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 (5th), 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 May 2025, in the Hadoop category, the mindshare of Apache Spark is 17.8%, down from 21.4% compared to the previous year. The mindshare of Cloudera Distribution for Hadoop is 25.7%, up from 24.2% 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 tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily."

"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."

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

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

"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."

"ETL and streaming capabilities."

"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 most crucial feature for us is the streaming capability. It serves as a fundamental aspect that allows us to exert control over our operations."

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 product is completely secure."

"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."

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

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

"The most valuable feature is that I can use CDH for almost all use cases across all industries, including the financial sector, public sector, private retailers, and so on."

More Cloudera Distribution for Hadoop pros

Cons

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

"The setup I worked on was really complex."

"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."

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

"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."

"The logging for the observability platform could be better."

"Apache Spark's GUI and scalability could be improved."

"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."

More Apache Spark cons

"The pricing needs to improve."

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

"The areas of improvement depend on the scale of the project. For banking customers, security features and an essential budget for commercial licenses would be the top priority. Data regulation could be the most crucial for a project with extensive data or an extra use case."

"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 governance aspect of the solution should be improved."

"Cloudera Distribution for Hadoop has a limited feature list and a lot of costs involved."

More Cloudera Distribution for Hadoop cons

Pricing and Cost Advice

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

"Apache Spark is an expensive solution."

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

"The product is expensive, considering the setup."

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

"The tool is an open-source product. If you're using the open-source Apache Spark, no fees are involved at any time. Charges only come into play when using it with other services like Databricks."

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

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

More Apache Spark pricing and cost advice

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

"The tool is not expensive."

"The solution is expensive."

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

"Cloudera requires a license to use."

"It is an expensive product."

"The pricing must be improved."

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

More Cloudera Distribution for Hadoop pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

26%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

Financial Services Firm

24%

Computer Software Company

15%

Educational Organization

14%

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?

Apache Spark is open-source, so it doesn't incur any charges.

What needs improvement with Apache Spark?

There is complexity when it comes to understanding the whole ecosystem, especially for beginners. I find it quite complex to understand how a Spark job is initiated, the roles of driver nodes, work...

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

AWS Batch vs Apache Spark

Compared 12% of the time

SAP HANA vs Apache Spark

Compared 11% 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 31% of the time

Amazon EMR vs Cloudera Distribution for Hadoop

Compared 29% of the time

Cassandra vs Cloudera Distribution for Hadoop

Compared 5% of the time

Neo4j Graph Database vs Cloudera Distribution for Hadoop

Compared 4% of the time

Apache HBase vs Cloudera Distribution for Hadoop

Compared 4% 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