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 17.5% mindshare in the category. Cloudera is ranked #7 in DMPD , with an average rating of 8.3. Additionally, 90% of Apache users are willing to recommend the solution, compared to 86% of Cloudera users who would recommend it.

Apache Spark

Read 66 Apache Spark reviews

1,404 Views
1,090 Comparison Views

90% willing to recommend

Cloudera Data Platform

Read 26 Cloudera Data Platform reviews

22 Views
11 Comparison Views

86% 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 (4th), 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

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

"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."

"It is useful for handling large amounts of data. It is very useful for scientific purposes."

"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."

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

"There's a lot of functionality."

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

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

More Apache Spark pros

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

"It is a scalable platform."

"The Hortonworks solution is so stable. It is working as a production system, without any error, without any downtime. If I have downtime, it is mostly caused by the hardware of the computers."

"Cloudera Data Platform has significantly improved our data management."

"The upgrades and patches must come from Hortonworks."

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

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

"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."

More Cloudera Data Platform pros

Cons

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

"One limitation is that not all machine learning libraries and models support it."

"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."

"They could improve the issues related to programming language for the platform."

"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 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."

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

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

More Apache Spark cons

"Security and workload management need improvement."

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

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

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

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

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

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

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

More Cloudera Data Platform cons

Pricing and Cost Advice

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

"Apache Spark is an expensive solution."

"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."

"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."

"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."

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

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

More Apache Spark pricing and cost advice

"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."

"It is priced well and it is affordable"

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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?

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...

See all answers

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...

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?

I haven't done a price analysis specifically for HDP. However, when it was first introduced as Hadoop 2.0, there were a few use cases where the price was quite high. It was particularly expensive f...

See all answers

What needs improvement with Hortonworks Data Platform?

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. These platforms offer competitive storage solu...

See all answers

Comparisons

Spring Boot vs Apache Spark

Compared 27% of the time

SAP HANA vs Apache Spark

Compared 12% of the time

AWS Batch vs Apache Spark

Compared 11% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 7% of the time

Spark SQL vs Apache Spark

Compared 7% of the time

More Apache Spark Competitors

IBM Spectrum Computing vs Cloudera Data Platform

Compared 57% of the time

HPE Ezmeral Data Fabric vs Cloudera Data Platform

Compared 43% of the time

More Cloudera Data Platform Competitors

Product Reports

Buyer's Guide

Apache Spark

April 2025

Download Apache Spark product report

Buyer's Guide

Cloudera Data Platform

April 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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