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

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

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

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

"The most valuable feature of Apache Spark is its ease of use."

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

"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."

"Spark can handle small to huge data and is suitable for any size of company."

More Apache Spark pros

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

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

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

"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."

"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."

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

"This is the only solution that is possible to install on-premise."

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

More Cloudera Distribution for Hadoop pros

Cons

"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."

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

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

"At the initial stage, the product provides no container logs to check the activity."

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

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

"The product could improve the user interface and make it easier for new users."

"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."

More Apache Spark cons

"The procedure for operations could be simplified."

"The Cloudera training has deteriorated significantly."

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

"The governance aspect of the solution should be improved."

"This is a very expensive solution."

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

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

More Cloudera Distribution for Hadoop cons

Pricing and Cost Advice

"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."

"The product is expensive, considering the setup."

"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."

"I did not pay anything when using the tool on cloud services, but I had to pay on the compute side. The tool is not expensive compared with the benefits it offers. I rate the price as an eight out of ten."

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

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

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

"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."

More Apache Spark pricing and cost advice

"The solution is fairly expensive."

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

"It is an expensive product."

"The pricing must be improved."

"The tool is not expensive."

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

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

"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

27%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

Financial Services Firm

25%

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?

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

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

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 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