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

"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."

"I appreciate everything about the solution, not just one or two specific features. The solution is highly stable. I rate it a perfect ten. The solution is highly scalable. I rate it a perfect ten. The initial setup was straightforward. I recommend using the solution. Overall, I rate the solution a perfect ten."

"The main feature that we find valuable is that it is very fast."

"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."

"Spark is used for transformations from large volumes of data, and it is usefully distributed."

"One of the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them."

"ETL and streaming capabilities."

"The product’s most valuable features are lazy evaluation and workload distribution."

More Apache Spark pros

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

"We're now able to store large volumes of data through Cloudera Distribution for Hadoop. We're able to push large volumes of data to the platform, and that used to be a challenge, especially when storing a terabyte of information. This is the area where Cloudera Distribution for Hadoop improved the organization."

"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."

"Cloudera provides a hybrid solution that combines compute on cloud or on-premises."

"I don't see any performance issues."

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

"The most valuable feature is Kubernetes."

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

More Cloudera Distribution for Hadoop pros

Cons

"Apache Spark should add some resource management improvements to the algorithms."

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

"The Spark solution could improve in scheduling tasks and managing dependencies."

"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."

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

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

"The solution must improve its performance."

"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."

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."

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

"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."

"There are better solutions out there that have more features than this one."

"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 pricing needs to improve."

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

"There are multiple bugs when we update."

More Cloudera Distribution for Hadoop cons

Pricing and Cost Advice

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

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

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

"Apache Spark is an expensive solution."

"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."

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

"Apache Spark is an open-source tool."

"The product is expensive, considering the setup."

More Apache Spark pricing and cost advice

"The tool is not expensive."

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

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

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

"Cloudera requires a license to use."

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

"The price could be better for the product."

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

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