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Apache Spark vs Spot by NetApp comparison

 

Comparison Buyer's Guide

Executive Summary

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 Compute Service
4th
Average Rating
8.4
Reviews Sentiment
7.7
Number of Reviews
65
Ranking in other categories
Hadoop (1st), Java Frameworks (2nd)
Spot by NetApp
Ranking in Compute Service
9th
Average Rating
8.0
Reviews Sentiment
7.0
Number of Reviews
2
Ranking in other categories
Cloud Management (28th), Server Virtualization Software (14th), Cloud Operations Analytics (1st), Cloud Analytics (3rd), Containers as a Service (CaaS) (6th), Cloud Cost Management (7th)
 

Mindshare comparison

As of March 2025, in the Compute Service category, the mindshare of Apache Spark is 11.3%, up from 9.2% compared to the previous year. The mindshare of Spot by NetApp is 1.1%, up from 0.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Compute Service
 

Featured Reviews

Ilya Afanasyev - PeerSpot reviewer
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.
Manpreet_Singh - PeerSpot reviewer
Used to manage Kubernetes infrastructure, but it doesn't have support from OCI
Spot Ocean is deployed on the cloud in our organization. I would recommend the solution to other users. You need to have an experience with Kubernetes, or else this product is of no use. It is not difficult to learn to use Spot Ocean. Overall, I rate the solution a seven out of ten.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library."
"Apache Spark provides a very high-quality implementation of distributed data processing."
"The processing time is very much improved over the data warehouse solution that we were using."
"The scalability has been the most valuable aspect of the solution."
"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."
"Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly."
"The solution helps us to manage and scale automatically whenever there is a limit to the increase in the application workflow."
"The solution offers both block access and file access, making it a nice solution for customers."
 

Cons

"The solution’s integration with other platforms should be improved."
"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 would like to see integration with data science platforms to optimize the processing capability for these tasks."
"The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate."
"The solution needs to optimize shuffling between workers."
"The migration of data between different versions could be improved."
"Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."
"From my perspective, the only thing that needs improvement is the interface, as it was not easily understandable."
"The solution doesn't have support from OCI, and it should start working to onboard OCI."
"There are no particular areas for improvement I can identify."
 

Pricing and Cost Advice

"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."
"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
"Apache Spark is an expensive solution."
"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 open-source solution, and there is no cost involved in deploying the solution on-premises."
"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."
"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."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
27%
Computer Software Company
13%
Manufacturing Company
7%
Comms Service Provider
5%
Manufacturing Company
35%
Computer Software Company
13%
Financial Services Firm
9%
Real Estate/Law Firm
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What do you like most about Apache Spark?
We use Spark to process data from different data sources.
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...
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...
What do you like most about Spot Ocean?
The solution helps us to manage and scale automatically whenever there is a limit to the increase in the application workflow.
What needs improvement with Spot Ocean?
There are no particular areas for improvement I can identify.
What is your primary use case for Spot Ocean?
Spot by NetApp is primarily used for backup and also for Fiservware.
 

Also Known As

No data available
Spot Ocean, Spot Elastigroup, Spot Eco
 

Overview

 

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
Freshworks, Zalando, Red Spark, News, Trax, ETAS, Demandbase, BeesWa, Duolingo, intel, IBM, N26, Wix, EyeEm, moovit, SAMSUNG, News UK, ticketmaster
Find out what your peers are saying about Apache Spark vs. Spot by NetApp and other solutions. Updated: January 2025.
841,004 professionals have used our research since 2012.