Apache Spark Streaming vs Cloudera DataFlow comparison

Why PeerSpot?

Apache and Cloudera are both solutions in the Streaming Analytics category. Apache is ranked #7 with an average rating of 7.9, while Cloudera is ranked #19 with an average rating of 8.0. Apache holds a 4.6% mindshare in SA, compared to Cloudera’s 3.2% mindshare. Additionally, 94% of Apache users are willing to recommend the solution, compared to 80% of Cloudera users who would recommend it.

Apache Spark Streaming

Read 17 Apache Spark Streaming reviews

3,138 Views
3,138 Comparison Views

94% willing to recommend

Cloudera DataFlow

Read 5 Cloudera DataFlow reviews

2,512 Views
2,361 Comparison Views

80% willing to recommend

Apache Spark Streaming

Cloudera DataFlow

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 17, 2024

Cloudera DataFlow and Apache Spark Streaming compete in the real-time data processing domain. Apache Spark Streaming is preferred for its superior features and capabilities.

Features:Cloudera DataFlow offers integration, user-friendly management tools, and streamlined data routing. Apache Spark Streaming provides scalability, real-time data analytics, and efficient data processing.

Room for Improvement:Cloudera DataFlow could enhance features for machine learning and provide better performance details. Apache Spark Streaming could be easier to deploy, with a more intuitive interface and improved customer service options.

Ease of Deployment and Customer Service:Cloudera DataFlow is easy to deploy with robust customer support throughout the deployment lifecycle. Apache Spark Streaming requires more technical expertise, though it benefits from extensive community support and documentation.

Pricing and ROI:Cloudera DataFlow is economically priced, offering quicker ROI due to lower initial costs. Apache Spark Streaming has higher initial costs but justifies this with high performance and scalability, leading to substantial ROI.

To learn more, read our detailed Apache Spark Streaming vs. Cloudera DataFlow Report (Updated: September 2026).

Buyer's Guide

Apache Spark Streaming vs. Cloudera DataFlow

September 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

Apache Spark Streaming

Ranking in Streaming Analytics

7th

Average Rating

7.8

Reviews Sentiment

6.4

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 October 2026, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 4.6%, up from 3.6% compared to the previous year. The mindshare of Cloudera DataFlow is 3.2%, up from 1.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Apache Spark Streaming	4.6%
Cloudera DataFlow	3.2%
Other	92.2%

Streaming Analytics

Featured Reviews

Khoa Dang Le

Principal AI Engineer at IMT Solutions

Have faced challenges with complex data handling and seek smoother integration for machine learning workflows

I find the fault tolerance feature beneficial because I use it for serving data from a landing area. I understand all of the structures we have for Spark SQL, Spark Streaming, and MLlib. The ability of Apache Spark Streaming to handle out-of-order data using watermarking and windowing is something we use in our pipeline. Nearly 50% of our usage is based on that because we use it for landing data, and we appreciate that we can work with it. The main benefits of Apache Spark Streaming include cost savings, time savings, and efficiency improvements about data storage. The fast storage capability is crucial because Apache Spark replaces Hadoop's MapReduce, allowing us to manage our data more efficiently.

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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

"By integrating Apache Spark Streaming, the data freshness rate, and latency have significantly improved from 24-hour batch processing to less than one minute, facilitating faster communication to downstream systems, aiding marketing campaigns."

"The platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."

"I appreciate Apache Spark Streaming's micro-batching capabilities; the watermarking functionality and related features are quite good."

"As an open-source solution, using it is basically free."

"The main benefits of Apache Spark Streaming include cost savings, time savings, and efficiency improvements about data storage."

"The solution is better than average and some of the valuable features include efficiency and stability."

"The solution is very stable and reliable."

"Apache Spark Streaming's most valuable feature is near real-time analytics. The developers can build APIs easily for a code-steaming pipeline. The solutions have an ecosystem of integration with other stock services."

More Apache Spark Streaming 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 initial setup was not so difficult"

"DataFlow's performance is okay."

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

Cons

"The cost and load-related optimizations are areas where the tool lacks and needs improvement."

"The problem is we need to use it in a certain manner. After that, we need to apply another pipeline for the machine learning processes, and that's what we work on."

"The debugging aspect could use some improvement."

"While it is reliable, there are some issues with Apache Spark Streaming as it is not 100% reliable."

"We would like to have the ability to do arbitrary stateful functions in Python."

"The downside is when you have this the other way around in the columns, it becomes really hard to use."

"Monitoring is an area where they could definitely improve Apache Spark Streaming. When you have a streaming application, it generates numerous logs. After some time, the logs become meaningless because they're quite large and impossible to open."

"Integrating event-level streaming capabilities could be beneficial."

More Apache Spark Streaming cons

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

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

"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

"People pay for Apache Spark Streaming as a service."

"Spark is an affordable solution, especially considering its open-source nature."

"I was using the open-source community version, which was self-hosted."

"On a scale from one to ten, where one is expensive, or not cost-effective, and ten is cheap, I rate the price a seven."

"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%

Outsourcing Company

14%

Comms Service Provider

Marketing Services Firm

Construction Company

14%

Financial Services Firm

12%

Comms Service Provider

12%

Outsourcing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	2
Large Enterprise	7

No data available

Questions from the Community

What needs improvement with Apache Spark Streaming?

One of the improvements we need is in Spark SQL and the machine learning library. I don't think there is too much to work on, but the issue is when we want to use machine learning, we always need t...

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What is your primary use case for Apache Spark Streaming?

We work with Apache Spark Streaming for our project because we use that as one of the landing data sources, and we work with it to ensure we can get all of the data before it goes through our data ...

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What advice do you have for others considering Apache Spark Streaming?

One thing I would share with other organizations considering Apache Spark Streaming is the necessity of having effective data storage. We want to ensure we acquire and manage our data storage effec...

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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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Compared 13% of the time

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Compared 13% of the time

WSO2 Stream Processor vs Cloudera DataFlow

Compared 13% of the time

Confluent vs Cloudera DataFlow

Compared 10% of the time

Amazon Kinesis vs Cloudera DataFlow

Compared 5% of the time

More Cloudera DataFlow Competitors

Product Reports

Buyer's Guide

Apache Spark Streaming

October 2026

Download Apache Spark Streaming product report

Buyer's Guide

Streaming Analytics

September 2026

Download Cloudera DataFlow product report

Also Known As

Spark Streaming

CDF, Hortonworks DataFlow, HDF

Overview

Apache Spark Streaming efficiently processes real-time data with features like micro-batching and native Python support. It's scalable and integrates with many services, ideal for reducing data latency and enabling real-time analytics across industries.

Apache Spark Streaming is a powerful tool for real-time data processing and analytics, offering support for multiple languages and robust integration capabilities. Its open-source nature, combined with features like checkpointing and watermarking, makes it a reliable choice for managing data streams with low latency. However, it faces challenges with Kubernetes deployments and requires improvements in memory management and latency. The installation process and handling of structured and unstructured data also present complexities. Despite these challenges, it's heavily utilized in building data pipelines and leveraging machine learning algorithms.

What are Apache Spark Streaming's key features?

Native Python Support: Efficient processing with Python language integration.
Micro-Batching: Handles streams in small batches for real-time processing.
Real-Time Analytics: Enables instant data insights.
Scalability: Adapts to varying data loads.
Low Latency: Processes data with minimal delays.

What benefits or ROI should users expect?

Efficiency: Streamlined real-time data processing.
Reliability: Consistent performance across tasks.
Integration: Seamless connection with other services.
Cost Optimization: Reduces processing expenses over time.

In industries like healthcare, telecommunications, and logistics, Apache Spark Streaming is implemented for real-time data processing and machine learning. It aids in predictive maintenance, anomaly detection, and fraud detection by reducing data latency with comprehensive analytics. Organizations frequently use it alongside Kafka and cloud storage solutions to enhance GIS, predictive analytics, and Customer 360 profiling.

Apache

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

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, eBay Inc.

Clearsense

Buyer's Guide

Apache Spark Streaming vs. Cloudera DataFlow

September 2026

Free Report: Apache Spark Streaming vs. Cloudera DataFlow

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