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Apache Spark Streaming vs Cloudera DataFlow comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Apache Spark Streaming
Ranking in Streaming Analytics
9th
Average Rating
8.0
Reviews Sentiment
6.2
Number of Reviews
10
Ranking in other categories
No ranking in other categories
Cloudera DataFlow
Ranking in Streaming Analytics
14th
Average Rating
7.2
Reviews Sentiment
6.8
Number of Reviews
4
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of November 2024, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 3.7%, down from 4.7% compared to the previous year. The mindshare of Cloudera DataFlow is 1.4%, 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

"As an open-source solution, using it is basically free."
"The platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."
"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."
"Apache Spark's capabilities for machine learning are quite extensive and can be used in a low-code way."
"It's the fastest solution on the market with low latency data on data transformations."
"The solution is better than average and some of the valuable features include efficiency and stability."
"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 was straightforward in terms of maintenance. It was actively developed, and migrating from an older to a newer version was quite simple."
"The initial setup was not so difficult"
"This solution is very scalable and robust."
"The most effective features are data management and analytics."
"DataFlow's performance is okay."
 

Cons

"It was resource-intensive, even for small-scale applications."
"In terms of improvement, the UI could be better."
"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 initial setup is quite complex."
"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."
"The solution itself could be easier to use."
"Integrating event-level streaming capabilities could be beneficial."
"It's an outdated legacy product that doesn't meet the needs of modern data analysts and scientists."
"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."
"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

"I was using the open-source community version, which was self-hosted."
"Spark is an affordable solution, especially considering its open-source nature."
"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."
"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
22%
Computer Software Company
20%
University
6%
Manufacturing Company
6%
Computer Software Company
20%
Financial Services Firm
16%
University
11%
Manufacturing Company
8%
 

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?
The product's event handling capabilities, particularly compared to Kaspersky, need improvement. Integrating event-level streaming capabilities could be beneficial. This aligns with the idea of exp...
What is your primary use case for Apache Spark Streaming?
I've used it more for ETL. It's useful for creating data pipelines, streaming datasets, generating synthetic data, synchronizing data, creating data lakes, and loading and unloading data is fast an...
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: October 2024.
816,406 professionals have used our research since 2012.