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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 solution is scalable."
"It is useful for handling large amounts of data. It is very useful for scientific purposes."
"The solution has been very stable."
"AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI."
"The processing time is very much improved over the data warehouse solution that we were using."
"The product’s most valuable features are lazy evaluation and workload distribution."
"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."
"We use it for ETL purposes as well as for implementing the full transformation pipelines."
"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

"The migration of data between different versions could be improved."
"Apache Spark should add some resource management improvements to the algorithms."
"There were some problems related to the product's compatibility with a few Python libraries."
"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."
"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."
"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."
"The initial setup was not easy."
"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

"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."
"Spark is an open-source solution, so there are no licensing costs."
"We are using the free version of the solution."
"They provide an open-source license for the on-premise version."
"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."
"Apache Spark is an expensive solution."
"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."
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.
845,040 professionals have used our research since 2012.