Apache Beam vs Google Cloud Dataflow

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Apache Beam

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Google Cloud Dataflow

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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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Pablo Estrada
Software Engineer at Google · | 2 upvotes · 352 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
    Open-source
  • 5
    Cross-platform
  • 2
    Portable
  • 2
    Unified batch and stream processing
  • 1
    Unified batch and stream processing
  • 1
    Autoscaling
  • 1
    Fully managed

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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
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
Airflow
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.
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