IBM Spectrum Computing vs Spark SQL comparison

IBM and Apache are both solutions in the Hadoop category. IBM is ranked #6 with an average rating of 8.0, while Apache is ranked #5 with an average rating of 8.7. IBM holds a 1.7% mindshare in H, compared to Apache’s 10.4% mindshare. Additionally, 57% of IBM users are willing to recommend the solution, compared to 85% of Apache users who would recommend it.

IBM Spectrum Computing

Read 9 IBM Spectrum Computing reviews

532 Views
138 Comparison Views

57% willing to recommend

Spark SQL

Read 14 Spark SQL reviews

662 Views
596 Comparison Views

85% willing to recommend

IBM Spectrum Computing

Spark SQL

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Executive Summary

We performed a comparison between IBM Spectrum Computing and Spark SQL based on real PeerSpot user reviews.

Find out in this report how the two Hadoop solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed IBM Spectrum Computing vs. Spark SQL Report (Updated: July 2025).

Buyer's Guide

IBM Spectrum Computing vs. Spark SQL

July 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

IBM Spectrum Computing

Ranking in Hadoop

6th

Average Rating

8.2

Reviews Sentiment

5.9

Number of Reviews

Ranking in other categories

Cloud Management (27th)

Spark SQL

Ranking in Hadoop

5th

Average Rating

7.8

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of August 2025, in the Hadoop category, the mindshare of IBM Spectrum Computing is 1.7%, down from 2.4% compared to the previous year. The mindshare of Spark SQL is 10.4%, down from 11.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop

Featured Reviews

OmarIsmail1

Infrastructure Technical Specialist II at Clicks Group

Senior Technical Specialist appreciates intelligent workload management, strong support, and scalability

The best features of IBM Spectrum Computing are common across many of their storage products. The software is solid, meaning that the code is stable. They take business seriously, which is what IBM stands for - International Business Machines. They always maintain a business-oriented approach in their software development. It's not simply clicking through interfaces; in IBM software, they consider their actions, process flows, and workflows around business processes. It requires understanding IBM and their methodology, as the software operates accordingly. I have utilized IBM Spectrum Computing's intelligent workload management feature. We use Insights, which is connected to the cloud. This provides AI capabilities for analyzing the configuration, offering smart recommendations on new code, warning about bugs in current code, and suggesting configuration improvements through its advisor tool. The predictive analytics feature in IBM Spectrum Computing enables optimal software performance through Insights. However, being a storage administrator requires foundational knowledge and understanding beyond these tools. For troubleshooting, it's efficient in spotting bottlenecks, but understanding the terms and metrics is essential as it provides answers that need interpretation.

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SurjitChoudhury

Data engineer at Cocos pt

Offers the flexibility to handle large-scale data processing

My experience with the initial setup of Spark SQL was relatively smooth. Understanding the system wasn't overly difficult because the data was structured in databases, and we could use notebooks for coding in Python or Java. Configuring networks and running scripts to load data into the database were routine tasks that didn't pose significant challenges. The flexibility to use different languages for coding and the ability to process data using key-value pairs in Python made the setup adaptable. Once we received the source data, processing it in SparkSQL involved writing scripts to create dimension and fact tables, which became a standard part of our workflow. Setting up Spark SQL was reasonably quick, but sometimes we face performance issues, especially during data loading into the SQL Server data warehouse. Sequencing notebooks for efficient job runs is crucial, and managing complex tasks with multiple notebooks requires careful tracking. Exploring ways to optimize this process could be beneficial. However, once you are familiar with the database architecture and project tools, understanding and adapting to the system become more straightforward.

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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

"The most valuable aspect of the product is the policy driving resource management, to optimize the computing across data centers."

"This solution is working for both VTL and tape."

"The best features of IBM Spectrum Computing are common across many of their storage products."

"We are satisfied with the technical support, we have no issues."

"Spectrum Computing's best features are its speed, robustness, and data processing and analysis."

"IBM's ability to cluster compute resources is impressive, with built-in support for scenarios like VR and active-active configurations,"

"The most valuable feature is the backup capability."

"The comparison was challenging, but the IBM Spectrum Scale offered a balanced solution. Our engineers rated itsanalytics capabilities equally high as Pure Storage. For workload management, Spectrum Computing provided effective solutions that met our needs. Workload management is part of a complete solution that uses different tools. There were the cloud and HPC parts; within HPC, there were parts like liquid cooling, simple computing, storage, and orchestration. The orchestration team handled the workload management."

More IBM Spectrum Computing pros

"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks."

"This solution is useful to leverage within a distributed ecosystem."

"It is a stable solution."

"The stability was fine. It behaved as expected."

"Data validation and ease of use are the most valuable features."

"The speed of getting data."

"I find the Thrift connection valuable."

"Certain data sets that are very large are very difficult to process with Pandas and Python libraries. Spark SQL has helped us a lot with that."

More Spark SQL pros

Cons

"Spectrum Computing is lagging behind other products, most likely because it hasn't been shifted to the cloud."

"IBM's sales and support structure can be challenging."

"The deduplication software isn't quite up to speed with the market. While IBM has excellent compression technology, specifically on their FlashCore modules, they lag behind competitors such as NetApp in deduplication capabilities."

"In Pakistan, IBM's disadvantage is the lack of OEM support and presence."

"The deduplication software isn't quite up to speed with the market."

"SMB storage and HPC is not compatible and it should be supported by IBM Spectrum Computing."

"We'd like to see some AI model training for machine learning."

"Lack of sufficient documentation, particularly in Spanish."

More IBM Spectrum Computing cons

"It would be useful if Spark SQL integrated with some data visualization tools."

"There should be better integration with other solutions."

"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."

"In the next update, we'd like to see better performance for small points of data. It is possible but there are better tools that are faster and cheaper."

"SparkUI could have more advanced versions of the performance and the queries and all."

"In the next release, maybe the visualization of some command-line features could be added."

"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."

"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."

More Spark SQL cons

Pricing and Cost Advice

"This solution is expensive."

"Spectrum Computing is one of the most expensive products on the market."

"We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small."

"We use the open-source version, so we do not have direct support from Apache."

"The solution is open-sourced and free."

"There is no license or subscription for this solution."

"The solution is bundled with Palantir Foundry at no extra charge."

"The on-premise solution is quite expensive in terms of hardware, setting up the cluster, memory, hardware and resources. It depends on the use case, but in our case with a shared cluster which is quite large, it is quite expensive."

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Top Industries

By visitors reading reviews

No data available

Financial Services Firm

17%

University

10%

Retailer

10%

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What is your experience regarding pricing and costs for IBM Spectrum Computing?

IBM Spectrum Computing consistently offers competitive pricing. When solutioning new implementations, IBM always presents the best solution and price. In a recent comparison with Pure Storage and N...

See all answers

What needs improvement with IBM Spectrum Computing?

IBM Spectrum Computing had limitations with remote copy services between head office and disaster recovery sites. In the last year, IBM has improved the code by re-engineering it to policy-based re...

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What is your primary use case for IBM Spectrum Computing?

The typical use case for IBM Spectrum Computing is that it's an all-rounder. It can be used in various scenarios, such as the retailer I work for that has batch processing. It's on-demand when perf...

See all answers

What do you like most about Spark SQL?

Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline.

See all answers

What is your experience regarding pricing and costs for Spark SQL?

We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small.

See all answers

What needs improvement with Spark SQL?

In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL. There could be additional features that I haven't explored but the current solution for working ...

See all answers

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Compared 46% of the time

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Compared 14% of the time

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Compared 11% of the time

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Compared 11% of the time

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Compared 10% of the time

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Product Reports

Buyer's Guide

Hadoop

August 2025

Download IBM Spectrum Computing product report

Buyer's Guide

Spark SQL

July 2025

Download Spark SQL product report

Also Known As

IBM Platform Computing

No data available

Overview

IBM Spectrum Computing uses intelligent workload and policy-driven resource management to optimize resources across the data center, on premises and in the cloud. Now up to 150X faster and scalable to over 160,000 cores, IBM provides you with the latest advances in software-defined infrastructure to help you unleash the power of your distributed mission-critical high performance computing (HPC), analytics and big data applications as well as a new generation open source frameworks such as Hadoop and Spark.

IBM

Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. There are several ways to interact with Spark SQL including SQL and the Dataset API. When computing a result the same execution engine is used, independent of which API/language you are using to express the computation. This unification means that developers can easily switch back and forth between different APIs based on which provides the most natural way to express a given transformation.

Apache

Sample Customers

London South Bank University, Transvalor, Infiniti Red Bull Racing, Genomic

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions

Buyer's Guide

IBM Spectrum Computing vs. Spark SQL

July 2025

Free Report: IBM Spectrum Computing vs. Spark SQL

Find out what your peers are saying about IBM Spectrum Computing vs. Spark SQL and other solutions. Updated: July 2025.

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