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 #5 with an average rating of 8.4, while Cloudera is ranked #19 with an average rating of 8.0. Amazon Web Services (AWS) holds a 4.2% mindshare in SA, compared to Cloudera’s 2.0% 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

3,106 Views
3,106 Comparison Views

90% willing to recommend

Cloudera DataFlow

Read 5 Cloudera DataFlow reviews

1,288 Views
1,198 Comparison Views

80% willing to recommend

Amazon Kinesis

Cloudera DataFlow

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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

5th

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

19th

Average Rating

7.4

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of June 2026, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 4.2%, down from 7.9% compared to the previous year. The mindshare of Cloudera DataFlow is 2.0%, 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	4.2%
Cloudera DataFlow	2.0%
Other	93.8%

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.

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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…

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Quotes from Members

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

Pros

"With AWS, you don't have to invest in any kind of infrastructure."

"Great auto-scaling, auto-sharing, and auto-correction features."

"The scalability is pretty good."

"From my experience, one of the most valuable features is the ability to track silent events on endpoints. Previously, these events might have gone unnoticed, but now we can access them within the product range. For example, if a customer reports that their calls are not reaching the portal files, we can use this feature to troubleshoot and optimize the system."

"I find almost all features valuable, especially the timing and fast pace movement."

"The scalability is pretty good, as one can have any number of nodes spawned or replicated on the primary so that any load can be handled, perhaps a few terabytes with ease in around 15 seconds."

"Setting Amazon Kinesis up is quick and easy; it only takes a few minutes to configure the necessary settings and start using it."

"The most valuable feature is that it has a pretty robust way of capturing things."

More Amazon Kinesis pros

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

"This solution is very scalable and robust."

"DataFlow's performance is okay."

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

"The initial setup was not so difficult"

Cons

"AI processing or cleaning up data would be nice since I don't think it is a feature in Amazon Kinesis right now."

"We are contemplating moving away from Amazon Kinesis primarily because of the cost."

"The price is not much cheaper. So, there is room for improvement in the pricing."

"One area for improvement in the solution is the file size limitation of 10 Mb. My company works with files with a larger file size. The batch size and throughput also need improvement in Amazon Kinesis."

"Something else to mention is that we use Kinesis with Lambda a lot and the fact that you can only connect one Stream to one Lambda, I find is a limiting factor. I would definitely recommend to remove that constraint."

"Amazon Kinesis involved a more complex setup and configuration than Azure Event Hub."

"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."

"The default limit that they have, which at the moment is 5,000 records per second seems too low."

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

"The solution's pricing is fair."

"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."

"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."

"I rate the product price a five on a scale of one to ten, where one is cheap, and ten is expensive."

"The product falls on a bit of an expensive side."

"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."

"In general, cloud services are very convenient to use, even if we have to pay a bit more, as we know what we are paying for and can focus on other tasks."

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

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

12%

Manufacturing Company

Construction Company

Financial Services Firm

18%

Construction Company

14%

Manufacturing Company

10%

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

June 2026

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Buyer's Guide

Streaming Analytics

June 2026

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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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