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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
64
Ranking in other categories
Hadoop (1st), Java Frameworks (2nd)
Spot by NetApp
Ranking in Compute Service
10th
Average Rating
8.0
Reviews Sentiment
7.0
Number of Reviews
2
Ranking in other categories
Cloud Management (29th), Server Virtualization Software (14th), Cloud Operations Analytics (1st), Cloud Analytics (3rd), Containers as a Service (CaaS) (6th), Cloud Cost Management (8th)
 

Mindshare comparison

As of January 2025, in the Compute Service category, the mindshare of Apache Spark is 11.4%, up from 8.2% compared to the previous year. The mindshare of Spot by NetApp is 0.9%, up from 0.2% 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

"Apache Spark provides a very high-quality implementation of distributed data processing."
"The good performance. The nice graphical management console. The long list of ML algorithms."
"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 product is useful for analytics."
"The most crucial feature for us is the streaming capability. It serves as a fundamental aspect that allows us to exert control over our operations."
"I found the solution stable. We haven't had any problems with it."
"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."
"Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly."
"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

"From my perspective, the only thing that needs improvement is the interface, as it was not easily understandable."
"There were some problems related to the product's compatibility with a few Python libraries."
"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."
"Apache Spark should add some resource management improvements to the algorithms."
"The initial setup was not easy."
"The solution must improve its performance."
"For improvement, I think the tool could make things easier for people who aren't very technical. There's a significant learning curve, and I've seen organizations give up because of it. Making it quicker or easier for non-technical people would be beneficial."
"Apache Spark lacks geospatial data."
"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 an open-source solution, it is free of charge."
"They provide an open-source license for the on-premise version."
"Spark is an open-source solution, so there are no licensing costs."
"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."
"Apache Spark is an open-source tool."
"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."
"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."
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Top Industries

By visitors reading reviews
Financial Services Firm
27%
Computer Software Company
13%
Manufacturing Company
7%
University
5%
Manufacturing Company
34%
Computer Software Company
12%
Financial Services Firm
9%
Real Estate/Law Firm
8%
 

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 main concern is the overhead of Java when distributed processing is not necessary. In such cases, operations can often be done on one node, making Spark's distributed mode unnecessary. Conseque...
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.
831,158 professionals have used our research since 2012.