Apache Spark vs Cloudera Data Platform comparison

The compared Apache and Cloudera solutions aren't in the same category. Apache is ranked #1 in H , with an average rating of 8.6, and holds a 17.8% mindshare in the category. Cloudera is ranked #7 in DMPD , with an average rating of 8.0. Additionally, 90% of Apache users are willing to recommend the solution, compared to 87% of Cloudera users who would recommend it.

Apache Spark

Read 66 Apache Spark reviews

1,245 Views
966 Comparison Views

90% willing to recommend

Cloudera Data Platform

Read 27 Cloudera Data Platform reviews

37 Views
18 Comparison Views

87% willing to recommend

Apache Spark

Cloudera Data Platform

Comparison Buyer's Guide

Download the report

Executive SummaryUpdated on Apr 1, 2025

Apache Spark and Cloudera Data Platform are key players in the big data landscape, excelling in different areas. Apache Spark leads with high-performance analytics through its rapid processing capabilities, whereas Cloudera Data Platform offers more comprehensive data management and security features.

Features: Apache Spark is renowned for its fast in-memory data processing, efficiency in handling batch and stream processing, and support for multiple programming languages like Scala, Java, and Python. Cloudera Data Platform shines with integrated data management, robust security features, and a comprehensive suite of analytics tools.

Room for Improvement: Apache Spark could enhance its data governance capabilities, improve integration with data management tools, and bolster support for enterprise-level security features. Cloudera Data Platform might improve its ease of use, enhance compatibility with a broader range of tools, and offer more flexibility in data processing options to meet diverse needs.

Ease of Deployment and Customer Service: Apache Spark is easy to deploy across various environments with a straightforward setup process. However, Cloudera Data Platform offers superior customer support, providing tailored solutions ideal for enterprise needs, making it favorable for organizations needing comprehensive support in deployment.

Pricing and ROI: Apache Spark being open-source ensures low setup costs and high ROI due to its performance efficiency. In contrast, Cloudera Data Platform may have higher initial costs, but offers better integration capabilities and data governance tools, potentially providing greater ROI over time by improving productivity in large-scale operations.

To learn more, read our detailed Hadoop Report (Updated: May 2025).

Buyer's Guide

Hadoop

May 2025

Download the complete report

Helped 851,604 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

Average Rating

8.4

Reviews Sentiment

7.7

Number of Reviews

Ranking in other categories

Hadoop (1st), Compute Service (5th), Java Frameworks (2nd)

Cloudera Data Platform

Average Rating

8.0

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Cloud Master Data Management (MDM) Solutions (10th), Data Management Platforms (DMP) (7th)

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

Miodrag-Stanic

Senior Architect at a comms service provider with 1,001-5,000 employees

Distributed computing improves data processing while upgrade complexity needs addressing

There are challenges with upgrading or updating various services like Spark, Impala, and Hive on on-premise and bare metal solutions. We aim to address these issues with a Kubernetes-based platform that will simplify the task of upgrading services. We also wish to implement lakehouse capabilities with Iceberg or Delta Lake frameworks.

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

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

"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."

"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."

"The fault tolerant feature is provided."

"The solution is scalable."

"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."

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

"The product's deployment phase is easy."

More Apache Spark pros

"The data platform is pretty neat. The workflow is also really good."

"Distributed computing, secure containerization, and governance capabilities are the most valuable features."

"Integration with other tools works well for us and we successfully scaled the solution after two to three years without any issues."

"The scalability is the key reason why we are on this platform."

"The product offers a fairly easy setup process."

"Ranger for security; with Ranger we can manager user’s permissions/access controls very easily."

"We use it for data science activities."

"Ambari Web UI: user-friendly."

More Cloudera Data Platform pros

Cons

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

"I would like to see integration with data science platforms to optimize the processing capability for these tasks."

"Spark could be improved by adding support for other open-source storage layers than Delta Lake."

"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."

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

"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."

"We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data."

"For improvement, I think the tool could make things easier for people who aren't very technical. There's a significant learning curve, and I've seen organizations give up because of it. Making it quicker or easier for non-technical people would be beneficial."

More Apache Spark cons

"The cost of the solution is high and there is room for improvement."

"The version control of the software is also an issue."

"I work a lot with banking, IT and communications customers. Hortonworks must improve or must upgrade their services for these sectors."

"Deleting any service requires a lot of clean up, unlike Cloudera."

"I would like to see more support for containers such as Docker and OpenShift."

"For on-premise use, I would not recommend Cloudera Data Platform as it is expensive and complicated to upgrade."

"It's at end of life and no longer will there be improvements."

"More information could be there to simplify the process of running the product."

More Cloudera Data Platform cons

Pricing and Cost Advice

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

"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."

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

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

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

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

"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."

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

More Apache Spark pricing and cost advice

"It is priced well and it is affordable"

"Currently, we are using the product in a sandbox environment, and there is no licensing. We might choose a licensing option once we get the results."

See which vendors are best for you

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

See recommendations

851,604 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

26%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

No data available

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.

See all answers

What is your experience regarding pricing and costs for Apache Spark?

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

See all answers

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...

See all answers

What do you like most about Hortonworks Data Platform?

Distributed computing, secure containerization, and governance capabilities are the most valuable features.

See all answers

What is your experience regarding pricing and costs for Hortonworks Data Platform?

The pricing model for Cloudera Data Platform is complex and has increased significantly compared to CDH. Initially, CDH had a straightforward pricing model based on nodes, but CDP includes factors ...

See all answers

What needs improvement with Hortonworks Data Platform?

See all answers

Comparisons

Spring Boot vs Apache Spark

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

Cloudera Distribution for Hadoop vs Apache Spark

Compared 7% of the time

More Apache Spark Competitors

IBM Spectrum Computing vs Cloudera Data Platform

Compared 59% of the time

HPE Ezmeral Data Fabric vs Cloudera Data Platform

Compared 41% of the time

More Cloudera Data Platform Competitors

Product Reports

Buyer's Guide

Apache Spark

May 2025

Download Apache Spark product report

Buyer's Guide

Cloudera Data Platform

May 2025

Download Cloudera Data Platform 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 Data Platform offers a powerful fusion of Hadoop technology and user-centric tools, enabling seamless scalability and open-source flexibility. It supports large-scale data operations with tools like Ranger and Cloudera Data Science Workbench, offering efficient cluster management and containerization capabilities.

Designed to support extensive data needs, Cloudera Data Platform encompasses a comprehensive Hadoop stack, which includes HDFS, Hive, and Spark. Its integration with Ambari provides user-friendliness in management and configuration. Despite its strengths in scalability and security, Cloudera Data Platform requires enhancements in multi-tenant implementation, governance, and UI, while attribute-level encryption and better HDFS namenode support are also needed. Stability, especially regarding the Hue UI, financial costs, and disaster recovery are notable challenges. Additionally, integration with cloud storage and deployment methods could be more intuitive to enhance user experience, along with more effective support and community engagement.

What are the key features?

Comprehensive Hadoop Stack: Integrates HDFS, Hive, Spark for large-scale data operations.
User-Friendly Interface: Managed through Ambari, simplifying configuration.
Seamless Scalability: Efficiently handles growing data demands with ease.
Open-Source Flexibility: Offers a customizable platform for specific needs.
Security Tools: Includes Ranger for advanced data protection measures.
Data Science Workbench: Provides a robust platform for data modeling.
Cluster Management: Efficient deployment and governance capabilities.
Containerization Support: Facilitates modern data processing environments.

What benefits and ROI should users expect?

Data Storage Flexibility: Handles diverse data types, enhancing storage solutions.
Advanced Security: Features tailored for data protection and compliance.
Scalability: Cost-efficient management of expanding data requirements.
Operational Efficiency: Streamlined processes through effective tools.
Data Science Integration: Supports building and deploying models efficiently.
Industry Versatility: Applicable across finance, healthcare, and more.

Cloudera Data Platform is implemented extensively across industries like hospitality for data science activities, including managing historical data. Its adaptability extends to operational analytics for sectors like oil & gas, finance, and healthcare, often enhanced by Hortonworks Data Platform for data ingestion and analytics tasks.

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

Information Not Available

Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop. Updated: May 2025.

DOWNLOAD NOW

851,604 professionals have used our research since 2012.

We monitor all Hadoop reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.