Google Cloud Dataflow vs Google Cloud Dataproc

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Google Cloud Dataflow

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Google Cloud Dataproc

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Google Cloud Dataflow vs Google Cloud Dataproc: What are the differences?

What is 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.

What is Google Cloud Dataproc? A managed Spark and Hadoop service hosted on Google Cloud Platform. It is a managed Spark and Hadoop service that lets you take advantage of open source data tools for batch processing, querying, streaming, and machine learning. It helps you create clusters quickly, manage them easily, and save money by turning clusters off when you don't need them.

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

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, Google Cloud Dataproc provides the following key features:

  • Spin up an autoscaling cluster in 90 seconds on custom machines
  • Build fully managed Apache Spark, Apache Hadoop, Presto, and other OSS clusters
  • Only pay for the resources you use and lower the total cost of ownership of OSS

According to the StackShare community, Google Cloud Dataflow has a broader approval, being mentioned in 58 company stacks & 100 developers stacks; compared to Google Cloud Dataproc, which is listed in 5 company stacks and 6 developer stacks.

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Pros of Google Cloud Dataflow
Pros of Google Cloud Dataproc
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    Fully managed
  • 3
    Unified batch and stream processing
  • 3
    Autoscaling
  • 1
    Throughput Transparency
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    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 Google Cloud Dataproc?

    It is a managed Spark and Hadoop service that lets you take advantage of open source data tools for batch processing, querying, streaming, and machine learning. It helps you create clusters quickly, manage them easily, and save money by turning clusters off when you don't need them.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use Google Cloud Dataflow?
    What companies use Google Cloud Dataproc?
    See which teams inside your own company are using Google Cloud Dataflow or Google Cloud Dataproc.
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    What tools integrate with Google Cloud Dataflow?
    What tools integrate with Google Cloud Dataproc?

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    What are some alternatives to Google Cloud Dataflow and Google Cloud Dataproc?
    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
    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
    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.
    Akutan
    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.
    See all alternatives