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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:
 

ROI

Sentiment score
7.8
Amazon Kinesis offers significant cost savings, seamless integration, improved monitoring, and reduces data ingestion costs, enhancing ROI and architecture.
Sentiment score
7.2
Google Cloud Dataflow offers substantial cost savings and efficiencies, with organizations experiencing 70% time savings and clear financial benefits.
With Lambda, there is no need for data transfer charges, which is beneficial for less frequent workloads.
 

Customer Service

Sentiment score
7.1
Amazon Kinesis customer support is generally quick and effective, but experiences vary in technical guidance and response times.
Sentiment score
7.9
Google Cloud Dataflow customer support experiences vary from slow to effective, with proactive updates and dedicated managers enhancing service.
We receive prompt support from AWS solution architects or TAMs.
The fact that no interaction is needed shows their great support since I don't face issues.
Google's support team is good at resolving issues, especially with large data.
Whenever we have issues, we can consult with Google.
 

Scalability Issues

Sentiment score
7.3
Amazon Kinesis is scalable for reliable streaming, but complex processing and costs may vary with implementation and data volumes.
Sentiment score
7.3
Google Cloud Dataflow is highly rated for scalability, handling large data loads seamlessly and offering dynamic resource optimization.
Amazon Kinesis provides auto-scaling with streams that handle large volumes well.
Google Cloud Dataflow has auto-scaling capabilities, allowing me to add different machine types based on pace and requirements.
Google Cloud Dataflow can handle large data processing for real-time streaming workloads as they grow, making it a good fit for our business.
As a team lead, I'm responsible for handling five to six applications, but Google Cloud Dataflow seems to handle our use case effectively.
 

Stability Issues

Sentiment score
7.8
Amazon Kinesis is praised for stability and fault tolerance, though some users report slowdowns and capacity issues.
Sentiment score
8.2
Google Cloud Dataflow is reliable and stable, with automatic scaling and minor errors in complex, long-running tasks.
I have not encountered any issues with the performance of Dataflow, as it is stable and backed by Google services.
The job we built has not failed once over six to seven months.
The automatic scaling feature helps maintain stability.
 

Room For Improvement

Amazon Kinesis requires improvements in throughput, automation, setup complexity, data retention, machine learning features, and user-friendly interfaces.
Google Cloud Dataflow improves integrations, but faces challenges in SDK features, support, authentication, cost, and scalability.
Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes.
Outside of Google Cloud Platform, it is problematic for others to use it and may require promotion as an actual technology.
I would like to see improvements in consistency and flexibility for schema design for NoSQL data stored in wide columns.
Dealing with a huge volume of data causes failure due to array size.
 

Setup Cost

Amazon Kinesis is cost-effective compared to self-managed solutions, but prices can increase with high data usage and features.
Google Cloud Dataflow is cost-effective and competitive, with expenses aligned to usage, often cheaper than AWS.
Amazon Kinesis and Lambda pricing is competitive, but we noticed that scaling and large volumes could potentially increase costs significantly.
It is part of a package received from Google, and they are not charging us too high.
 

Valuable Features

Amazon Kinesis offers easy configuration, real-time analytics, and robust AWS integration, ideal for managing large, complex data workflows.
Google Cloud Dataflow offers seamless integration, flexibility, scalability, cost-effectiveness, and powerful event stream processing for real-time insights.
Lambda's scalability, seamless integration with other AWS services, and support for multiple programming languages are very beneficial.
It supports multiple programming languages such as Java and Python, enabling flexibility without the need to learn something new.
The integration within Google Cloud Platform is very good.
We then perform data cleansing, including deduplications, schema standardizations, and filtering of invalid records.
 

Categories and Ranking

Amazon Kinesis
Ranking in Streaming Analytics
2nd
Average Rating
8.0
Reviews Sentiment
7.1
Number of Reviews
28
Ranking in other categories
No ranking in other categories
Google Cloud Dataflow
Ranking in Streaming Analytics
6th
Average Rating
8.0
Reviews Sentiment
7.3
Number of Reviews
13
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of May 2025, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 8.3%, down from 13.6% compared to the previous year. The mindshare of Google Cloud Dataflow is 7.1%, down from 7.2% 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.
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Top Industries

By visitors reading reviews
Computer Software Company
18%
Financial Services Firm
17%
Manufacturing Company
10%
Retailer
5%
Financial Services Firm
17%
Manufacturing Company
13%
Retailer
11%
Computer Software Company
10%
 

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?
Pricing is normal. It is part of a package received from Google, and they are not charging us too high.
What needs improvement with Google Cloud Dataflow?
I am not sure, as we built only one job, and it is running on a daily basis. Everything else is managed using BigQuery schedulers and Talend. However, occasionally, dealing with a huge volume of da...
 

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: April 2025.
851,604 professionals have used our research since 2012.