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Apache Spark Streaming vs Cloudera DataFlow comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 17, 2024
 

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
14th
Average Rating
7.2
Reviews Sentiment
6.3
Number of Reviews
4
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of December 2024, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 3.6%, down from 4.6% compared to the previous year. The mindshare of Cloudera DataFlow is 1.2%, down from 1.6% 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.
Júlio César Gomes Fonseca - PeerSpot reviewer
A stable solution that helps develop quality modules but needs to improve its programming language
The initial setup was not so difficult. The deployment took so long, at least one or two years, because the team has a project that aims to be exceptional in the future. It's good to say because the company is very good. It's a self-confirmation technical integration company. We have numerous reasons why reducing staff workload is beneficial. However, it is important to note that this does not directly apply to the application used. They will only do the service.

Quotes from Members

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

Pros

"Apache Spark's capabilities for machine learning are quite extensive and can be used in a low-code way."
"The solution is better than average and some of the valuable features include efficiency and stability."
"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."
"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."
"Spark Streaming is critical, quite stable, full-featured, and scalable."
"It's the fastest solution on the market with low latency data on data transformations."
"As an open-source solution, using it is basically free."
"The solution is very stable and reliable."
"The most effective features are data management and analytics."
"This solution is very scalable and robust."
"DataFlow's performance is okay."
"The initial setup was not so difficult"
 

Cons

"The debugging aspect could use some improvement."
"Integrating event-level streaming capabilities could be beneficial."
"It was resource-intensive, even for small-scale applications."
"We don't have enough experience to be judgmental about its flaws."
"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 solution itself could be easier to use."
"The service structure of Apache Spark Streaming can improve. There are a lot of issues with memory management and latency. There is no real-time analytics. We recommend it for the use cases where there is a five-second latency, but not for a millisecond, an IOT-based, or the detection anomaly-based. Flink as a service is much better."
"The cost and load-related optimizations are areas where the tool lacks and needs improvement."
"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."
"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."
 

Pricing and Cost Advice

"People pay for Apache Spark Streaming as a service."
"Spark is an affordable solution, especially considering its open-source nature."
"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."
"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
24%
Computer Software Company
20%
Manufacturing Company
6%
University
5%
Computer Software Company
18%
Financial Services Firm
17%
University
12%
Manufacturing Company
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 is your primary use case for Cloudera DataFlow?
We use Cloudera DataFlow for stream analytics.
 

Also Known As

Spark Streaming
CDF, Hortonworks DataFlow, HDF
 

Learn More

 

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: December 2024.
824,053 professionals have used our research since 2012.