Amazon Kinesis vs Cloudera DataFlow comparison

Amazon Web Services (AWS) and Cloudera are both solutions in the Streaming Analytics category. Amazon Web Services (AWS) is ranked #11 with an average rating of 8.3, while Cloudera is ranked #20 with an average rating of 8.0. Amazon Web Services (AWS) holds a 4.0% mindshare in SA, compared to Cloudera’s 2.8% mindshare. Additionally, 90% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 80% of Cloudera users who would recommend it.

Amazon Kinesis

Read 29 Amazon Kinesis reviews

3,300 Views
3,300 Comparison Views

90% willing to recommend

Cloudera DataFlow

Read 5 Cloudera DataFlow reviews

2,015 Views
1,914 Comparison Views

80% willing to recommend

Amazon Kinesis

Cloudera DataFlow

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 17, 2024

Cloudera DataFlow and Amazon Kinesis compete in the data processing and streaming sector, each showing distinct strengths. Cloudera DataFlow has an edge in scalability and open-source integration, while Amazon Kinesis is superior with its seamless AWS integration.

Features: Cloudera DataFlow excels in data flow management, edge processing, and flexible deployment options. Amazon Kinesis stands out with robust data streaming, real-time processing and can effortlessly handle massive data ingestion.

Room for Improvement: Cloudera DataFlow could enhance compatibility with non-Cloudera environments and simplify its configuration processes. It might also improve documentation for better ease of use. Amazon Kinesis could benefit from expanded data source integration, improve its analytics capabilities outside AWS, and reduce reliance on AWS’s service ecosystem for users not utilizing other AWS tools.

Ease of Deployment and Customer Service: Amazon Kinesis is simple to deploy, especially for AWS users, supported by comprehensive support and documentation. Cloudera DataFlow offers flexible deployment but may need more configuration effort, especially when integrating with non-native environments.

Pricing and ROI: Cloudera DataFlow involves higher initial setup costs yet yields steady ROI due to effective hybrid cloud integration. Amazon Kinesis is usually more cost-effective initially, thanks to AWS's flexible pricing, and offers better ROI in AWS-focused environments.

To learn more, read our detailed Amazon Kinesis vs. Cloudera DataFlow Report (Updated: June 2026).

Buyer's Guide

Amazon Kinesis vs. Cloudera DataFlow

June 2026

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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

11th

Average Rating

8.0

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Cloudera DataFlow

Ranking in Streaming Analytics

20th

Average Rating

7.4

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of August 2026, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 4.0%, down from 7.6% compared to the previous year. The mindshare of Cloudera DataFlow is 2.8%, up from 1.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Amazon Kinesis	4.0%
Cloudera DataFlow	2.8%
Other	93.2%

Streaming Analytics

Featured Reviews

reviewer1480695

Director of Software Development at a tech vendor with 10,001+ employees

Has enabled real-time processing of critical event streams with seamless cloud integration

We are contemplating moving away from Amazon Kinesis primarily because of the cost. It is very useful, but if we write our own analytics and data processing pipeline, it would be much cheaper for us. The cost is a primary hindrance. That's why we are not using it widely. For our critical pipeline we are using it, but after that we are putting it in an S3 bucket. Other pipelines directly put the events in an S3 bucket and then process from there. There is no lack of functions in Amazon Kinesis. Functionality-wise, we feel it's complete. The cost aspect is what we are really concerned about.

Read full review

Mohamed-Saied

Senior Data Architect at Teradata Corporation

Efficient data integration and workflow scheduling elevate project performance

Cloudera DataFlow is used as an ETL or ELT solution within Cloudera's data pipeline. Our organization heavily relies on it for data ingestion, transformation, and warehousing. It is also used daily for operational tasks, and it integrates well within Cloudera's ecosystem for high performance and…

Read full review

Quotes from Members

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

Pros

"The major advantage with Amazon Kinesis is the availability, additionally the reliability is awesome, the replay is incredibly fast, and the ingesting, buffering, and processing of data are really fast."

"Amazon Kinesis also provides us with plenty of flexibility."

"The integration between Amazon Kinesis and Lambda helps us significantly."

"Amazon Kinesis is easy to get started with, provides good documentation, and has a multilang daemon interface that makes it programming-language agnostic."

"The solution works well in rather sizable environments."

"I like the ease of use and how we can quickly get the configurations done, making it pretty straightforward and stable."

"We have been able to drop our costs for ingesting data by about 60 to 70%."

"Amazon Kinesis's main purpose is to provide near real-time data streaming at a consistent 2Mbps rate, which is really impressive."

More Amazon Kinesis pros

"Cloudera DataFlow is fully compatible with Cloudera's ecosystem and offers high efficiency through native connectors for various ecosystems."

"This solution is very scalable and robust."

"The most effective features are data management and analytics."

"The initial setup was not so difficult"

"DataFlow's performance is okay."

Cons

"I suggest integrating additional features, such as incorporating Amazon Pinpoint or Amazon Connect as bundled offerings, rather than deploying them as separate services."

"Kinesis can be expensive, especially when dealing with large volumes of data."

"The solution has a two-minute maximum time delay for live streaming, which could be reduced."

"Amazon Kinesis has a less meaningful and easy use than Azure Event Hub."

"One thing that would be nice would be a policy for increasing the number of Kinesis streams because that's the one thing that's constant. You can change it in real time, but somebody has to change it, or you have to set some kind of meter. So, auto-scaling of adding and removing streams would be nice."

"The services which are described in the documentation could use some visual presentation because for someone who is new to the solution the documentation is not easy to follow or beginner friendly and can leave a person feeling helpless."

"For me, especially with video streams, there's sometimes a kind of delay when the data has to be pumped to other services. This delay could be improved in Kinesis, or especially the Kinesis Video Streams, which is being used for different use cases for Amazon Connect. With that improvement, a lot of other use cases of Amazon Connect integrating with third-party analytic tools would be easier."

"There are some kind of hard limits on Amazon Kinesis, and if you hit that, then you will get the throughput exceed error."

More Amazon Kinesis cons

"Cloudera DataFlow's UI interface could be enhanced significantly. Memory handling can also be improved to be better than it is today."

"It's an outdated legacy product that doesn't meet the needs of modern data analysts and scientists."

"Although their workflow is pretty neat, it still requires a lot of transformation coding; especially when it comes to Python and other demanding programming languages."

"It is not easy to use the R language. Though I don't know if it's possible, I believe it is possible, but it is not the best language for machine learning."

Pricing and Cost Advice

"Amazon Kinesis pricing is sometimes reasonable and sometimes could be better, depending on the planning, so it's a five out of ten for me."

"The pricing depends on the use cases and the level of usage. If you wanted to use Kinesis for different use cases, there's definitely a cheaper base cost involved. However, it's not entirely cheap, as different use cases might require different levels of Kinesis usage."

"It was actually a fairly high volume we were spending. We were spending about 150 a month."

"Under $1,000 per month."

"The solution's pricing is fair."

"The tool's entry price is cheap. However, pricing increases with data volume."

"The tool's pricing is cheap."

"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."

More Amazon Kinesis pricing and cost advice

"DataFlow isn't expensive, but its value for money isn't great."

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Computer Software Company

10%

Manufacturing Company

Construction Company

Financial Services Firm

15%

Construction Company

13%

Manufacturing Company

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	8
Midsize Enterprise	10
Large Enterprise	10

No data available

Questions from the Community

What is your experience regarding pricing and costs for Amazon Kinesis?

Amazon Kinesis and Lambda pricing is competitive, but we noticed that scaling and large volumes could potentially increase costs significantly.

See all answers

What needs improvement with Amazon Kinesis?

See all answers

What is your primary use case for Amazon Kinesis?

We use Amazon Kinesis for stream processing. We get events from on-premise devices to the cloud. We get many device events and we have to process these events that are coming from the devices. To p...

See all answers

What needs improvement with Cloudera DataFlow?

Cloudera DataFlow's UI interface could be enhanced significantly. Memory handling can also be improved to be better than it is today.

See all answers

What is your primary use case for Cloudera DataFlow?

See all answers

What advice do you have for others considering Cloudera DataFlow?

Cloudera DataFlow is fully compatible with Cloudera's ecosystem and offers high efficiency through native connectors for various ecosystems. However, the learning curve is high, and there is a shor...

See all answers

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Product Reports

Buyer's Guide

Amazon Kinesis

August 2026

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Buyer's Guide

Streaming Analytics

July 2026

Download Cloudera DataFlow product report

Also Known As

Amazon AWS Kinesis, AWS Kinesis, Kinesis

CDF, Hortonworks DataFlow, HDF

Overview

Amazon Kinesis provides real-time data streaming with seamless AWS integration, ideal for analytics, data transformation, and external customer feeds. It offers cost-effective data management with high throughput and low latency, supporting multiple programming languages.

Amazon Kinesis enables organizations to manage real-time data streams efficiently. Its integration with AWS ensures seamless setup and operation, while features like auto-scaling and fault tolerance make it reliable for diverse data sources such as IoT devices and server logs. The platform's ability to handle large-scale event-driven systems and dynamic workloads makes it suitable for complex streaming architectures. Despite some challenges with costs and setup complexity, Kinesis remains a popular choice for its efficient data management and processing capabilities.

What are the key features of Amazon Kinesis?

Real-time Data Streaming: Enables live data processing and analytics.
Seamless AWS Integration: Ensures smooth operation with AWS services.
High Throughput: Consistent 2Mbps rate supports large volumes of data.
Auto-scaling: Automatically adjusts to data load.
Fault Tolerance: Maintains system reliability and data integrity.
Lambda Function Processing: Processes data efficiently using Lambda.

What benefits or ROI should users expect?

Scalable Data Management: Handles data influx effortlessly.
Efficiency: Streamlines data flows without impacting performance.
Cost-effectiveness: Delivers value for budget-conscious users.
Low Latency: Provides real-time insights and reactions.
Multi-language Support: Accommodates various development needs.

In industries such as IoT, finance, and entertainment, Amazon Kinesis facilitates the real-time ingestion and processing of data streams. It connects seamlessly to data lakes and warehouses, enabling businesses to harness data-driven insights without performance loss. This capability is essential for managing dynamic workloads and large-scale event systems. By supporting tools like KDS, Firehose, and Video Streams, Kinesis empowers organizations to respond quickly to changing data environments, enhancing operational effectiveness across different sectors.

Amazon Web Services (AWS)

Cloudera DataFlow is a scalable data integration platform offering high performance through native connections with Cloudera ecosystems like Hive, Impala, and Spark, facilitating robust data management and analytics.

Cloudera DataFlow excels in delivering comprehensive data analysis with end-to-end workflow scheduling and stands out for its high throughput and effective integration capabilities. However, users note areas needing improvement, such as transformation coding complexity, limited language support, and memory handling. While it plays an essential ETL or ELT role in Cloudera's data pipeline, providing seamless data ingestion, transformation, and warehousing, the platform's restriction to its environment and the setup's complexity remain points of user concern.

What are the key features of Cloudera DataFlow?

Scalability: Offers robust performance across various data workloads.
Native Connectivity: Seamless integration with Cloudera ecosystems like Hive and Spark for high efficiency.
Workflow Scheduling: Supports comprehensive end-to-end scheduling capabilities.
Data Management: High throughput and effective data integration capabilities.

What benefits should users expect?

High Performance: Strong throughput and efficient workload processing.
Seamless Integration: Smooth operations within Cloudera's ecosystem.
Comprehensive Analysis: Supports advanced analytics without extensive coding.

Industries use Cloudera DataFlow for applications like sentiment analysis, fraud detection, and product royalty analysis. It is widely deployed for stream analytics and module development in telecommunications, functioning as a critical tool for data ingestion and transformation, ensuring efficient operational tasks.

Cloudera

Sample Customers

Zillow, Netflix, Sonos

Clearsense

Buyer's Guide

Amazon Kinesis vs. Cloudera DataFlow

June 2026

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