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 8.3, while Apache is ranked #5 with an average rating of 8.7. Cloudera holds a 25.5% mindshare in H, compared to Apache’s 10.6% 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 50 Cloudera Distribution for Hadoop reviews

1,398 Views
1,053 Comparison Views

92% willing to recommend

Spark SQL

Read 14 Spark SQL reviews

696 Views
483 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: June 2025).

Buyer's Guide

Cloudera Distribution for Hadoop vs. Spark SQL

June 2025

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Helped 859,129 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

6.4

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 June 2025, in the Hadoop category, the mindshare of Cloudera Distribution for Hadoop is 25.5%, up from 24.2% compared to the previous year. The mindshare of Spark SQL is 10.6%, down from 11.8% 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 tool's most interesting features are the distributed file system and unstructured data processing capability. Because we have a lot of unstructured data, like XML and social media logs, these features make it more valuable than the usual data warehousing solutions."

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

"The solution's most valuable feature is the enterprise data platform."

"It has the best proxy, security, and support features compared to open-source products."

"In terms of scalability, if you have enough hardware you can scale out. Scalability doesn't have any issues."

"We had a data warehouse before all the data. We can process a lot more data structures."

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

"The most valuable feature is Kubernetes."

More Cloudera Distribution for Hadoop pros

"It is a stable solution."

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

"I find the Thrift connection valuable."

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

"The team members don't have to learn a new language and can implement complex tasks very easily using only 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."

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

"The stability was fine. It behaved as expected."

More Spark SQL pros

Cons

"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."

"It could be faster and more user-friendly."

"The performance of some analytics engines provided by Cloudera is not that good."

"The tool's ability to be deployed on a cloud model is an area of concern where improvements are required."

"There is a maximum of a one-gigabyte block size, which is an area of storage that can be improved upon."

"They should focus on upgrading their technical capabilities in the market."

"The initial setup of Cloudera is difficult."

"It is quite complicated to configure and install."

More Cloudera Distribution for Hadoop cons

"Anything to improve the GUI would be helpful."

"This solution could be improved by adding monitoring and integration for the EMR."

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

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

"In the next update, we'd like to see better performance for small points of data. It is possible but there are better tools that are faster and cheaper."

"There should be better integration with other solutions."

"SparkUI could have more advanced versions of the performance and the queries and all."

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

More Spark SQL cons

Pricing and Cost Advice

"Cloudera requires a license to use."

"I believe we pay for a three-year license."

"The pricing must be improved."

"The product’s price depends from project to project."

"The solution is fairly expensive."

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

"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."

More Cloudera Distribution for Hadoop pricing and cost advice

"The solution is open-sourced and free."

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

"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."

"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."

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

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

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Top Industries

By visitors reading reviews

Financial Services Firm

23%

Educational Organization

15%

Computer Software Company

14%

Energy/Utilities Company

Financial Services Firm

19%

Computer Software Company

13%

Retailer

Healthcare 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...

See all answers

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

HPE Ezmeral Data Fabric vs Cloudera Distribution for Hadoop

Compared 28% of the time

Amazon EMR vs Cloudera Distribution for Hadoop

Compared 27% of the time

Apache Spark vs Cloudera Distribution for Hadoop

Compared 16% of the time

Cassandra vs Cloudera Distribution for Hadoop

Compared 6% of the time

Apache HBase vs Cloudera Distribution for Hadoop

Compared 4% of the time

More Cloudera Distribution for Hadoop Competitors

Apache Spark vs Spark SQL

Compared 48% of the time

SAP HANA vs Spark SQL

Compared 12% of the time

IBM Db2 Big SQL vs Spark SQL

Compared 11% of the time

HPE Ezmeral Data Fabric vs Spark SQL

Compared 10% of the time

Amazon EMR vs Spark SQL

Compared 9% of the time

More Spark SQL Competitors

Product Reports

Buyer's Guide

Cloudera Distribution for Hadoop

June 2025

Download Cloudera Distribution for Hadoop product report

Buyer's Guide

Spark SQL

May 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

June 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: June 2025.

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