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 March 2025, in the Hadoop category, the mindshare of Apache Spark is 17.8%, down from 21.2% compared to the previous year. The mindshare of Cloudera Distribution for Hadoop is 25.6%, up from 22.7% 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.

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

"ETL and streaming capabilities."

"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."

"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."

"The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."

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

"The product's initial setup phase was easy."

"The product is useful for analytics."

"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."

More Apache Spark pros

"The tool can be deployed using different container technologies, which makes it very scalable."

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

"The product as a whole is good."

"I don't see any performance issues."

"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 product is completely secure."

"Cloudera, as a whole, is designed to provide organizations with solutions for big data."

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

More Cloudera Distribution for Hadoop pros

Cons

"From my perspective, the only thing that needs improvement is the interface, as it was not easily understandable."

"Apache Spark lacks geospatial data."

"The setup I worked on was really complex."

"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."

"Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."

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

"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."

"In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that."

More Apache Spark cons

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

"This is a very expensive solution."

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

"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."

"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."

"The pricing needs to improve."

"The dashboard could be improved."

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

More Cloudera Distribution for Hadoop cons

Pricing and Cost Advice

"It is an open-source solution, it is free of charge."

"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 an open-source tool."

"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."

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

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

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

More Apache Spark pricing and cost advice

"Cloudera requires a license to use."

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

"The solution is fairly expensive."

"The price could be better for the product."

"The pricing must be improved."

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

"It is an expensive product."

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

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...

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Comparisons

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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

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HPE Ezmeral Data Fabric vs Cloudera Distribution for Hadoop

Compared 29% of the time

Amazon EMR vs Cloudera Distribution for Hadoop

Compared 28% of the time

Vertica vs Cloudera Distribution for Hadoop

Compared 3% of the time

Apache HBase vs Cloudera Distribution for Hadoop

Compared 3% of the time

Neo4j Graph Database vs Cloudera Distribution for Hadoop

Compared 3% of the time

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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