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

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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.

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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

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.

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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.

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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

"Apache Spark can do large volume interactive data analysis."

"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."

"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."

"One of the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them."

"The most valuable feature of Apache Spark is its flexibility."

"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."

"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."

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

More Apache Spark pros

"Hortonworks should not be expensive at all to those looking into using it."

"The upgrades and patches must come from Hortonworks."

"Ambari Web UI: user-friendly."

"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."

"Now, using this solution, it is much cheaper to have all of the data available for searching, not in real-time, but whenever there is a pending request."

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

"It is a scalable platform."

More Cloudera Data Platform pros

Cons

"The solution needs to optimize shuffling between workers."

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

"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."

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

"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."

"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."

"Apache Spark lacks geospatial data."

"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."

More Apache Spark cons

"It would also be nice if there were less coding involved."

"Since Cloudera acquired HDP, it's been bundled with CBH and HDP. However, the biggest challenge is cloud storage integration with Azure, GCP, and AWS."

"Hive performance. If Hive performance increased, Hadoop would replace (not everywhere) traditional databases."

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

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

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

"Security and workload management need improvement."

"The initial setup may take several hours or days, depending on the challenges faced during installation. It's not always a smooth process due to potential complexities."

More Cloudera Data Platform cons

Pricing and Cost Advice

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

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

"The product is expensive, considering the setup."

"Licensing costs can vary. For instance, when purchasing a virtual machine, you're asked if you want to take advantage of the hybrid benefit or if you prefer the license costs to be included upfront by the cloud service provider, such as Azure. If you choose the hybrid benefit, it indicates you already possess a license for the operating system and wish to avoid additional charges for that specific VM in Azure. This approach allows for a reduction in licensing costs, charging only for the service and associated resources."

"Apache Spark is an expensive solution."

"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."

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

"Apache Spark is an open-source tool."

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."

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Top Industries

By visitors reading reviews

Financial Services Firm

27%

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

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