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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
3rd
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 (8th)
 

Mindshare comparison

As of February 2025, in the Compute Service category, the mindshare of Apache Spark is 11.3%, up from 8.5% compared to the previous year. The mindshare of Spot by NetApp is 1.0%, 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

"ETL and streaming capabilities."
"The most valuable feature of Apache Spark is its flexibility."
"The main feature that we find valuable is that it is very fast."
"The solution is scalable."
"The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."
"The most significant advantage of Spark 3.0 is its support for DataFrame UDF Pandas UDF features."
"We use it for ETL purposes as well as for implementing the full transformation pipelines."
"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."
"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

"We are building our own queries on Spark, and it can be improved in terms of query handling."
"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."
"The logging for the observability platform could be better."
"When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources."
"The product could improve the user interface and make it easier for new users."
"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 main concern is the overhead of Java when distributed processing is not necessary."
"Apache Spark provides very good performance The tuning phase is still tricky."
"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

"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."
"The product is expensive, considering the setup."
"Spark is an open-source solution, so there are no licensing costs."
"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."
"They provide an open-source license for the on-premise version."
"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."
"Apache Spark is an open-source tool."
"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."
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
34%
Computer Software Company
13%
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 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.
838,640 professionals have used our research since 2012.