Apache Spark vs Cloudera Distribution for Hadoop comparison

Apache and Cloudera are both solutions in the Hadoop category. Apache is ranked #1 with an average rating of 8.6, while Cloudera is ranked #2 with an average rating of 8.3. Apache holds a 17.5% mindshare in H, compared to Cloudera’s 25.0% mindshare. Additionally, 90% of Apache users are willing to recommend the solution, compared to 92% of Cloudera users who would recommend it.

Apache Spark

Read 65 Apache Spark reviews

1,404 Views
1,090 Comparison Views

90% willing to recommend

Cloudera Distribution for H...

Read 50 Cloudera Distribution for Hadoop reviews

1,644 Views
1,275 Comparison Views

92% willing to recommend

Apache Spark

Cloudera Distribution for H...

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Executive Summary

We performed a comparison between Apache Spark and Cloudera Distribution for Hadoop based on real PeerSpot user reviews.

Find out in this report how the two Hadoop solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Apache Spark vs. Cloudera Distribution for Hadoop Report (Updated: March 2025).

Buyer's Guide

Apache Spark vs. Cloudera Distribution for Hadoop

March 2025

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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

Ranking in Hadoop

1st

Average Rating

8.4

Reviews Sentiment

7.7

Number of Reviews

Ranking in other categories

Compute Service (4th), Java Frameworks (2nd)

Cloudera Distribution for H...

Ranking in Hadoop

2nd

Average Rating

8.0

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

NoSQL Databases (8th)

Mindshare comparison

As of April 2025, in the Hadoop category, the mindshare of Apache Spark is 17.5%, down from 21.4% compared to the previous year. The mindshare of Cloudera Distribution for Hadoop is 25.0%, up from 23.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop

Featured Reviews

Ilya Afanasyev

Senior Software Development Engineer at Yahoo!

Reliable, able to expand, and handle large amounts of data well

We use batch processing. It works well with our formats and file versions. There's a lot of functionality. In our pipeline each hour, we make a copy of data from MongoDB, of the changes from MongoDB to some specific file. Each time pipeline copied all of the data, it would do it each time without changes to all of the tables. Tables have a lot of data, and in the last MongoDB version, there is a possibility to read only changed data. This reduced the cost and configuration of the cluster, and we saved about $150,000. The solution is scalable. It's a stable product.

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Rok Dolinsek

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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Quotes from Members

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

Pros

"I feel the streaming is its best feature."

"This solution provides a clear and convenient syntax for our analytical tasks."

"The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly."

"The tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily."

"The most valuable feature of Apache Spark is its flexibility."

"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."

"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."

"The features we find most valuable are the machine learning, data learning, and Spark Analytics."

More Apache Spark pros

"The data science aspect of the solution is valuable."

"Provides a viable open-source solution for enterprise implementations and reliable, intelligent data analysis."

"The product as a whole is good."

"The search function is the most valuable aspect of the solution."

"The solution is reliable and stable, it fits our requirements."

"Cloudera, as a whole, is designed to provide organizations with solutions for big data."

"The most valuable feature is that I can use CDH for almost all use cases across all industries, including the financial sector, public sector, private retailers, and so on."

"CDH has a wide variety of proprietary tools that we use, like Impala. So from that perspective, it's quite useful as opposed to something open-source. We get a lot of value from Cloudera's proprietary tools."

More Cloudera Distribution for Hadoop pros

Cons

"For improvement, I think the tool could make things easier for people who aren't very technical. There's a significant learning curve, and I've seen organizations give up because of it. Making it quicker or easier for non-technical people would be beneficial."

"I know there is always discussion about which language to write applications in and some people do love Scala. However, I don't like it."

"One limitation is that not all machine learning libraries and models support it."

"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."

"The migration of data between different versions could be improved."

"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."

"We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data."

"Apache Spark provides very good performance The tuning phase is still tricky."

More Apache Spark cons

"The tool doesn't support reporting, and relational databases are still the major source of reporting data. Apache Iceberg will be launched soon within the Cloudera cluster for analytical purposes. The Cloudera Machine Learning aspect could be tuned and enhanced to enable us to host some predictive analytics machine learning and AI use cases."

"The dashboard could be improved."

"It would be useful if Cloudera had more tools like SQL Engines that offer the traditional relational database. We have to do a lot of work preparing the data outside Cloudera before getting it into the platform."

"The price of this solution could be lowered."

"We experienced many issues when we started working with Hadoop 3.0 in the Cloudera 6.0 version, so there is a lot of things that need to improve."

"The solution does not support multiple languages very well and this means users need to create work-arounds to implement some solutions."

"The Cloudera training has deteriorated significantly."

"This is a very expensive solution."

More Cloudera Distribution for Hadoop cons

Pricing and Cost Advice

"The tool is an open-source product. If you're using the open-source Apache Spark, no fees are involved at any time. Charges only come into play when using it with other services like Databricks."

"The product is expensive, considering the setup."

"Licensing costs can vary. For instance, when purchasing a virtual machine, you're asked if you want to take advantage of the hybrid benefit or if you prefer the license costs to be included upfront by the cloud service provider, such as Azure. If you choose the hybrid benefit, it indicates you already possess a license for the operating system and wish to avoid additional charges for that specific VM in Azure. This approach allows for a reduction in licensing costs, charging only for the service and associated resources."

"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."

"Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."

"We are using the free version of the solution."

"Spark is an open-source solution, so there are no licensing costs."

"I did not pay anything when using the tool on cloud services, but I had to pay on the compute side. The tool is not expensive compared with the benefits it offers. I rate the price as an eight out of ten."

More Apache Spark pricing and cost advice

"It is an expensive product."

"I haven't bought a license for this solution. I'm only using the Apache license version."

"The price could be better for the product."

"The tool is expensive...For the SMB market or customers whose environments are not that complex and do not have multiple systems running, Cloudera might not be a good option."

"The solution is fairly expensive."

"The solution is expensive."

"The price is very high. The solution is expensive."

"The pricing must be improved."

More Cloudera Distribution for Hadoop pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

Financial Services Firm

25%

Computer Software Company

15%

Educational Organization

13%

Manufacturing Company

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.

See all answers

What is your experience regarding pricing and costs for Apache Spark?

Compared to other solutions like Doc DB, Spark is more costly due to the need for extensive infrastructure. It requires significant investment in infrastructure, which can be expensive. While cloud...

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What needs improvement with Apache Spark?

The Spark solution could improve in scheduling tasks and managing dependencies. Spark alone cannot handle sequential tasks, requiring environments like Airflow scheduler or scripts. For instance, o...

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What do you like most about Cloudera Distribution for Hadoop?

The tool can be deployed using different container technologies, which makes it very scalable.

See all answers

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.

See all answers

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...

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Product Reports

Buyer's Guide

Apache Spark

April 2025

Download Apache Spark product report

Buyer's Guide

Cloudera Distribution for Hadoop

April 2025

Download Cloudera Distribution for Hadoop product report

Overview

Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory

Apache

Cloudera Distribution for Hadoop is the world's most complete, tested, and popular distribution of Apache Hadoop and related projects. CDH is 100% Apache-licensed open source and is the only Hadoop solution to offer unified batch processing, interactive SQL, and interactive search, and role-based access controls. More enterprises have downloaded CDH than all other such distributions combined.

Cloudera

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

Buyer's Guide

Apache Spark vs. Cloudera Distribution for Hadoop

March 2025

Free Report: Apache Spark vs. Cloudera Distribution for Hadoop

Find out what your peers are saying about Apache Spark vs. Cloudera Distribution for Hadoop and other solutions. Updated: March 2025.

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