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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
6.6
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.
The technical support is quite good and better than IBM.
 

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
7.5
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.
MapReduce needs to perform numerous disk input and output operations, while Apache Spark can use memory to store and process data.
We faced challenges but overcame those challenges successfully.
 

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.
Integrating with Active Directory, managing security, and configuration are the main concerns.
 

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.
 

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.
Apache Spark is the solution, and within it, you have PySpark, which is the API for Apache Spark to write and run Python code.
This is the only solution that is possible to install on-premise.
 

Categories and Ranking

Apache Spark
Ranking in Hadoop
1st
Average Rating
8.4
Reviews Sentiment
7.3
Number of Reviews
67
Ranking in other categories
Compute Service (4th), 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 (8th)
 

Mindshare comparison

As of August 2025, in the Hadoop category, the mindshare of Apache Spark is 19.2%, down from 20.2% compared to the previous year. The mindshare of Cloudera Distribution for Hadoop is 23.3%, down from 25.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop
 

Featured Reviews

Omar Khaled - PeerSpot reviewer
Empowering data consolidation and fast decision-making with efficient big data processing
I can improve the organization's functions by taking less time to make decisions. To make the right decision, you need the right data, and a solution can provide this by hiring talent and employees who can consolidate data from different sources and organize it. Not all solutions can make this data fast enough to be used, except for solutions such as Apache Spark Structured Streaming. To make the right decision, you should have both accurate and fast data. Apache Spark itself is similar to the Python programming language. Python is a language with many libraries for mathematics and machine learning. Apache Spark is the solution, and within it, you have PySpark, which is the API for Apache Spark to write and run Python code. Within it, there are many APIs, including SQL APIs, allowing you to write SQL code within a Python function in Apache Spark. You can also use Apache Spark Structured Streaming and machine learning APIs.
Rok Dolinsek - PeerSpot reviewer
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
26%
Computer Software Company
10%
Manufacturing Company
7%
Comms Service Provider
7%
Financial Services Firm
19%
Educational Organization
17%
Computer Software Company
12%
Energy/Utilities Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

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?
There is complexity when it comes to understanding the whole ecosystem, especially for beginners. I find it quite complex to understand how a Spark job is initiated, the roles of driver nodes, work...
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?
It is quite complicated to configure and install. Integrating the platform into an information system is always a challenge, especially when starting with on-premise implementation. Integrating wit...
 

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: July 2025.
865,164 professionals have used our research since 2012.