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Apache Spark vs IBM Spectrum Computing comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Apache Spark
Ranking in Hadoop
1st
Average Rating
8.4
Number of Reviews
64
Ranking in other categories
Compute Service (4th), Java Frameworks (2nd)
IBM Spectrum Computing
Ranking in Hadoop
7th
Average Rating
8.2
Number of Reviews
8
Ranking in other categories
Cloud Management (23rd)
 

Mindshare comparison

As of November 2024, in the Hadoop category, the mindshare of Apache Spark is 18.2%, down from 21.9% compared to the previous year. The mindshare of IBM Spectrum Computing is 2.0%, down from 2.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop
 

Featured Reviews

SurjitChoudhury - PeerSpot reviewer
Feb 20, 2024
Offers batch processing of data and in-memory processing in Spark greatly enhances performance
Spark supports real-time data processing through Spark Streaming. It allows for batch processing of data. If you have immediate data, like chat information, that needs to be processed in real-time, Spark Streaming is used. For data that can be evaluated later, batch processing with Apache Spark is suitable. Mostly, batch processing is utilized in our organization, but for streaming data processing, tools like Kafka are often integrated. In-memory processing in Spark greatly enhances performance, making it a hundred times faster than the previous MapReduce methods. This improvement is achieved through optimization techniques like caching, broadcasting, and partitioning, which help in optimizing queries for faster processing.
Avra Jyoti Ghosh - PeerSpot reviewer
Aug 25, 2022
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

"Features include machine learning, real time streaming, and data processing."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"The deployment of the product is easy."
"The product's initial setup phase was easy."
"It provides a scalable machine learning library."
"The data processing framework is good."
"The solution is very stable."
"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
"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."
"The most valuable feature is the backup capability."
"We are satisfied with the technical support, we have no issues."
"Easy to operate and use."
"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,"
"This solution is working for both VTL and tape."
"The most valuable aspect of the product is the policy driving resource management, to optimize the computing across data centers."
 

Cons

"Apache Spark's GUI and scalability could be improved."
"The setup I worked on was really complex."
"The initial setup was not easy."
"Dynamic DataFrame options are not yet available."
"It should support more programming languages."
"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"The solution needs to optimize shuffling between workers."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"Lack of sufficient documentation, particularly in Spanish."
"IBM's sales and support structure can be challenging."
"We have not been able to use deduplication."
"This solution is no longer managing tapes correctly."
"Spectrum Computing is lagging behind other products, most likely because it hasn't been shifted to the cloud."
"SMB storage and HPC is not compatible and it should be supported by IBM Spectrum Computing."
"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."
 

Pricing and Cost Advice

"We are using the free version of the solution."
"Apache Spark is an expensive solution."
"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 platform. We do not pay for its subscription."
"It is an open-source solution, it is free of charge."
"Apache Spark is an open-source tool."
"Spark is an open-source solution, so there are no licensing costs."
"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."
"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
26%
Computer Software Company
13%
Manufacturing Company
8%
Educational Organization
5%
Financial Services Firm
40%
Computer Software Company
10%
Retailer
9%
Educational Organization
6%
 

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 main concern is the overhead of Java when distributed processing is not necessary. In such cases, operations can often be done on one node, making Spark's distributed mode unnecessary. Conseque...
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

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IBM Platform Computing
 

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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: October 2024.
814,763 professionals have used our research since 2012.