Apache Spark vs Hortonworks Data Platform comparison

Apache and Cloudera are both solutions in the Hadoop category. Apache is ranked #1 with an average rating of 8.6, while Cloudera is ranked #6 with an average rating of 8.3. Apache holds a 17.8% mindshare in H, compared to Cloudera’s 3.6% mindshare. Additionally, 90% of Apache users are willing to recommend the solution, compared to 89% of Cloudera users who would recommend it.

Apache Spark

Read 65 Apache Spark reviews

1,580 Views
1,196 Comparison Views

90% willing to recommend

Hortonworks Data Platform

Read 25 Hortonworks Data Platform reviews

333 Views
191 Comparison Views

89% willing to recommend

Apache Spark

Hortonworks Data Platform

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Executive Summary

We performed a comparison between Apache Spark and Hortonworks Data Platform based on real PeerSpot user reviews.

Find out in this report how the two Hadoop solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Apache Spark vs. Hortonworks Data Platform Report (Updated: March 2025).

Buyer's Guide

Apache Spark vs. Hortonworks Data Platform

March 2025

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Helped 842,194 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

Ranking in Hadoop

1st

Average Rating

8.4

Reviews Sentiment

7.7

Number of Reviews

Ranking in other categories

Compute Service (4th), Java Frameworks (2nd)

Hortonworks Data Platform

Ranking in Hadoop

6th

Average Rating

8.0

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Open Source Databases (14th), Data Management Platforms (DMP) (9th)

Mindshare comparison

As of March 2025, in the Hadoop category, the mindshare of Apache Spark is 17.8%, down from 21.2% compared to the previous year. The mindshare of Hortonworks Data Platform is 3.6%, up from 3.0% 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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Prashant Singh

Vice President -Product Management at Paytm

A good technology with an easy setup but is at end of life

The solution is fairly simple to set up. It's not too complex or difficult. If you know the solution, it's easy. However, there is a learning curve. If you don't know anything about it, it can be more complex. You can typically deploy it within a week. We have five or six people capable of handling a deployment.

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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

"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."

"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."

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

"The deployment of the product is easy."

"There's a lot of functionality."

"The product’s most valuable features are lazy evaluation and workload distribution."

"I like Apache Spark's flexibility the most. Before, we had one server that would choke up. With the solution, we can easily add more nodes when needed. The machine learning models are also really helpful. We use them to predict energy theft and find infrastructure problems."

"I appreciate everything about the solution, not just one or two specific features. The solution is highly stable. I rate it a perfect ten. The solution is highly scalable. I rate it a perfect ten. The initial setup was straightforward. I recommend using the solution. Overall, I rate the solution a perfect ten."

More Apache Spark pros

"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."

"The product offers a fairly easy setup process."

"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."

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

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

"We use it for data science activities."

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

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

More Hortonworks Data Platform pros

Cons

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

"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."

"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."

"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."

"The logging for the observability platform could be better."

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

"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."

More Apache Spark cons

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

"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."

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

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

"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."

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

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

More Hortonworks Data Platform cons

Pricing and Cost Advice

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

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

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

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

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

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

"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."

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

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

28%

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

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Product Reports

Buyer's Guide

Apache Spark

March 2025

Download Apache Spark product report

Buyer's Guide

Hortonworks Data Platform

March 2025

Download Hortonworks Data Platform product report

Also Known As

No data available

Hortonworks, HDP

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

The Hortonworks Data Platform is acclaimed for its robust handling of big data, offering scalable solutions for data storage optimization and advanced analytics. Users benefit from its seamless processing of both streaming and batch data, and efficient maintenance of data lakes for improved governance. Key features include comprehensive security and seamless integration with existing analytics tools, significantly enhancing organizational efficiency and decision-making capabilities.

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

Mayo Clinic, Symantec, Progressive Insurance, Noble Energy, Cardinal Health, Rogers, Mercy, Neustar, TRUECar, T-Mobile

Buyer's Guide

Apache Spark vs. Hortonworks Data Platform

March 2025

Free Report: Apache Spark vs. Hortonworks Data Platform

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