Apache Spark vs Spot comparison

Apache and Spot are both solutions in the Compute Service category. Apache is ranked #4 with an average rating of 8.6, while Spot is ranked #9 with an average rating of 8.0. Apache holds a 11.3% mindshare in CS, compared to Spot’s 1.2% mindshare. Additionally, 90% of Apache users are willing to recommend the solution, compared to 100% of Spot users who would recommend it.

Apache Spark

Read 66 Apache Spark reviews

1,526 Views
1,149 Comparison Views

90% willing to recommend

Spot

Read 2 Spot reviews

104 Views
61 Comparison Views

100% willing to recommend

Apache Spark

Spot

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Executive SummaryUpdated on Apr 8, 2025

Apache Spark and Spot are key players in the data analytics and processing space. While Apache Spark is known for its strong data processing capabilities, Spot has an advantage in deployment and customer service, offering a more seamless experience.

Features: Apache Spark provides high-speed data processing, a rich set of APIs for various programming languages, and robust data analytics features. Spot, however, focuses on infrastructure efficiency, ease of use, and cost optimization.

Ease of Deployment and Customer Service: Spot offers a streamlined setup process with outstanding customer service that integrates easily into existing workflows. Apache Spark's setup can be more technical, but it benefits from extensive documentation and community support, facilitating troubleshooting and learning.

Pricing and ROI: Apache Spark has a higher initial setup cost because of its complex architecture, but its analytics power translates into substantial long-term ROI for data-intensive operations. Spot features a budget-friendly initial model that optimizes resource usage, leading to faster ROI and cost savings for businesses focusing on efficiency.

To learn more, read our detailed Apache Spark vs. Spot Report (Updated: April 2025).

Buyer's Guide

Apache Spark vs. Spot

April 2025

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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

Ranking in other categories

Hadoop (1st), Java Frameworks (2nd)

Spot

Ranking in Compute Service

9th

Average Rating

8.0

Reviews Sentiment

7.0

Number of Reviews

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 May 2025, in the Compute Service category, the mindshare of Apache Spark is 11.3%, up from 10.2% compared to the previous year. The mindshare of Spot is 1.2%, up from 0.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Compute Service

Featured Reviews

Ilya Afanasyev

Senior Software Development Engineer at Yahoo!

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.

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Manpreet_Singh

Senior Principal Infrastructure Engineer at Mphasis

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.

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Quotes from Members

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

Pros

"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."

"I found the solution stable. We haven't had any problems with it."

"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."

"The product is useful for analytics."

"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."

"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."

"Spark is used for transformations from large volumes of data, and it is usefully distributed."

"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."

More Apache Spark pros

"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

"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."

"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."

"It requires overcoming a significant learning curve due to its robust and feature-rich nature."

"The solution must improve its performance."

"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."

"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."

"The initial setup was not easy."

"At the initial stage, the product provides no container logs to check the activity."

More Apache Spark cons

"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

"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."

"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 an open-source platform. We do not pay for its subscription."

"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."

"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."

"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."

"They provide an open-source license for the on-premise version."

More Apache Spark pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

Manufacturing Company

25%

Computer Software Company

15%

Financial Services Firm

12%

Real Estate/Law Firm

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.

See all answers

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...

See all answers

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...

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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.

See all answers

What needs improvement with Spot Ocean?

There are no particular areas for improvement I can identify.

See all answers

What is your primary use case for Spot Ocean?

Spot by NetApp is primarily used for backup and also for Fiservware.

See all answers

Comparisons

Spring Boot vs Apache Spark

Compared 26% of the time

AWS Batch vs Apache Spark

Compared 12% of the time

SAP HANA vs Apache Spark

Compared 12% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 7% of the time

AWS Fargate vs Apache Spark

Compared 2% of the time

More Apache Spark Competitors

ProsperOps vs Spot

Compared 21% of the time

Flexera Cloud Cost Management vs Spot

Compared 16% of the time

Cloudability vs Spot

Compared 13% of the time

IBM Turbonomic vs Spot

Compared 13% of the time

nOps vs Spot

Compared 13% of the time

More Spot Competitors

Product Reports

Buyer's Guide

Apache Spark

April 2025

Download Apache Spark product report

Buyer's Guide

Cloud Analytics

April 2025

Download Spot product report

Also Known As

No data available

Spot Ocean, Spot Elastigroup, Spot Eco

Overview

Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory

Apache

Spot provides dynamic workload management for cloud environments, offering cost optimization and enhanced performance. It stands out with its unique approach to managing resources efficiently.

Spot is designed to enhance cloud resource utilization and cost-effectiveness through intelligent workload management. With real-time analysis, Spot determines and deploys the most efficient resources, ensuring optimal performance for applications. Businesses benefit from reduced cloud expenses and increased operational efficiency, making it an essential tool for managing cloud infrastructure effectively.

What are the key features of Spot?

Automated Scaling: Automatically adjusts cloud resources based on demand and usage patterns.
Real-Time Monitoring: Provides continuous oversight to ensure optimal performance and operational efficiency.
Cost Optimization: Reduces expenses by utilizing spot instances and leveraging cost-effective cloud resources.
Seamless Integration: Easily integrates with existing cloud services and infrastructures.

What benefits should users look for in Spot?

Cost Reduction: Users can expect to see a significant decrease in cloud expenses.
Improved Efficiency: Enhancements in operational processes and workload management.
Resource Optimization: Better allocation and usage of cloud resources.
Scalability: Capability to handle increasing amounts of work effectively.

In finance, Spot ensures cost-effective cloud computing for trading platforms, while in e-commerce, it dynamically manages back-end processes. In the entertainment industry, Spot optimizes media streaming by deploying resources when user demand spikes. Each industry leverages Spot to maximize performance and minimize operational costs, demonstrating its versatility and reliability across sectors.

Spot

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

Buyer's Guide

Apache Spark vs. Spot

April 2025

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