Apache Spark vs Cloudera Distribution for Hadoop comparison

Apache Spark vs. Cloudera Distribution for Hadoop

Download the complete report

Helped 842,767 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

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.

Read full review

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.

Read full review

Quotes from Members

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

Pros

"I like Apache Spark's flexibility the most. Before, we had one server that would choke up. With the solution, we can easily add more nodes when needed. The machine learning models are also really helpful. We use them to predict energy theft and find infrastructure problems."

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

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

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

"ETL and streaming capabilities."

"Apache Spark can do large volume interactive data analysis."

More Apache Spark pros

"Very good end-to-end security features."

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

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

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

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

"The solution is stable."

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

"The main advantage is the storage is less expensive."

More Cloudera Distribution for Hadoop pros

Cons

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

"We are building our own queries on Spark, and it can be improved in terms of query handling."

"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 initial setup was not easy."

"It requires overcoming a significant learning curve due to its robust and feature-rich nature."

"We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."

"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."

"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."

More Apache Spark cons

"Cloudera's support is extremely bad and cannot be relied on."

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

"The pricing needs to improve."

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

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

"It is quite complicated to configure and install."

"The one thing that we struggled with predominately was support. Because it was relatively new, support was always a big issue and I think it's still a bit of an ongoing concern with the team currently managing it."

"This is a very expensive solution."

More Cloudera Distribution for Hadoop cons

Pricing and Cost Advice

"Apache Spark is an open-source tool."

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

"Apache Spark is not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera."

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

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

"It is an open-source platform. We do not pay for its subscription."

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

"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."

More Apache Spark pricing and cost advice

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

"The solution is fairly expensive."

"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 expensive."

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

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

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

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

More Cloudera Distribution for Hadoop pricing and cost advice

See which vendors are best for you

Use our free recommendation engine to learn which Hadoop solutions are best for your needs.

See recommendations

842,767 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

28%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

Financial Services Firm

25%

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