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Amazon Kinesis vs Google Cloud Dataflow comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 17, 2024

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Amazon Kinesis
Ranking in Streaming Analytics
2nd
Average Rating
8.0
Reviews Sentiment
7.1
Number of Reviews
27
Ranking in other categories
No ranking in other categories
Google Cloud Dataflow
Ranking in Streaming Analytics
7th
Average Rating
7.8
Reviews Sentiment
7.3
Number of Reviews
12
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of March 2025, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 9.1%, down from 14.2% compared to the previous year. The mindshare of Google Cloud Dataflow is 7.4%, up from 6.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

Rajni Kumar Jha - PeerSpot reviewer
Used for media streaming and live-streaming data
It is not compulsory to use Amazon Kinesis. If you don't want to use the data streaming, you can use just the Kinesis data firehose. Using the Kinesis data firehose is compulsory because we can't store all chats and recordings in Amazon S3 without it. When a call comes in the Amazon Kinesis instance, it will go to Data Streams if we use it. Otherwise, it will go to the Kinesis data firehose, where we need to define the S3 bucket path, and it will go to Amazon S3. So, without the Kinesis data firehose, we can't store all the chats and recordings in Amazon S3. Using Amazon Kinesis totally depends upon the user's requirements. If you want to use live streaming for the data lake or data analyst team, you need to use Amazon Kinesis. If you don't want to use it, you can directly use the Kinesis data firehose, which will be stored in Amazon S3. Overall, I rate the solution an eight out of ten.
Jana Polianskaja - PeerSpot reviewer
Build Scalable Data Pipelines with Apache Beam and Google Cloud Dataflow
As a data engineer, I find several features of Google Cloud Dataflow particularly valuable. The ability to test solutions locally using Direct Runner is crucial for development, allowing me to validate pipelines without incurring the costs of full Dataflow jobs. The unified programming model for both batch and streaming processing is exceptional - requiring only minor code adjustments to optimize for either mode. This flexibility extends to language support, with robust implementations in both Java and Python, allowing teams to leverage their existing expertise. The platform's comprehensive monitoring capabilities are another standout feature. The intuitive interface, Grafana integration, and extensive service connectivity make troubleshooting and performance tracking highly efficient. Furthermore, seamless integration with Google Cloud Composer (managed Airflow) enables sophisticated orchestration of data pipelines.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"Amazon Kinesis has improved our ROI."
"The feature that I've found most valuable is the replay. That is one of the most valuable in our business. We are business-to-business so replay was an important feature - being able to replay for 24 hours. That's an important feature."
"What turns out to be most valuable is its integration with Lambda functions because you can process the data as it comes in. As soon as data comes, you'll fire a Lambda function to process a trench of data."
"Amazon Kinesis's main purpose is to provide near real-time data streaming at a consistent 2Mbps rate, which is really impressive."
"Great auto-scaling, auto-sharing, and auto-correction features."
"I find almost all features valuable, especially the timing and fast pace movement."
"The solution's technical support is flawless."
"Everything is hosted and simple."
"The service is relatively cheap compared to other batch-processing engines."
"It allows me to test solutions locally using runners like Direct Runner without having to start a Dataflow job, which can be costly."
"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."
"The support team is good and it's easy to use."
"The integration within Google Cloud Platform is very good."
"The product's installation process is easy...The tool's maintenance part is somewhat easy."
"The most valuable features of Google Cloud Dataflow are scalability and connectivity."
"The best feature of Google Cloud Dataflow is its practical connectedness."
 

Cons

"There are certain shortcomings in the machine learning capacity offered by the product, making it an area where improvements are required."
"Amazon Kinesis involved a more complex setup and configuration than Azure Event Hub."
"There are some kind of hard limits on Amazon Kinesis, and if you hit that, then you will get the throughput exceed error."
"It would be beneficial if Amazon Kinesis provided document based support on the internet to be able to read the data from the Kinesis site."
"AI processing or cleaning up data would be nice since I don't think it is a feature in Amazon Kinesis right now."
"Lacks first in, first out queuing."
"The price is not much cheaper. So, there is room for improvement in the pricing."
"Could include features that make it easier to scale."
"There are certain challenges regarding the Google Cloud Composer which can be improved."
"The technical support has slight room for improvement."
"The authentication part of the product is an area of concern where improvements are required."
"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."
"Occasionally, dealing with a huge volume of data causes failure due to array size."
"The deployment time could also be reduced."
"When I deploy the product in local errors, a lot of errors pop up which are not always caught. The solution's error logging is bad. It can take a lot of time to debug the errors. It needs to have better logs."
"Promoting the technology more broadly would help increase its adoption."
 

Pricing and Cost Advice

"The tool's entry price is cheap. However, pricing increases with data volume."
"The fee is based on the number of hours the service is running."
"Under $1,000 per month."
"I rate the product price a five on a scale of one to ten, where one is cheap, and ten is expensive."
"Amazon Kinesis is an expensive solution."
"I think for us, with Amazon Kinesis, if we have to set up our own Kafka or cluster, it will be very time-consuming. If one considers the aforementioned aspect, Amazon Kinesis is a cheap tool."
"The product falls on a bit of an expensive side."
"It was actually a fairly high volume we were spending. We were spending about 150 a month."
"Google Cloud Dataflow is a cheap solution."
"The solution is cost-effective."
"Google Cloud is slightly cheaper than AWS."
"The solution is not very expensive."
"The tool is cheap."
"On a scale from one to ten, where one is cheap, and ten is expensive, I rate the solution's pricing a seven to eight out of ten."
"The price of the solution depends on many factors, such as how they pay for tools in the company and its size."
"On a scale from one to ten, where one is cheap, and ten is expensive, I rate Google Cloud Dataflow's pricing a four out of ten."
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Top Industries

By visitors reading reviews
Computer Software Company
19%
Financial Services Firm
17%
Manufacturing Company
9%
Retailer
5%
Financial Services Firm
17%
Manufacturing Company
12%
Retailer
11%
Computer Software Company
11%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Amazon Kinesis?
Amazon Kinesis's main purpose is to provide near real-time data streaming at a consistent 2Mbps rate, which is really impressive.
What is your experience regarding pricing and costs for Amazon Kinesis?
Amazon Kinesis is moderately priced. In comparison with other competitors, it is fairly priced, however, if they reduced the price a little, it could add more value to customers.
What needs improvement with Amazon Kinesis?
I do not see any scope for improvement as it does what it is supposed to do. No changes are required. Since it's predominantly a back-end service, any end-user isn't going to interact with it direc...
What do you like most about Google Cloud Dataflow?
The product's installation process is easy...The tool's maintenance part is somewhat easy.
What is your experience regarding pricing and costs for Google Cloud Dataflow?
Google Cloud Dataflow costs are primarily driven by compute resources (worker type and count) and data volume. However, other factors like pipeline complexity also contribute significantly to the t...
What needs improvement with Google Cloud Dataflow?
Apache Beam represents a powerful data processing solution that deserves wider recognition in the broader tech community. This technology offers remarkable capabilities for handling data at scale, ...
 

Also Known As

Amazon AWS Kinesis, AWS Kinesis, Kinesis
Google Dataflow
 

Overview

 

Sample Customers

Zillow, Netflix, Sonos
Absolutdata, Backflip Studios, Bluecore, Claritics, Crystalloids, Energyworx, GenieConnect, Leanplum, Nomanini, Redbus, Streak, TabTale
Find out what your peers are saying about Amazon Kinesis vs. Google Cloud Dataflow and other solutions. Updated: March 2025.
842,296 professionals have used our research since 2012.