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 #11 with an average rating of 7.0. Apache holds a 11.5% mindshare in CS, compared to Spot’s 1.5% 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

4,837 Views
1,209 Comparison Views

90% willing to recommend

Spot

Read 2 Spot reviews

1,057 Views
116 Comparison Views

100% willing to recommend

Apache Spark

Spot

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Executive SummaryUpdated on Jul 13, 2025

Apache Spark and Spot compete in data processing solutions. Apache Spark holds an edge in performance efficiency and speed, while Spot is notable for its analytics capabilities and scalability.

Features: Apache Spark is designed for handling large-scale data with in-memory computation for swift analytics and machine learning. Spot offers robust features for extensive data analysis, enhanced scalability, and integration support, ideal for complex environments. Users often prefer Spot for its versatility in integration, whereas Apache Spark is preferred for speed and computing efficiency.

Ease of Deployment and Customer Service: Apache Spark deployment can be complex, needing expertise and resources, but has strong community support and documentation. Spot provides a streamlined deployment process with intuitive configuration and dedicated customer service for quicker adoption and ongoing support.

Pricing and ROI: Apache Spark usually has lower initial setup costs, offering favorable ROI due to its open-source nature and performance efficiency. Spot may have higher upfront costs, but its superior features and integration capabilities can provide robust long-term ROI. While Apache Spark is attractive for cost-efficiency, Spot’s comprehensive features and support might provide stronger ROI in data-intensive scenarios.

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

Buyer's Guide

Apache Spark vs. Spot

June 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

7.4

Number of Reviews

Ranking in other categories

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

Spot

Ranking in Compute Service

11th

Average Rating

8.0

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Cloud Management (32nd), Server Virtualization Software (15th), Cloud Operations Analytics (1st), Cloud Analytics (5th), Containers as a Service (CaaS) (7th), Cloud Cost Management (8th)

Mindshare comparison

As of July 2025, in the Compute Service category, the mindshare of Apache Spark is 11.5%, up from 11.1% compared to the previous year. The mindshare of Spot is 1.5%, up from 0.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Compute Service

Featured Reviews

Dunstan Matekenya

Data Scientist at a financial services firm with 10,001+ employees

Open-source solution for data processing with portability

Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly. While many choices now exist, Spark remains easy to use, particularly with Python. You can utilize familiar programming styles similar to Pandas in Python, including object-oriented programming. Another advantage is its portability. I can prototype and perform some initial tasks on my laptop using Spark without needing to be on Databricks or any cloud platform. I can transfer it to Databricks or other platforms, such as AWS. This flexibility allows me to improve processing even on my laptop. For instance, if I'm processing large amounts of data and find my laptop becoming slow, I can quickly switch to Spark. It handles small and large datasets efficiently, making it a versatile tool for various data processing needs.

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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

"Apache Spark provides a very high-quality implementation of distributed data processing."

"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."

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

"The solution is scalable."

"The solution has been very stable."

"The features we find most valuable are the machine learning, data learning, and Spark Analytics."

"There's a lot of functionality."

"The fault tolerant feature is provided."

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 using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."

"From my perspective, the only thing that needs improvement is the interface, as it was not easily understandable."

"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."

"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."

"One limitation is that not all machine learning libraries and models support it."

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

"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."

"The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate."

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

"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."

"It is an open-source solution, it is free of charge."

"Spark is an open-source solution, so there are no licensing costs."

"We are using the free version of the solution."

"Apache Spark is an open-source tool."

"The solution is affordable and there are no additional licensing costs."

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

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

12%

Manufacturing Company

Comms Service Provider

Manufacturing Company

21%

Computer Software Company

14%

Financial Services Firm

Educational Organization

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?

Apache Spark is open-source, so it doesn't incur any charges.

See all answers

What needs improvement with Apache Spark?

There is complexity when it comes to understanding the whole ecosystem, especially for beginners. I find it quite complex to understand how a Spark job is initiated, the roles of driver nodes, work...

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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 25% of the time

AWS Batch vs Apache Spark

Compared 11% of the time

SAP HANA vs Apache Spark

Compared 10% of the time

Spark SQL vs Apache Spark

Compared 7% of the time

Jakarta EE vs Apache Spark

Compared 3% of the time

More Apache Spark Competitors

ProsperOps vs Spot

Compared 19% of the time

Flexera Cloud Cost Management vs Spot

Compared 15% of the time

IBM Turbonomic vs Spot

Compared 13% of the time

Flexera Cloud Management Platform (CMP) vs Spot

Compared 12% of the time

Cloudability vs Spot

Compared 10% of the time

More Spot Competitors

Product Reports

Buyer's Guide

Apache Spark

July 2025

Download Apache Spark product report

Buyer's Guide

Cloud Analytics

June 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

June 2025

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