Google Cloud Dataflow vs. Google Cloud Pub/Sub

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Google Cloud Dataflow
Score 7.9 out of 10
N/A
Google offers Cloud Dataflow, a managed streaming analytics platform for real-time data insights, fraud detection, and other purposes.N/A
Google Cloud Pub/Sub
Score 9.0 out of 10
N/A
Google offers Cloud Pub/Sub, a managed message oriented middleware supporting many-to-many asynchronous messaging between applications.N/A
Pricing
Google Cloud DataflowGoogle Cloud Pub/Sub
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Google Cloud DataflowGoogle Cloud Pub/Sub
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details——
More Pricing Information
Community Pulse
Google Cloud DataflowGoogle Cloud Pub/Sub
Considered Both Products
Google Cloud Dataflow

No answer on this topic

Google Cloud Pub/Sub
Chose Google Cloud Pub/Sub
Compute Engine is not a direct competitor, but in fact, works well coupled with Pub/Sub.
Top Pros

No answers on this topic

Top Cons

No answers on this topic

Features
Google Cloud DataflowGoogle Cloud Pub/Sub
Streaming Analytics
Comparison of Streaming Analytics features of Product A and Product B
Google Cloud Dataflow
7.5
1 Ratings
8% below category average
Google Cloud Pub/Sub
-
Ratings
Real-Time Data Analysis8.01 Ratings00 Ratings
Data Ingestion from Multiple Data Sources8.01 Ratings00 Ratings
Low Latency8.01 Ratings00 Ratings
Linear Scale-Out7.01 Ratings00 Ratings
Machine Learning Automation7.01 Ratings00 Ratings
Data Enrichment7.01 Ratings00 Ratings
Best Alternatives
Google Cloud DataflowGoogle Cloud Pub/Sub
Small Businesses
IBM Streams
IBM Streams
Score 9.0 out of 10
AWS IoT Core
AWS IoT Core
Score 7.8 out of 10
Medium-sized Companies
Confluent
Confluent
Score 7.4 out of 10
Apache Kafka
Apache Kafka
Score 8.4 out of 10
Enterprises
Spotfire Streaming
Spotfire Streaming
Score 7.9 out of 10
Apache Kafka
Apache Kafka
Score 8.4 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Google Cloud DataflowGoogle Cloud Pub/Sub
Likelihood to Recommend
8.0
(1 ratings)
9.4
(7 ratings)
Likelihood to Renew
-
(0 ratings)
10.0
(1 ratings)
Usability
-
(0 ratings)
10.0
(2 ratings)
Availability
-
(0 ratings)
10.0
(1 ratings)
Performance
-
(0 ratings)
10.0
(1 ratings)
Support Rating
-
(0 ratings)
9.8
(3 ratings)
Configurability
-
(0 ratings)
10.0
(1 ratings)
Product Scalability
-
(0 ratings)
10.0
(1 ratings)
User Testimonials
Google Cloud DataflowGoogle Cloud Pub/Sub
Likelihood to Recommend
Google
Based on my experience, streaming / real time / machine learning / AI type of processing and batch processing which needs less transformation are very well suited. Work load that needs complex transformation / multiple hops gets very complicated to implement. New feature like Dataflow SQL option will come in handy for sql heavy users.
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Google
If you want to stream high volumes of data, be it for ETL streaming or event sourcing, Google Cloud Pub/Sub is your go-to tool. It's easy to learn, easy to observe its metrics and scales with ease without additional configuration so if you have more producers of consumers, all you need to do is to deploy on k8s your solutions so that you can perform autoscaling on your pods to adjust to the data volume. The DLQ is also very transparent and easy to configure. Your code will have no logic whatsoever regarding orchestrating pubsub, you just plug and play. However, if you are not in the Google Cloud Pub/Sub environment, you might have trouble or be most likely unable to use it since I think it's a product of Google Cloud.
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Pros
Google
  • Streaming, Real time work load
  • Batch processing
  • Auto scaling
  • flexible pricing
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Google
  • With a pub/sub architecture the consumer is decoupled in time from the publisher i.e. if the consumer goes down, it can replay any events that occurred during its downtime.
  • It also allows consumer to throttle and batch incoming data providing much needed flexibility while working with multiple types of data sources
  • A simple and easy to use UI on cloud console for setup and debugging
  • It enables event-driven architectures and asynchronous parallel processing, while improving performance, reliability and scalability
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Cons
Google
  • inbuild template options can be expanded
  • more data connector options
  • easy of use
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Google
  • Would be nice if the queue could be extended beyond 7 days.
  • We found it a bit tricky replay unacknowledged messages when needed.
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Likelihood to Renew
Google
No answers on this topic
Google
It serves all of our purposes in the most transparent way I can imagine, after seeing other message queueing providers, I can only attest to its quality.
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Usability
Google
No answers on this topic
Google
It has many libraries in many languages, google provides either good guides or they're AI generated code libraries that are easy to understand. It has very good observability too.
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Reliability and Availability
Google
No answers on this topic
Google
I have never faced a single problem in 4 years.
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Performance
Google
No answers on this topic
Google
It's very fast, can be even better if you use protobuf.
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Support Rating
Google
No answers on this topic
Google
They have decent documentation, but you need to pay for support. We weren't able to answer all our questions with the documentation and didn't have time to setup support before we needed it so I can't give it a higher rating but I think it tends to be a bit slow unless you're a GCP enterprise support customer.
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Alternatives Considered
Google
Google Cloud Dataproc Cloud Datafusion
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Google
Having used Amazon Web Services SNS & SQS I can say that even if the latter may offer more features, Google Cloud Pub/Sub is easier to use. On the other hand, usage of SNS & SQS as well as documentation and troubleshooting is easier with the AWS solution. Since we are not using GCP only for Pub/Sub the choice depends on other variables.
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Scalability
Google
No answers on this topic
Google
You can just plug in consumers at will and it will respond, there's no need for further configuration or introducing new concepts. You have a queue, if it's slow, you plug in more consumers to process more messages: simple as that.
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Return on Investment
Google
  • cost saving from managing our own data center for ETL servers
  • consumption based pricing
  • with auto scaling feature, we were able to expand components to support work load
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Google
  • Increased Efficiency with reliable and Google managed services up all the time wit Disaster Recovery in place as well
  • Definitely Lower costs being a cloud based solution and easier to setup
  • Faster Project delivery and go to market plan for the business use cases basis this technology at the back end
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ScreenShots