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Google Cloud Dataflow

A fully-managed cloud service and programming model for batch and streaming big data processing.
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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.
Google Cloud Dataflow is a tool in the Real-time Data Processing category of a tech stack.

Who uses Google Cloud Dataflow?

Companies
54 companies reportedly use Google Cloud Dataflow in their tech stacks, including Spotify, The New York Times, and PLAID.

Developers
87 developers on StackShare have stated that they use Google Cloud Dataflow.

Google Cloud Dataflow Integrations

Google AI Platform, Google AutoML Tables, Google Cloud Healthcare API, Aviatrix, and Cloud AI Platform Pipelines are some of the popular tools that integrate with Google Cloud Dataflow. Here's a list of all 5 tools that integrate with Google Cloud Dataflow.
Public Decisions about Google Cloud Dataflow

Here are some stack decisions, common use cases and reviews by companies and developers who chose Google Cloud Dataflow in their tech stack.

What are the best options to host a Spring Boot application that acts as a receiver and publisher from Google Cloud Pub/Sub. I am using Google App Engine to do that, but there is Google Cloud Dataflow and Google Cloud Run that can be used. Which is the best option that can be used for this purpose and also that can handle the failover scenarios as well. Thanks!

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Google Cloud Dataflow's Features

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

Google Cloud Dataflow Alternatives & Comparisons

What are some alternatives to 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
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

Google Cloud Dataflow's Followers
235 developers follow Google Cloud Dataflow to keep up with related blogs and decisions.