Apache Spark vs Cloudera Distribution for Hadoop comparison

Apache Spark vs. Cloudera Distribution for Hadoop

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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

"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."

"I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library."

"The solution has been very stable."

"Now, when we're tackling sentiment analysis using NLP technologies, we deal with unstructured data—customer chats, feedback on promotions or demos, and even media like images, audio, and video files. For processing such data, we rely on PySpark. Beneath the surface, Spark functions as a compute engine with in-memory processing capabilities, enhancing performance through features like broadcasting and caching. It's become a crucial tool, widely adopted by 90% of companies for a decade or more."

"The processing time is very much improved over the data warehouse solution that we were using."

"Features include machine learning, real time streaming, and data processing."

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

"The good performance. The nice graphical management console. The long list of ML algorithms."

More Apache Spark pros

"The scalability of Cloudera Distribution for Hadoop is excellent."

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

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

"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."

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

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

"I don't see any performance issues."

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

More Cloudera Distribution for Hadoop pros

Cons

"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."

"The main concern is the overhead of Java when distributed processing is not necessary."

"There were some problems related to the product's compatibility with a few Python libraries."

"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."

"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."

"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 solution needs to optimize shuffling between workers."

"The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate."

More Apache Spark cons

"The competitors provide better functionalities."

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

"The procedure for operations could be simplified."

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

"The initial setup of Cloudera is difficult."

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

"Currently, we are using many other tools such as Spark and Blade Job to improve the performance."

"The user infrastructure and user interface needs to be improved, as well as the performance. The GUI needs to be better."

More Cloudera Distribution for Hadoop cons

Pricing and Cost Advice

"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."

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

"Apache Spark is an open-source tool."

"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."

"The solution is affordable and there are no additional licensing costs."

"The product is expensive, considering the setup."

"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."

"They provide an open-source license for the on-premise version."

More Apache Spark pricing and cost advice

"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."

"It is an expensive product."

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

"Cloudera Distribution for Hadoop is expensive, with support costs involved."

"The solution is expensive."

"The solution is fairly expensive."

"The tool is not 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

28%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

Financial Services Firm

24%

Computer Software Company

15%

Educational Organization

12%

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.

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...

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...

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...

Spring Boot vs Apache Spark

Comparisons

Compared 27% of the time

SAP HANA vs Apache Spark

Compared 12% of the time

AWS Batch vs Apache Spark

Compared 12% of the time

Spark SQL vs Apache Spark

Compared 7% of the time

AWS Lambda vs Apache Spark

Compared 6% of the time

More Apache Spark Competitors

HPE Ezmeral Data Fabric vs Cloudera Distribution for Hadoop

Compared 31% of the time

Amazon EMR vs Cloudera Distribution for Hadoop

Compared 28% of the time

Neo4j Graph Database vs Cloudera Distribution for Hadoop

Compared 4% of the time

Cassandra vs Cloudera Distribution for Hadoop

Compared 4% of the time

ScyllaDB vs Cloudera Distribution for Hadoop

Compared 3% of the time

More Cloudera Distribution for Hadoop Competitors

Product Reports

Apache Spark

Download Apache Spark product report

Cloudera Distribution for Hadoop

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

Apache Spark vs. Cloudera Distribution for Hadoop