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Apache Spark vs IBM Spectrum Computing 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 Hadoop
1st
Average Rating
8.4
Reviews Sentiment
7.7
Number of Reviews
65
Ranking in other categories
Compute Service (4th), Java Frameworks (2nd)
IBM Spectrum Computing
Ranking in Hadoop
7th
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
8
Ranking in other categories
Cloud Management (23rd)
 

Mindshare comparison

As of April 2025, in the Hadoop category, the mindshare of Apache Spark is 17.5%, down from 21.4% compared to the previous year. The mindshare of IBM Spectrum Computing is 2.6%, up from 2.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop
 

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.
Avra Jyoti Ghosh - PeerSpot reviewer
One of the best tools in the data management and services area
I mainly used Spectrum Computing for data management, governance, quality, and ETL activity Spectrum Computing's best features are its speed, robustness, and data processing and analysis.  Spectrum Computing is lagging behind other products, most likely because it hasn't been shifted to the…

Quotes from Members

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

Pros

"We use Spark to process data from different data sources."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"ETL and streaming capabilities."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"The solution is very stable."
"I feel the streaming is its best feature."
"Spectrum Computing's best features are its speed, robustness, and data processing and analysis."
"Easy to operate and use."
"IBM's ability to cluster compute resources is impressive, with built-in support for scenarios like VR and active-active configurations,"
"We are satisfied with the technical support, we have no issues."
"The most valuable aspect of the product is the policy driving resource management, to optimize the computing across data centers."
"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."
"This solution is working for both VTL and tape."
"The most valuable feature is the backup capability."
 

Cons

"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 solution needs to optimize shuffling between workers."
"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"From my perspective, the only thing that needs improvement is the interface, as it was not easily understandable."
"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."
"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."
"SMB storage and HPC is not compatible and it should be supported by IBM Spectrum Computing."
"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."
"We have not been able to use deduplication."
"In Pakistan, IBM's disadvantage is the lack of OEM support and presence."
"We'd like to see some AI model training for machine learning."
"This solution is no longer managing tapes correctly."
"Lack of sufficient documentation, particularly in Spanish."
 

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."
"It is an open-source solution, it is free of charge."
"Apache Spark is an open-source tool."
"The product is expensive, considering the setup."
"Apache Spark is an expensive solution."
"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."
"It is an open-source platform. We do not pay for its subscription."
"Spark is an open-source solution, so there are no licensing costs."
"This solution is expensive."
"Spectrum Computing is one of the most expensive products on the market."
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Top Industries

By visitors reading reviews
Financial Services Firm
28%
Computer Software Company
13%
Manufacturing Company
8%
Comms Service Provider
5%
Financial Services Firm
40%
Computer Software Company
9%
Retailer
7%
Real Estate/Law Firm
5%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

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 needs improvement with IBM Spectrum Computing?
IBM's sales and support structure can be challenging. To work on an IBM deal, you often need to involve multiple specialists, each knowledgeable about only part of the product, rather than having a...
 

Also Known As

No data available
IBM Platform Computing
 

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
London South Bank University, Transvalor, Infiniti Red Bull Racing, Genomic
Find out what your peers are saying about Apache Spark vs. IBM Spectrum Computing and other solutions. Updated: March 2025.
845,406 professionals have used our research since 2012.