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Apache Spark vs Cloudera Distribution for Hadoop comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

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

ROI

Sentiment score
6.1
Apache Spark enhances machine learning, cutting operational costs by up to 50%, with efficiency reliant on resources and expertise.
Sentiment score
5.5
Measuring ROI from Cloudera Distribution for Hadoop is complex due to diverse applications, pricing, and evaluation difficulties.
 

Customer Service

Sentiment score
5.9
Apache Spark support feedback varies, with mixed reviews on community forums, vendor support, and documentation adequacy.
Sentiment score
6.5
Cloudera's Hadoop support receives mixed reviews, with users praising responsiveness while noting concerns on quality and accessibility.
I have received support via newsgroups or guidance on specific discussions, which is what I would expect in an open-source situation.
Data Architect at Devtech
The technical support is quite good and better than IBM.
Manager, Bussines Development & Co Owner at Troia d.o.o.
 

Scalability Issues

Sentiment score
7.5
Apache Spark excels in scalability, efficiently handling large data workloads with ease, though it requires skilled infrastructure management.
Sentiment score
7.7
Cloudera Distribution for Hadoop is highly scalable and flexible, suitable for large deployments but can be costly to expand.
 

Stability Issues

Sentiment score
7.4
Apache Spark is generally stable, trusted by companies; newer versions enhance reliability, though memory issues may arise without proper configuration.
Sentiment score
7.3
Cloudera Distribution for Hadoop has mixed stability reviews, with hardware issues noted, but support and workarounds are available.
Apache Spark resolves many problems in the MapReduce solution and Hadoop, such as the inability to run effective Python or machine learning algorithms.
Data Engineer at a tech company with 10,001+ employees
Without a doubt, we have had some crashes because each situation is different, and while the prototype in my environment is stable, we do not know everything at other customer sites.
Data Architect at Devtech
We faced challenges but overcame those challenges successfully.
Head of Advaced Analytics & Intelligence; AGM at Alinma Bank
 

Room For Improvement

Apache Spark requires improvements in scalability, usability, documentation, memory efficiency, real-time processing, and broader language support for better performance.
Cloudera Distribution for Hadoop struggles with stability and integration, needing better performance, security, documentation, and modern deployment solutions.
Various tools like Informatica, TIBCO, or Talend offer specific aspects, licensing can be costly;
Data Architect at Devtech
Integrating with Active Directory, managing security, and configuration are the main concerns.
Manager, Bussines Development & Co Owner at Troia d.o.o.
 

Setup Cost

Apache Spark is cost-effective but may incur expenses from hardware, cloud resources, or commercial support, impacting deployment costs.
Cloudera's Hadoop distribution is costly, aimed at large enterprises, lacking a community version, with per-node licensing.
It can be deployed on-premises, unlike competitors' cloud-only solutions.
Manager, Bussines Development & Co Owner at Troia d.o.o.
 

Valuable Features

Apache Spark offers fast in-memory processing, scalable analytics, MLlib for machine learning, SQL support, and seamless integration with languages.
Cloudera for Hadoop offers easy installation, robust security, tool integration, scalability, and supports on-premises and cloud environments.
Not all solutions can make this data fast enough to be used, except for solutions such as Apache Spark Structured Streaming.
Data Engineer at a tech company with 10,001+ employees
The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.
Data Architect at Devtech
This is the only solution that is possible to install on-premise.
Manager, Bussines Development & Co Owner at Troia d.o.o.
 

Categories and Ranking

Apache Spark
Ranking in Hadoop
1st
Average Rating
8.4
Reviews Sentiment
6.9
Number of Reviews
68
Ranking in other categories
Compute Service (5th), Java Frameworks (2nd)
Cloudera Distribution for H...
Ranking in Hadoop
2nd
Average Rating
8.0
Reviews Sentiment
6.3
Number of Reviews
51
Ranking in other categories
NoSQL Databases (9th)
 

Mindshare comparison

As of January 2026, in the Hadoop category, the mindshare of Apache Spark is 13.9%, down from 18.2% compared to the previous year. The mindshare of Cloudera Distribution for Hadoop is 15.1%, down from 27.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Market Share Distribution
ProductMarket Share (%)
Apache Spark13.9%
Cloudera Distribution for Hadoop15.1%
Other71.0%
Hadoop
 

Featured Reviews

Devindra Weerasooriya - PeerSpot reviewer
Data Architect at Devtech
Provides a consistent framework for building data integration and access solutions with reliable performance
The in-memory computation feature is certainly helpful for my processing tasks. It is helpful because while using structures that could be held in memory rather than stored during the period of computation, I go for the in-memory option, though there are limitations related to holding it in memory that need to be addressed, but I have a preference for in-memory computation. The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.
Rok Dolinsek - PeerSpot reviewer
Manager, Bussines Development & Co Owner at Troia d.o.o.
Enables on-premise implementation with powerful data processing capabilities
This is the only solution that is possible to install on-premise. Cloudera provides a hybrid solution that combines compute on cloud or on-premises. It includes all machine learning algorithms in the Spark machine learning library. All functionalities needed for a big data platform and ETL are on the platform, eliminating the need for other tools. It is scalable, ready for vertical scaling, and very powerful, offering numerous functionalities and configurations for generative AI.
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Top Industries

By visitors reading reviews
Financial Services Firm
25%
Computer Software Company
11%
Manufacturing Company
7%
Comms Service Provider
6%
Financial Services Firm
19%
Educational Organization
19%
Computer Software Company
8%
Healthcare Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business28
Midsize Enterprise15
Large Enterprise32
By reviewers
Company SizeCount
Small Business16
Midsize Enterprise9
Large Enterprise31
 

Questions from the Community

What do you like most about Apache Spark?
We use Spark to process data from different data sources.
What is your experience regarding pricing and costs for Apache Spark?
Apache Spark is open-source, so it doesn't incur any charges.
What needs improvement with Apache Spark?
Areas for improvement are obviously ease of use considerations, though there are limitations in doing that, so while various tools like Informatica, TIBCO, or Talend offer specific aspects, licensi...
What do you like most about Cloudera Distribution for Hadoop?
The tool can be deployed using different container technologies, which makes it very scalable.
What is your experience regarding pricing and costs for Cloudera Distribution for Hadoop?
The price for Cloudera is average, yet it is very good compared to other solutions. It can be deployed on-premises, unlike competitors' cloud-only solutions.
What needs improvement with Cloudera Distribution for Hadoop?
If they could support modifying the data more easily than the current implementation, it would be beneficial.
 

Overview

 

Sample Customers

NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
37signals, Adconion,adgooroo, Aggregate Knowledge, AMD, Apollo Group, Blackberry, Box, BT, CSC
Find out what your peers are saying about Apache Spark vs. Cloudera Distribution for Hadoop and other solutions. Updated: December 2025.
879,768 professionals have used our research since 2012.