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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 (3rd), Cloud Analytics (3rd), Containers as a Service (CaaS) (6th), Cloud Cost Management (7th)
 

Mindshare comparison

As of April 2025, in the Compute Service category, the mindshare of Apache Spark is 11.2%, up from 9.7% compared to the previous year. The mindshare of Spot by NetApp is 1.2%, up from 0.4% 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."
"Features include machine learning, real time streaming, and data processing."
"The processing time is very much improved over the data warehouse solution that we were using."
"Now, when we're tackling sentiment analysis using NLP technologies, we deal with unstructured data—customer chats, feedback on promotions or demos, and even media like images, audio, and video files. For processing such data, we rely on PySpark. Beneath the surface, Spark functions as a compute engine with in-memory processing capabilities, enhancing performance through features like broadcasting and caching. It's become a crucial tool, widely adopted by 90% of companies for a decade or more."
"The most significant advantage of Spark 3.0 is its support for DataFrame UDF Pandas UDF features."
"We use Spark to process data from different data sources."
"The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"The solution offers both block access and file access, making it a nice solution for customers."
"The solution helps us to manage and scale automatically whenever there is a limit to the increase in the application workflow."
 

Cons

"It should support more programming languages."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."
"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."
"The initial setup was not easy."
"Apache Spark lacks geospatial data."
"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."
"There are no particular areas for improvement I can identify."
"The solution doesn't have support from OCI, and it should start working to onboard OCI."
 

Pricing and Cost Advice

"Spark is an open-source solution, so there are no licensing costs."
"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."
"We are using the free version of the solution."
"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."
"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 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 expensive solution."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
28%
Computer Software Company
13%
Manufacturing Company
8%
Comms Service Provider
5%
Manufacturing Company
29%
Computer Software Company
15%
Financial Services Firm
11%
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.
 

Comparisons

 

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: March 2025.
844,944 professionals have used our research since 2012.