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Apache Spark Streaming vs Cloudera 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

Apache Spark Streaming
Ranking in Streaming Analytics
10th
Average Rating
8.0
Reviews Sentiment
7.4
Number of Reviews
11
Ranking in other categories
No ranking in other categories
Cloudera DataFlow
Ranking in Streaming Analytics
15th
Average Rating
7.4
Reviews Sentiment
6.5
Number of Reviews
5
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of June 2025, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 2.7%, down from 3.8% compared to the previous year. The mindshare of Cloudera DataFlow is 1.1%, down from 1.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

Oscar Estorach - PeerSpot reviewer
Versatile and flexible when dealing with large-scale data streams
What I like about Spark is its versatility in supporting multiple languages and that makes it my preferred choice for building scalable and efficient systems, whether it is hooking databases with web applications or handling large-scale data transformations. Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows. It works well in the cloud, and you can structure data using Databricks or Spark, providing flexibility for different projects. Spark Streaming's flexibility shines when dealing with large-scale data streams. It caters to different needs, offering real-time insights for tasks like online sales analytics. The ability to prioritize data streams is valuable, especially for monitoring competitor prices online.
Mohamed-Saied - PeerSpot reviewer
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…

Quotes from Members

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

Pros

"Spark Streaming is critical, quite stable, full-featured, and scalable."
"Apache Spark Streaming was straightforward in terms of maintenance. It was actively developed, and migrating from an older to a newer version was quite simple."
"Apache Spark's capabilities for machine learning are quite extensive and can be used in a low-code way."
"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."
"The platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."
"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."
"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."
"The solution is better than average and some of the valuable features include efficiency and stability."
"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."
"DataFlow's performance is okay."
"The initial setup was not so difficult"
 

Cons

"The cost and load-related optimizations are areas where the tool lacks and needs improvement."
"The initial setup is quite complex."
"The solution itself could be easier to use."
"Integrating event-level streaming capabilities could be beneficial."
"We don't have enough experience to be judgmental about its flaws."
"It was resource-intensive, even for small-scale applications."
"We would like to have the ability to do arbitrary stateful functions in Python."
"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."
"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."
"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."
 

Pricing and Cost Advice

"People pay for Apache Spark Streaming as a service."
"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."
"Spark is an affordable solution, especially considering its open-source nature."
"I was using the open-source community version, which was self-hosted."
"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
26%
Computer Software Company
21%
Manufacturing Company
5%
University
5%
University
16%
Financial Services Firm
15%
Computer Software Company
13%
Retailer
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What do you like most about Apache Spark Streaming?
Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows.
What needs improvement with Apache Spark Streaming?
We don't have enough experience to be judgmental about its flaws, as we've only used stable features like batch micro-batch. Integration poses no problem; however, I don't use some features and can...
What is your primary use case for Apache Spark Streaming?
We use Spark Streaming in a micro-batch region. It's not a full real-time system, but it offers high performance and low latency.
What do you like most about Cloudera DataFlow?
The most effective features are data management and analytics.
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.
What is your primary use case for Cloudera DataFlow?
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...
 

Also Known As

Spark Streaming
CDF, Hortonworks DataFlow, HDF
 

Overview

 

Sample Customers

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, eBay Inc.
Clearsense
Find out what your peers are saying about Apache Spark Streaming vs. Cloudera DataFlow and other solutions. Updated: June 2025.
859,129 professionals have used our research since 2012.