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

Developers
76 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.
Private Decisions at about Google Cloud Dataflow

Here are some stack decisions, common use cases and reviews by members of with Google Cloud Dataflow in their tech stack.

Shared insights
on
Google Cloud DataflowGoogle Cloud DataflowJavaJava

I use Google Cloud Dataflow because it has great templates for plug and play action.

I haven't invested in the apache beam framework because you need to know Java to take full advantage of it. The Python API is a second class citizen.

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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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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.

Nick Rockwell
Nick Rockwell
CTO at NY Times | 6 upvotes 54.5K views

We really drank the Google Kool-Aid on analytics. So, everything's going into Google BigQuery and almost everything is going straight into Google Cloud Pub/Sub and then doing some processing in Google Cloud Dataflow before ending up in BigQuery. We still do too much processing and augmentation on the front end before it goes into Pub/Sub. And that's using some kind of stuff we pulled together using Amazon DynamoDB and so on. And it's very brittle, actually. Actually, Dynamo throttling is one of our biggest headaches. So, I want all of that to go away and do all our augmentation in BigQuery after the data's been collected. And having it just go straight into Pub/Sub. So, we're working on that. And it'll happen, some time. #Analytics #AnalyticsPipeline

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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!

See more
Shared insights
on
Google Cloud DataflowGoogle Cloud DataflowJavaJava

I use Google Cloud Dataflow because it has great templates for plug and play action.

I haven't invested in the apache beam framework because you need to know Java to take full advantage of it. The Python API is a second class citizen.

See more

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
188 developers follow Google Cloud Dataflow to keep up with related blogs and decisions.
EMMANUEL PRATT
Georgios Eleftheriadis
harf rajanaa
edisplay
jvschoen
Oliver Haupt
Arunkumar Malli Sundararaman Jayaprakash
James Edwards
Vindeep V
Hugo Valenza