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 8.7, and holds a 13.9% mindshare in the category. Cloudera is ranked #4 in DMPD , with an average rating of 6.5, and holds a 7.6% mindshare. Additionally, 90% of Apache users are willing to recommend the solution, compared to 85% of Cloudera users who would recommend it.

Apache Spark

Read 68 Apache Spark reviews

3,530 Views
812 Comparison Views

90% willing to recommend

Cloudera Data Platform

Read 37 Cloudera Data Platform reviews

1,399 Views
350 Comparison Views

85% willing to recommend

Apache Spark

Cloudera Data Platform

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Executive SummaryUpdated on Jan 18, 2026

Apache Spark and Cloudera Data Platform compete in the data processing and management category. Cloudera Data Platform appears to have an upper hand due to its robust support for a hybrid environment and comprehensive integration across tools, which offers significant benefits for enterprises needing extensive support and streamlined operations.

Features: Apache Spark offers fast in-memory data processing with extensive support for Spark Streaming, Spark SQL, and MLlib. It is highly effective for large-scale data processing and low-latency access. Cloudera Data Platform provides strong data management with distributed storage via HDFS and security through Apache Ranger, enabling hybrid environments and real-time analytics with tools like NiFi and Spark. Its integration capabilities across processing tools enhance its value.

Room for Improvement: Apache Spark could improve real-time querying, memory management, and integration with BI tools. User interface enhancements and expansion of machine learning libraries are also needed. Cloudera Data Platform could enhance support for cutting-edge AI and simplify its interface and configuration, in addition to addressing scalability and cloud integration challenges.

Ease of Deployment and Customer Service: Apache Spark is commonly deployed across on-premises, hybrid, and cloud environments but faces complexity issues due to its open-source nature, with users heavily relying on community support. Cloudera Data Platform offers more structured deployment and excellent customer service, supported by its commercial backing, which is highly appreciated by its users.

Pricing and ROI: Apache Spark, being open source, generally incurs no direct licensing costs but may require investment in infrastructure. It is regarded as a cost-effective solution for reducing operational expenses. Cloudera Data Platform involves higher costs due to licensing but offers extensive features and support, which can justify investment for large-scale implementations. Although viewed as complex, its pricing is considered reasonable for the operational gains it provides.

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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:

ROI

Sentiment score

6.1

Apache Spark enhances machine learning, cutting operational costs by up to 50%, with efficiency reliant on resources and expertise.

Sentiment score

4.8

Organizations see varied ROI from Cloudera Data Platform, with benefits in efficiency and costs, but experiences and expectations differ.

No quotes available

For more quotes and insights, download the Apache Spark report

There are licensing costs that have been saved when we moved some of the data platforms, decommissioned them, and moved on to this platform.

reviewer2776239

Data engineer at a tech vendor with 10,001+ employees

In terms of return on investment, I see great changes in operational effectiveness measured by RTO when comparing on-premises solutions with cloud solutions.

reviewer2763942

Cloud Data Administrator at a financial services firm with 10,001+ employees

A specific example of the positive impact of Cloudera Data Platform is the clearly saved time and improved performance, which is the main result of it.

Ciro Porzio

Data Platform Specialist at Lutech

For more quotes and insights, download the Cloudera Data Platform report

Customer Service

Sentiment score

5.9

Apache Spark support feedback varies, with mixed reviews on community forums, vendor support, and documentation adequacy.

Sentiment score

6.0

Cloudera Data Platform's customer service is praised for responsiveness but experiences vary; community resources aid those without paid service.

I have received support via newsgroups or guidance on specific discussions, which is what I would expect in an open-source situation.

Devindra Weerasooriya

Data Architect at Devtech

For more quotes and insights, download the Apache Spark report

I would rate the customer support of Cloudera Data Platform ten out of ten.

Sajid Mehmood

Principal Consultant Data Analytics at a outsourcing company with 5,001-10,000 employees

I have communicated with technical support, and they are responsive and helpful.

Shan Hasan

Data Architect at ubl

Cloudera support is timely and responsive, adhering to the SLAs they provide.

reviewer2763942

Cloud Data Administrator at a financial services firm with 10,001+ employees

For more quotes and insights, download the Cloudera Data Platform report

Scalability Issues

Sentiment score

7.5

Apache Spark excels in scalability, efficiently handling large data workloads with ease, though it requires skilled infrastructure management.

Sentiment score

6.4

Cloudera Data Platform is praised for its scalability and seamless cloud integration, though some face challenges during upgrades or on-premises.

No quotes available

For more quotes and insights, download the Apache Spark report

CDP allows for easy, mostly automated scalability where I can schedule job workflows, fine-tune system resource metrics, and add nodes with just a click.

reviewer2763942

Cloud Data Administrator at a financial services firm with 10,001+ employees

They have the cloud burst feature available where if the on-premises capacity is not sufficient at a point in time, you can run that Spark job on the cloud itself.

reviewer2776239

Data engineer at a tech vendor with 10,001+ employees

The ability to scale processing capacity on demand for batch jobs without impacting other workloads, and support for a growing number of concurrent users and teams accessing the platform simultaneously are significant advantages.

reviewer2784462

Software Engineer at a tech vendor with 10,001+ employees

For more quotes and insights, download the Cloudera Data Platform report

Stability Issues

Sentiment score

7.4

Apache Spark is generally stable, trusted by companies; newer versions enhance reliability, though memory issues may arise without proper configuration.

Sentiment score

6.5

Cloudera Data Platform offers reliable performance with minor issues, requiring careful configuration, especially in complex environments to prevent downtime.

Apache Spark resolves many problems in the MapReduce solution and Hadoop, such as the inability to run effective Python or machine learning algorithms.

Omar Khaled

Data Engineer at a tech company with 10,001+ employees

Without a doubt, we have had some crashes because each situation is different, and while the prototype in my environment is stable, we do not know everything at other customer sites.

Devindra Weerasooriya

Data Architect at Devtech

For more quotes and insights, download the Apache Spark report

Sometimes the end user is not experienced or does not have all the expertise related to Cloudera specifically, making it very difficult to manage properly

T Sarwar

Data architect at SentientAI, Karachi

Sometimes a node goes down, but it automatically returns to a healthy state.

reviewer2763942

Cloud Data Administrator at a financial services firm with 10,001+ employees

Cloudera Data Platform is pretty stable in my experience; there are not any downtime or reliability issues.

reviewer2776239

Data engineer at a tech vendor with 10,001+ employees

For more quotes and insights, download the Cloudera Data Platform report

Room For Improvement

Apache Spark requires improvements in scalability, usability, documentation, memory efficiency, real-time processing, and broader language support for better performance.

Cloudera Data Platform needs usability, stability, and security improvements, enhanced AI/ML features, and better multi-tenancy and cloud integration.

Various tools like Informatica, TIBCO, or Talend offer specific aspects, licensing can be costly;

Devindra Weerasooriya

Data Architect at Devtech

For more quotes and insights, download the Apache Spark report

We aim to address these issues with a Kubernetes-based platform that will simplify the task of upgrading services.

Miodrag-Stanic

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

Cloudera Data Platform should include additional capabilities and features similar to those offered by other data management solutions like Azure and Databricks.

Shan Hasan

Data Architect at ubl

Cloudera Data Platform can be improved by addressing the feasibility of using it in the cloud; there are some complexities around the components used in cloud by Cloudera Data Platform that are not really convenient.

Dhananjay Koyani

ML Engineer - Director at a financial services firm with 10,001+ employees

For more quotes and insights, download the Cloudera Data Platform report

Setup Cost

Apache Spark is cost-effective but may incur expenses from hardware, cloud resources, or commercial support, impacting deployment costs.

Enterprise buyers find Cloudera cost-effective versus Oracle, though pricing complexity varies based on deployment size and negotiations.

No quotes available

For more quotes and insights, download the Apache Spark report

Initially, CDH had a straightforward pricing model based on nodes, but CDP includes factors like processors, cores, terabytes, and drives, making it difficult to calculate costs.

Miodrag-Stanic

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

We find Cloudera Data Platform to be cost-effective.

reviewer2763942

Cloud Data Administrator at a financial services firm with 10,001+ employees

So far, I would say that it is competitive pricing that we have received.

reviewer2776239

Data engineer at a tech vendor with 10,001+ employees

For more quotes and insights, download the Cloudera Data Platform report

Valuable Features

Apache Spark offers fast in-memory processing, scalable analytics, MLlib for machine learning, SQL support, and seamless integration with languages.

Cloudera Data Platform offers scalability, user-friendly interface, integration, cost-effective storage, security, and simplifies administration for hybrid environments.

Not all solutions can make this data fast enough to be used, except for solutions such as Apache Spark Structured Streaming.

Omar Khaled

Data Engineer at a tech company with 10,001+ employees

The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.

Devindra Weerasooriya

Data Architect at Devtech

For more quotes and insights, download the Apache Spark report

By using the Hadoop File System for distributed storage, we have 1.5 petabytes of physical storage with 500 terabytes of effective storage due to a replication factor of three.

Miodrag-Stanic

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

The Ranger integration makes it more flexible and reliable for me by allowing control over data access, specifying who can access at what level, such as table level, masking, or data layer level.

reviewer2763942

Cloud Data Administrator at a financial services firm with 10,001+ employees

What stands out the most in Cloudera Manager are SDX, which provide centralized control for governance, security, and data lineage across multiple sources.

Ciro Porzio

Data Platform Specialist at Lutech

For more quotes and insights, download the Cloudera Data Platform report

Categories and Ranking

Apache Spark

Average Rating

8.4

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

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

Cloudera Data Platform

Average Rating

7.6

Reviews Sentiment

5.5

Number of Reviews

Ranking in other categories

Cloud Master Data Management (MDM) (8th), Data Management Platforms (DMP) (4th), AI Data Analysis (12th)

Mindshare comparison

Apache Spark and Cloudera Data Platform aren’t in the same category and serve different purposes. Apache Spark is designed for Hadoop and holds a mindshare of 13.9%, down 18.2% compared to last year.
Cloudera Data Platform, on the other hand, focuses on Data Management Platforms (DMP), holds 7.6% mindshare, up 0.6% since last year.

Hadoop Market Share Distribution
Product	Market Share (%)
Apache Spark	13.9%
Cloudera Distribution for Hadoop	15.1%
HPE Data Fabric	14.9%
Other	56.1%

Hadoop

Data Management Platforms (DMP) Market Share Distribution
Product	Market Share (%)
Cloudera Data Platform	7.6%
Palantir Foundry	15.6%
Informatica Intelligent Data Management Cloud (IDMC)	10.8%
Other	66.0%

Data Management Platforms (DMP)

Featured Reviews

Devindra Weerasooriya

Data Architect at Devtech

Provides a consistent framework for building data integration and access solutions with reliable performance

The in-memory computation feature is certainly helpful for my processing tasks. It is helpful because while using structures that could be held in memory rather than stored during the period of computation, I go for the in-memory option, though there are limitations related to holding it in memory that need to be addressed, but I have a preference for in-memory computation. The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.

Read full review

T Sarwar

Data architect at SentientAI, Karachi

Has enabled efficient big data processing and querying but remains complex to manage and configure

Cloudera Data Platform should use fewer tools and remove the complexity between them. It should make it easier for the end user to change the configuration and understand it better. The UI tool for jobs in Cloudera Data Platform can be improved to provide a proper image of ETL jobs and detailed consolidated graphs to monitor Spark-based Hue jobs.

Read full review

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Top Industries

By visitors reading reviews

Financial Services Firm

25%

Computer Software Company

Manufacturing Company

Comms Service Provider

Manufacturing Company

10%

Performing Arts

10%

Financial Services Firm

Transportation Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	28
Midsize Enterprise	15
Large Enterprise	32

By reviewers
Company Size	Count
Small Business	8
Midsize Enterprise	7
Large Enterprise	26

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?

Areas for improvement are obviously ease of use considerations, though there are limitations in doing that, so while various tools like Informatica, TIBCO, or Talend offer specific aspects, licensi...

See all answers

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

The experience with pricing, setup cost, and licensing is very good.

See all answers

What needs improvement with Hortonworks Data Platform?

Areas for improvement with Cloudera Data Platform could be the initial learning curve that can be a step for teams new to big data economy systems. Platform setup and configuration require careful ...

See all answers

What is your primary use case for Hortonworks Data Platform?

Cloudera Data Platform on AWS was adopted as the core enterprise data platform, covering the full data lifecycle from ingestion to analytics and advanced use cases. Cloudera Data Platform was used ...

See all answers

Comparisons

Spring Boot vs Apache Spark

Compared 16% of the time

AWS Lambda vs Apache Spark

Compared 7% of the time

AWS Batch vs Apache Spark

Compared 6% of the time

Apache NiFi vs Apache Spark

Compared 5% of the time

SAP HANA vs Apache Spark

Compared 5% of the time

More Apache Spark Competitors

HPE Data Fabric vs Cloudera Data Platform

Compared 17% of the time

Databricks vs Cloudera Data Platform

Compared 15% of the time

Palantir Foundry vs Cloudera Data Platform

Compared 13% of the time

Amazon EMR vs Cloudera Data Platform

Compared 11% of the time

ClickHouse vs Cloudera Data Platform

Compared 4% of the time

More Cloudera Data Platform Competitors

Product Reports

Buyer's Guide

Apache Spark

January 2026

Download Apache Spark product report

Buyer's Guide

Cloudera Data Platform

January 2026

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

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