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Google Cloud Dataflow vs Kafka: What are the differences?

Google Cloud Dataflow: A fully-managed cloud service and programming model for batch and streaming big data processing. 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; Kafka: Distributed, fault tolerant, high throughput pub-sub messaging system. Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

Google Cloud Dataflow belongs to "Real-time Data Processing" category of the tech stack, while Kafka can be primarily classified under "Message Queue".

Some of the features offered by Google Cloud Dataflow are:

  • Fully managed
  • Combines batch and streaming with a single API
  • High performance with automatic workload rebalancing Open source SDK

On the other hand, Kafka provides the following key features:

  • Written at LinkedIn in Scala
  • Used by LinkedIn to offload processing of all page and other views
  • Defaults to using persistence, uses OS disk cache for hot data (has higher throughput then any of the above having persistence enabled)

Kafka is an open source tool with 12.7K GitHub stars and 6.81K GitHub forks. Here's a link to Kafka's open source repository on GitHub.

According to the StackShare community, Kafka has a broader approval, being mentioned in 509 company stacks & 470 developers stacks; compared to Google Cloud Dataflow, which is listed in 32 company stacks and 8 developer stacks.

- No public GitHub repository available -

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.

What is Kafka?

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
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      What are some alternatives to Google Cloud Dataflow and Kafka?
      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.
      Hadoop
      The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
      Beam
      A distributed knowledge graph store. Knowledge graphs are suitable for modeling data that is highly interconnected by many types of relationships, like encyclopedic information about the world.
      Apache Beam
      It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.
      Amazon Kinesis
      Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.
      See all alternatives
      Decisions about Google Cloud Dataflow and Kafka
      Roman Bulgakov
      Roman Bulgakov
      Senior Back-End Developer, Software Architect at Chemondis GmbH | 3 upvotes 10.5K views
      Kafka
      Kafka

      I use Kafka because it has almost infinite scaleability in terms of processing events (could be scaled to process hundreds of thousands of events), great monitoring (all sorts of metrics are exposed via JMX).

      Downsides of using Kafka are: - you have to deal with Zookeeper - you have to implement advanced routing yourself (compared to RabbitMQ it has no advanced routing)

      See more
      Kafka
      Kafka
      RabbitMQ
      RabbitMQ

      The question for which Message Queue to use mentioned "availability, distributed, scalability, and monitoring". I don't think that this excludes many options already. I does not sound like you would take advantage of Kafka's strengths (replayability, based on an even sourcing architecture). You could pick one of the AMQP options.

      I would recommend the RabbitMQ message broker, which not only implements the AMQP standard 0.9.1 (it can support 1.x or other protocols as well) but has also several very useful extensions built in. It ticks the boxes you mentioned and on top you will get a very flexible system, that allows you to build the architecture, pick the options and trade-offs that suite your case best.

      For more information about RabbitMQ, please have a look at the linked markdown I assembled. The second half explains many configuration options. It also contains links to managed hosting and to libraries (though it is missing Python's - which should be Puka, I assume).

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      Fr茅d茅ric MARAND
      Fr茅d茅ric MARAND
      Core Developer at OSInet | 2 upvotes 121.5K views
      atOSInetOSInet
      Beanstalkd
      Beanstalkd
      RabbitMQ
      RabbitMQ
      Kafka
      Kafka

      I used Kafka originally because it was mandated as part of the top-level IT requirements at a Fortune 500 client. What I found was that it was orders of magnitude more complex ...and powerful than my daily Beanstalkd , and far more flexible, resilient, and manageable than RabbitMQ.

      So for any case where utmost flexibility and resilience are part of the deal, I would use Kafka again. But due to the complexities involved, for any time where this level of scalability is not required, I would probably just use Beanstalkd for its simplicity.

      I tend to find RabbitMQ to be in an uncomfortable middle place between these two extremities.

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      Interest over time
      Reviews of Google Cloud Dataflow and Kafka
      No reviews found
      How developers use Google Cloud Dataflow and Kafka
      Avatar of Pinterest
      Pinterest uses KafkaKafka

      http://media.tumblr.com/d319bd2624d20c8a81f77127d3c878d0/tumblr_inline_nanyv6GCKl1s1gqll.png

      Front-end messages are logged to Kafka by our API and application servers. We have batch processing (on the middle-left) and real-time processing (on the middle-right) pipelines to process the experiment data. For batch processing, after daily raw log get to s3, we start our nightly experiment workflow to figure out experiment users groups and experiment metrics. We use our in-house workflow management system Pinball to manage the dependencies of all these MapReduce jobs.

      Avatar of Coolfront Technologies
      Coolfront Technologies uses KafkaKafka

      Building out real-time streaming server to present data insights to Coolfront Mobile customers and internal sales and marketing teams.

      Avatar of ShareThis
      ShareThis uses KafkaKafka

      We are using Kafka as a message queue to process our widget logs.

      Avatar of Christopher Davison
      Christopher Davison uses KafkaKafka

      Used for communications and triggering jobs across ETL systems

      Avatar of theskyinflames
      theskyinflames uses KafkaKafka

      Used as a integration middleware by messaging interchanging.

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