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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
Reviews Sentiment
7.7
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
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
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

"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."
"AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI."
"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."
"The most significant advantage of Spark 3.0 is its support for DataFrame UDF Pandas UDF features."
"There's a lot of functionality."
"The most valuable feature of Apache Spark is its flexibility."
"The product's deployment phase is easy."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"The most valuable feature is the backup capability."
"This solution is working for both VTL and tape."
"IBM's ability to cluster compute resources is impressive, with built-in support for scenarios like VR and active-active configurations,"
"Spectrum Computing's best features are its speed, robustness, and data processing and analysis."
"The most valuable aspect of the product is the policy driving resource management, to optimize the computing across data centers."
"Easy to operate and use."
"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."
"We are satisfied with the technical support, we have no issues."
 

Cons

"We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."
"I know there is always discussion about which language to write applications in and some people do love Scala. However, I don't like it."
"The initial setup was not easy."
"The setup I worked on was really complex."
"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."
"The solution needs to optimize shuffling between workers."
"The migration of data between different versions could be improved."
"At the initial stage, the product provides no container logs to check the activity."
"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."
"We have not been able to use deduplication."
"This solution is no longer managing tapes correctly."
"Lack of sufficient documentation, particularly in Spanish."
"IBM's sales and support structure can be challenging."
"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

"Apache Spark is an expensive solution."
"Apache Spark is an open-source tool."
"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."
"It is an open-source platform. We do not pay for its subscription."
"We are using the free version of the solution."
"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."
"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."
"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
27%
Computer Software Company
13%
Manufacturing Company
8%
University
5%
Financial Services Firm
42%
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

No data available
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.
816,406 professionals have used our research since 2012.