Google Cloud Dataflow OverviewUNIXBusinessApplication

Google Cloud Dataflow is the #11 ranked solution in Streaming Analytics tools. PeerSpot users give Google Cloud Dataflow an average rating of 7.0 out of 10. Google Cloud Dataflow is most commonly compared to Apache NiFi: Google Cloud Dataflow vs Apache NiFi. Google Cloud Dataflow is popular among the large enterprise segment, accounting for 70% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a comms service provider, accounting for 15% of all views.
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What is Google Cloud Dataflow?
Google 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 was previously known as Google Dataflow.

Google Cloud Dataflow Customers
Absolutdata, Backflip Studios, Bluecore, Claritics, Crystalloids, Energyworx, GenieConnect, Leanplum, Nomanini, Redbus, Streak, TabTale
Google Cloud Dataflow Video

Google Cloud Dataflow Pricing Advice

What users are saying about Google Cloud Dataflow pricing:
"The price of the solution depends on many factors, such as how they pay for tools in the company and its size."

Google Cloud Dataflow Reviews

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Jose Pineda - PeerSpot reviewer
Head of Data and Analytics at a tech services company with 201-500 employees
Real User
Top 10
Easy to use for programmers, user-friendly, and scalable
Pros and Cons
  • "The most valuable features of Google Cloud Dataflow are the integration, it's very simple if you have the complete stack, which we are using. It is overall very easy to use, user-friendly friendly, and cost-effective if you know how to use it. The solution is very flexible for programmers, if you know how to do scripts or program in Python or any other language, it's extremely easy to use."
  • "Google Cloud Data Flow can improve by having full simple integration with Kafka topics. It's not that complicated, but it could improve a bit. The UI is easy to use but the experience could be better. There are other tools available that do a better job."

What is our primary use case?

we are using Google Cloud Dataflow for retailers and eCommerce.

What is most valuable?

The most valuable features of Google Cloud Dataflow are the integration, it's very simple if you have the complete stack, which we are using. It is overall very easy to use, user-friendly friendly, and cost-effective if you know how to use it. The solution is very flexible for programmers, if you know how to do scripts or program in Python or any other language, it's extremely easy to use.

What needs improvement?

Google Cloud Data Flow can improve by having full simple integration with Kafka topics. It's not that complicated, but it could improve a bit. The UI is easy to use but the experience could be better. There are other tools available that do a better job.

For how long have I used the solution?

I have been using Google Cloud Dataflow for approximately one year.

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What do I think about the stability of the solution?

Google Cloud Dataflow has been around for a while and it is stable.

What do I think about the scalability of the solution?

Google Cloud Dataflow is scalable because it is on the cloud. If we were hosting it we might have some troubles.

We have approximately five people in my organization using the solution. We plan to increase usage but not the engineers that use it.

How are customer service and support?

The technical support is very hard to reach.

How was the initial setup?

The initial setup of Google Cloud Dataflow is simple. The deployment tool is approximately 10 minutes.

I rate the complexity of the initial setup a one out of five.

What about the implementation team?

We did the implementation of Google Cloud Data Flow ourselves. We have five people in our engineering team and one of us that is free does the maintenance of the solution when required. Whoever builds the ETLs and the flow has to build the observability part and monitoring.

What was our ROI?

We're a startup and we've only recently been building the data architecture and it is too early to tell.

What's my experience with pricing, setup cost, and licensing?

The price of the solution depends on many factors, such as how they pay for tools in the company and its size.

I rate the price of Google Cloud Data Flow a two out of five.

Which other solutions did I evaluate?

We evaluated other options before choosing Google Cloud Dataflow, such as Confluent. We chose Google Cloud Dataflow because we went towards an all-GCP cloud initiative in the companies. It was more of a position as the stack we were going to use rather than having multiple different components of other third-party companies. 

There are other cool solutions, such as Databricks, but those are for different things. Google Cloud Data Flow is mostly for transformation in the cloud, in streams. There are other nice solutions out there on the market but choosing Google Cloud Data Flow made sense because we have everything integrated into GCP.

What other advice do I have?

I rate Google Cloud Dataflow an eight out of ten.

Which deployment model are you using for this solution?

Public Cloud
Disclosure: I am a real user, and this review is based on my own experience and opinions.
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PeerSpot user
Darasimi Ajewole - PeerSpot reviewer
Software Engineer at Formplus
Real User
Top 5
Helps to run batch-specific jobs, but notifications for error messages could be more detailed
Pros and Cons
  • "The service is relatively cheap compared to other batch-processing engines."
  • "The deployment time could also be reduced."

What is our primary use case?

Our primary use case for the solution is running batch jobs. It is mainly used for running computations on large batches of data. So in a case where you have big data, you need to know the analytics on the data, process the data, and present it. Google Cloud Dataflow gives you the scale and processing engine to run expensive computations on your data, quite similar to big data processing engines.

How has it helped my organization?

Migrating our batch processing jobs to Google Cloud Dataflow led to a reduction in cost by 70%.

What is most valuable?

The service is built on an open-source framework - Apache Beam, which has a lot of documentation. Additionally, the service is relatively cheap compared to other batch-processing engines, and the SDKs are well-supported. Hence, these are three significant features that are valuable.

What needs improvement?

Currently, not all error logs are available to users and this could make debugging failed jobs very difficult.  

The startup time of Dataflow jobs could also be reduced, and some features available in Java SDK can be included in the Python SDK.

For how long have I used the solution?

We have been using the solution for approximately one year.

What do I think about the stability of the solution?

The solution is stable.

What do I think about the scalability of the solution?

The solution is scalable and scales very well, regardless of how much data we process. Currently, four members of the team are using the solution.

How was the initial setup?

The initial setup assumes much background knowledge about how the Google Cloud platform works, so new developers will come to Google Cloud Dataflow and find the solution challenging. Therefore, I rate the initial setup process as medium.

What's my experience with pricing, setup cost, and licensing?

The solution is cheap compared to other similar products and emphasizes many features compared to many other streaming engines.

What other advice do I have?

I rate the solution a six out of ten. The solution is good, but notifications for error messages could be more detailed, and deployment times could be reduced.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
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PeerSpot user
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Updated: November 2022
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Download our free Streaming Analytics Report and find out what your peers are saying about Google, Apache, Amazon, and more!