an open-source project, provides connectors to a wide range of data sources and sinks, including various file formats, databases, and messaging systems, making it more flexible in terms of data source connectivity.

5. **Community Support and Development**: Apache Beam has a strong community of contributors and users who actively provide support, contribute to the development of new features, and share best practices for building efficient data pipelines. Google Cloud Dataflow, while benefiting from the Apache Beam community, has dedicated support from Google Cloud Platform engineers for managing and optimizing data processing pipelines on the GCP infrastructure, ensuring timely updates and enhancements.

6. **Deployment Flexibility**: Apache Beam allows you to deploy your pipelines on different environments such as on-premises, cloud, or hybrid setups, giving you more flexibility in choosing where to run your data processing workloads. Google Cloud Dataflow, on the other hand, is specifically designed to run on the Google Cloud Platform, limiting the deployment options to GCP infrastructure but providing seamless integration with other GCP services for a more streamlined workflow.

In Summary, Apache Beam and Google Cloud Dataflow offer different advantages in terms of integration, pricing, managed services, data source connectivity, community support, and deployment flexibility for building and running data processing pipelines.

Advice on Apache Beam and Google Cloud Dataflow

I need to design a pipeline for ingesting streaming data (video, audio, and telemetry) from remote video cameras to Cloud AI/ML services. Cameras can be wired or wireless. So connection can be unstable. The video should be processed separately from each camera. Telemetry and audio can be added in the future, for now, it's only video stream. Looking for a solution for GCP. Thanks!

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Replies (1)
Pablo Estrada
Software Engineer at Google · | 2 upvotes · 1.1K views

Disclosure: I work on Beam and Dataflow.

I have seen Apache Beam and Cloud Dataflow used to develop pipelines processing data from IoT devices via PubSub. Beam also has connectors for Cloud AI services, like the Vision API[1]. If you can upload data to Cloud Storage, or stream it via PubSub, Beam has appropriate connectors for all of those.

I have no exposure to the services around Cloud IoT, but I believe they all work via PubSub, so they should integrate well with Dataflow.

Check the video in [2]: A use case that seems very similar to yours - they don't go into implementation details much, but it should give you an idea of the general architecture.

[1] https://beam.apache.org/releases/pydoc/2.25.0/apache_beam.ml.gcp.visionml.html

[2] https://www.youtube.com/watch?v=dPuE30kY6-c

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Pros of Apache Beam
Pros of Google Cloud Dataflow
  • 5
  • 5
  • 2
  • 2
    Unified batch and stream processing
  • 7
    Unified batch and stream processing
  • 5
  • 4
    Fully managed
  • 3
    Throughput Transparency

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What is Apache Beam?

It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.

What is Google Cloud Dataflow?

Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.

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What are some alternatives to Apache Beam and Google Cloud Dataflow?
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Kafka Streams
It is a client library for building applications and microservices, where the input and output data are stored in Kafka clusters. It combines the simplicity of writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's server-side cluster technology.
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
Apache Flink
Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
See all alternatives