Cloudera Distribution for Hadoop vs Spark SQL comparison

Cloudera and Apache are both solutions in the Hadoop category. Cloudera is ranked #2 with an average rating of 8.4, while Apache is ranked #5 with an average rating of 8.7. Cloudera holds a 23.3% mindshare in H, compared to Apache’s 10.4% mindshare. Additionally, 92% of Cloudera users are willing to recommend the solution, compared to 85% of Apache users who would recommend it.

Cloudera Distribution for H...

Read 51 Cloudera Distribution for Hadoop reviews

1,873 Views
1,255 Comparison Views

92% willing to recommend

Spark SQL

Read 14 Spark SQL reviews

662 Views
596 Comparison Views

85% willing to recommend

Cloudera Distribution for H...

Spark SQL

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Executive Summary

We performed a comparison between Cloudera Distribution for Hadoop and Spark SQL 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 Cloudera Distribution for Hadoop vs. Spark SQL Report (Updated: July 2025).

Buyer's Guide

Cloudera Distribution for Hadoop vs. Spark SQL

July 2025

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Helped 865,384 peers since 2012

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

Cloudera Distribution for H...

Ranking in Hadoop

2nd

Average Rating

8.0

Reviews Sentiment

6.3

Number of Reviews

Ranking in other categories

NoSQL Databases (8th)

Spark SQL

Ranking in Hadoop

5th

Average Rating

7.8

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of August 2025, in the Hadoop category, the mindshare of Cloudera Distribution for Hadoop is 23.3%, down from 25.0% compared to the previous year. The mindshare of Spark SQL is 10.4%, down from 11.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop

Featured Reviews

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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SurjitChoudhury

Data engineer at Cocos pt

Offers the flexibility to handle large-scale data processing

My experience with the initial setup of Spark SQL was relatively smooth. Understanding the system wasn't overly difficult because the data was structured in databases, and we could use notebooks for coding in Python or Java. Configuring networks and running scripts to load data into the database were routine tasks that didn't pose significant challenges. The flexibility to use different languages for coding and the ability to process data using key-value pairs in Python made the setup adaptable. Once we received the source data, processing it in SparkSQL involved writing scripts to create dimension and fact tables, which became a standard part of our workflow. Setting up Spark SQL was reasonably quick, but sometimes we face performance issues, especially during data loading into the SQL Server data warehouse. Sequencing notebooks for efficient job runs is crucial, and managing complex tasks with multiple notebooks requires careful tracking. Exploring ways to optimize this process could be beneficial. However, once you are familiar with the database architecture and project tools, understanding and adapting to the system become more straightforward.

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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

"The product provides better data processing features than other tools."

"The product as a whole is good."

"With a cluster available, you can manage the security layer using the shared SDX - it provides flexibility."

"The file system is a valuable feature."

"Cloudera Distribution for Hadoop provides numerous features and capabilities combined into one platform, offers power processing, supports different file systems and query engines, and provides parallel processing for handling many requests."

"The product is completely secure."

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

"Customer service and support were able to fix whatever the issue was."

More Cloudera Distribution for Hadoop pros

"This solution is useful to leverage within a distributed ecosystem."

"The performance is one of the most important features. It has an API to process the data in a functional manner."

"Data validation and ease of use are the most valuable features."

"Certain data sets that are very large are very difficult to process with Pandas and Python libraries. Spark SQL has helped us a lot with that."

"I find the Thrift connection valuable."

"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks."

"The solution is easy to understand if you have basic knowledge of SQL commands."

"Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline."

More Spark SQL pros

Cons

"The procedure for operations could be simplified."

"Cloudera Distribution for Hadoop is not always completely stable in some cases, which can be a concern for big data solutions."

"The pricing needs to improve."

"The price of this solution could be lowered."

"The security of this solution could be improved. There should also be a way to basically have a blockchain enabled storage with the HDFS."

"Cloudera Distribution for Hadoop has a limited feature list and a lot of costs involved."

"I would like to see an improvement in how the solution helps me to handle the whole cluster."

"If they could support modifying the data more easily than the current implementation, it would be beneficial."

More Cloudera Distribution for Hadoop cons

"There are many inconsistencies in syntax for the different querying tasks."

"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."

"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."

"Being a new user, I am not able to find out how to partition it correctly. I probably need more information or knowledge. In other database solutions, you can easily optimize all partitions. I haven't found a quicker way to do that in Spark SQL. It would be good if you don't need a partition here, and the system automatically partitions in the best way. They can also provide more educational resources for new users."

"It would be beneficial for aggregate functions to include a code block or toolbox that explains its calculations or supported conditional statements."

"I've experienced some incompatibilities when using the Delta Lake format."

"There should be better integration with other solutions."

"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."

More Spark SQL cons

Pricing and Cost Advice

"Cloudera requires a license to use."

"The pricing must be improved."

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

"It is an expensive 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 price could be better for the product."

"I wouldn't recommend CDH to others because of its high cost."

"When comparing with Oracle Sybase and SQL, it's cheaper. It's not expensive."

More Cloudera Distribution for Hadoop pricing and cost advice

"There is no license or subscription for this solution."

"The solution is bundled with Palantir Foundry at no extra charge."

"The solution is open-sourced and free."

"The on-premise solution is quite expensive in terms of hardware, setting up the cluster, memory, hardware and resources. It depends on the use case, but in our case with a shared cluster which is quite large, it is quite expensive."

"We use the open-source version, so we do not have direct support from Apache."

"We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small."

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Educational Organization

17%

Computer Software Company

12%

Energy/Utilities Company

Financial Services Firm

17%

University

10%

Retailer

10%

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

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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What do you like most about Spark SQL?

Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline.

See all answers

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

We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small.

See all answers

What needs improvement with Spark SQL?

In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL. There could be additional features that I haven't explored but the current solution for working ...

See all answers

Comparisons

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Compared 13% of the time

Couchbase Enterprise vs Cloudera Distribution for Hadoop

Compared 9% of the time

MongoDB Enterprise Advanced vs Cloudera Distribution for Hadoop

Compared 8% of the time

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Apache Spark vs Spark SQL

Compared 46% of the time

SAP HANA vs Spark SQL

Compared 14% of the time

IBM Db2 Big SQL vs Spark SQL

Compared 11% of the time

Amazon EMR vs Spark SQL

Compared 11% of the time

IBM Analytics Engine vs Spark SQL

Compared 10% of the time

More Spark SQL Competitors

Product Reports

Buyer's Guide

Cloudera Distribution for Hadoop

August 2025

Download Cloudera Distribution for Hadoop product report

Buyer's Guide

Spark SQL

July 2025

Download Spark SQL product report

Overview

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

Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. There are several ways to interact with Spark SQL including SQL and the Dataset API. When computing a result the same execution engine is used, independent of which API/language you are using to express the computation. This unification means that developers can easily switch back and forth between different APIs based on which provides the most natural way to express a given transformation.

Apache

Sample Customers

37signals, Adconion,adgooroo, Aggregate Knowledge, AMD, Apollo Group, Blackberry, Box, BT, CSC

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions

Buyer's Guide

Cloudera Distribution for Hadoop vs. Spark SQL

July 2025

Free Report: Cloudera Distribution for Hadoop vs. Spark SQL

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

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