Try our new research platform with insights from 80,000+ expert users

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
7th
Average Rating
7.8
Reviews Sentiment
6.4
Number of Reviews
17
Ranking in other categories
No ranking in other categories
Cloudera DataFlow
Ranking in Streaming Analytics
17th
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 October 2025, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 3.6%, up from 3.4% compared to the previous year. The mindshare of Cloudera DataFlow is 1.3%, down from 1.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics Market Share Distribution
ProductMarket Share (%)
Apache Spark Streaming3.6%
Cloudera DataFlow1.3%
Other95.1%
Streaming Analytics
 

Featured Reviews

Himansu Jena - PeerSpot reviewer
Efficient real-time data management and analysis with advanced features
There are various ways we can improve Apache Spark Streaming through best practices. The initial part requires attention to batch interval tuning, which helps small intervals in micro batches based on latency requirements and helps prevent back pressure. We can use data formats such as Parquet or ORC for storage that needs faster reads and leveraging feature predicate push-down optimizations. We can implement serialization which helps with any Kyro in terms of .NET or Java. We have boxing and unboxing serialization for XML and JSON for converting key-pair values stored in browser. We can also implement caching mechanisms for storing and recomputing multiple operations. We can use specified joins which help with smaller databases, and distributed joins can minimize users. We can implement project optimization memory for CPU efficiency, known as Tungsten. Additionally, load balancing, checkpointing, and schema evaluation are areas to consider based on performance and bottlenecks. We can use Bugzilla tools for tracking and Splunk to monitor the performance of process systems, utilization, and performance based on data frames or data sets.
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

"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."
"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 main benefits of Apache Spark Streaming include cost savings, time savings, and efficiency improvements about data storage."
"With Apache Spark Streaming's integration with Anaconda and Miniconda with Python, I interact with databases using data frames or data sets in micro versions and create solutions based on business expectations for decision-making, logistic regression, linear regression, or machine learning which provides image or voice record and graphical data for improved accuracy."
"For Apache Spark Streaming, the feature I appreciated most is that it provides live data delivery; additionally, it provides the capability to send a larger amount of data in parallel."
"I appreciate Apache Spark Streaming's micro-batching capabilities; the watermarking functionality and related features are quite good."
"The main benefits of Apache Spark Streaming include cost savings, time savings, and efficiency improvements about data storage."
"Apache Spark's capabilities for machine learning are quite extensive and can be used in a low-code way."
"The initial setup was not so difficult"
"This solution is very scalable and robust."
"DataFlow's performance is okay."
"The most effective features are data management and analytics."
"Cloudera DataFlow is fully compatible with Cloudera's ecosystem and offers high efficiency through native connectors for various ecosystems."
 

Cons

"We would like to have the ability to do arbitrary stateful functions in Python."
"Integrating event-level streaming capabilities could be beneficial."
"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."
"The downside is when you have this the other way around in the columns, it becomes really hard to use."
"In terms of improvement, the UI could be better."
"One improvement I would expect is real-time processing instead of micro-batch or near real-time."
"It was resource-intensive, even for small-scale applications."
"The initial setup is quite complex."
"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

"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."
"I was using the open-source community version, which was self-hosted."
"Spark is an affordable solution, especially considering its open-source nature."
"DataFlow isn't expensive, but its value for money isn't great."
report
Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.
869,202 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Computer Software Company
23%
Financial Services Firm
20%
Healthcare Company
6%
University
6%
University
24%
Computer Software Company
13%
Financial Services Firm
11%
Performing Arts
10%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business9
Midsize Enterprise2
Large Enterprise7
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?
I believe the downsides of Apache Spark Streaming are that it primarily supports structured data. Currently, in my organization, we require thousands of transcripts that need to be handled during l...
What is your primary use case for Apache Spark Streaming?
My use cases for Apache Spark Streaming were during my academics. During that time, I used Apache Spark Streaming to transmit data live from one source to another.
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: September 2025.
869,202 professionals have used our research since 2012.