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Apache Spark vs IBM InfoSphere BigInsights [EOL] comparison

Apache and IBM are both solutions in the Hadoop category. Additionally, 90% of Apache users are willing to recommend the solution, compared to 83% of IBM users who would recommend it.
Apache Spark
Read 69 Apache Spark reviews
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  • 2,251 Comparison Views
90% willing to recommend
 

Comparison Buyer's Guide

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Executive Summary
We performed a comparison between Apache Spark and IBM InfoSphere BigInsights [EOL] based on real PeerSpot user reviews.

Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop.

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Hadoop
May 2026
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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
Average Rating
8.4
Reviews Sentiment
6.9
Number of Reviews
69
Ranking in other categories
Hadoop (1st), Compute Service (6th), Java Frameworks (2nd)
IBM InfoSphere BigInsights ...
Average Rating
7.6
Number of Reviews
7
Ranking in other categories
No ranking in other categories
 

Featured Reviews

Devindra Weerasooriya - PeerSpot reviewer
Devindra Weerasooriya
Data Architect at Devtech
Provides a consistent framework for building data integration and access solutions with reliable performance
The in-memory computation feature is certainly helpful for my processing tasks. It is helpful because while using structures that could be held in memory rather than stored during the period of computation, I go for the in-memory option, though there are limitations related to holding it in memory that need to be addressed, but I have a preference for in-memory computation. The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.
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it_user743022 - PeerSpot reviewer
it_user743022
BigData Consultant at a tech services company with 10,001+ employees
Served our customers better by giving real-time suggestions and proactive maintenance, however the UI was not interactive
* The UI was not interactive: Responses used to be very slow and hang up at times. * The UI was not really helping to track the real-time jobs and its logs. * You can bring in a better UI for job management and health checks. * Developer API documentation needs improvement.
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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

"Powerful language."
"The fast performance is the most valuable aspect of the solution."
"The best features in Apache Spark that I appreciate are the fast database access, the data transformation, and the data exchange."
"The scalability has been the most valuable aspect of the solution."
"The most valuable feature of Apache Spark is its flexibility."
"The product’s most valuable features are lazy evaluation and workload distribution."
"The solution is scalable."
"Spark, as a tool, is easy to work with as you can work with Python, Scala, and Java."
More Apache Spark pros
"Definitely a product worth evaluating, esp if you are an IBM shop and if done on Bluemix, it gives a jump start on protoypes/POCs."
"The thing that I have found most valuable in this solution is the BIQSQL implementation which is fully SQL ANSI compliant."
"It integrates with JSqsh, enabling us to submit long-running exports from the shell."
"This is a very helpful product, with continuous improvements by IBM and a great customer service which enables easy access to valuable information for both Hadoop developers and system administrators."
"This helped us to serve our customers better by giving real-time suggestions and proactive maintenance."
"It gives us the option of extending our analytics system."
"InfoSphere Streams was the one core product from the platform in which we were using. We were building a real-time response system and we built it on InfoSphere Streams."
"Watson is the perfect engine for text analysis for us, but in 2014 it doesn’t support the Russian language."
 

Cons

"Better integration of BI tools would be a much appreciated improvement."
"Better data lineage support."
"It requires overcoming a significant learning curve due to its robust and feature-rich nature."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"The product could improve the user interface and make it easier for new users."
"At times when users do not know how to use Spark and request a lot of resources, then the underlying JVMs can crash, which is a big sense of worry."
"Apache Spark could improve the connectors that it supports."
"It should support more programming languages."
More Apache Spark cons
"Initial setup is rather complex in comparison with Cloudera."
"I have found a lot of issues in Fluid Query and BigInsights Applications to move data in the enterprise version."
"For our business customer pricing is very important motivation, so I can advise change licensing policy from “by volume in the cluster” to “number of machines in the cluster”."
"The UI was not interactive: Responses used to be very slow and hang up at times."
"I'd like to see faster execution time, especially for simple queries that don't touch on many rows and don't involve many operations (Joins, Unions, Groupbys)."
"I encountered issues with having the appropriate documentation resources, as well as getting the right stability when explored virtualized environments based on Virtualbox and HyperV software."
"Unfortunately the stability of the platform was an issue."
 

Pricing and Cost Advice

"They provide an open-source license for the on-premise version."
"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."
"Licensing costs can vary. For instance, when purchasing a virtual machine, you're asked if you want to take advantage of the hybrid benefit or if you prefer the license costs to be included upfront by the cloud service provider, such as Azure. If you choose the hybrid benefit, it indicates you already possess a license for the operating system and wish to avoid additional charges for that specific VM in Azure. This approach allows for a reduction in licensing costs, charging only for the service and associated resources."
"It is an open-source solution, it is free of charge."
"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."
"The product is expensive, considering the setup."
"Apache Spark is an open-source tool."
"We are using the free version of the solution."
More Apache Spark pricing and cost advice
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Top Industries

By visitors reading reviews
Financial Services Firm
22%
Manufacturing Company
9%
Construction Company
8%
Comms Service Provider
7%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business28
Midsize Enterprise16
Large Enterprise33
By reviewers
Company SizeCount
Small Business3
Large Enterprise4
 

Questions from the Community

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?
I find that there really lacks the technical depth to do any recommendations for future updates of Apache Spark. I used it for two years for our prototype work and testing things, but because I had...
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What is your primary use case for Apache Spark?
I attempted to use Apache Spark in one of our customer projects, but after the initial test, our customer moved to another technology and another database system. I do not have any final remarks on...
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Comparisons

AWS Lambda vs Apache Spark Logo
AWS Lambda vs Apache Spark
Compared 7% of the time
Amazon EC2 vs Apache Spark Logo
Amazon EC2 vs Apache Spark
Compared 7% of the time
Cloudera Distribution for Hadoop vs Apache Spark Logo
Cloudera Distribution for Hadoop vs Apache Spark
Compared 6% of the time
Apache NiFi vs Apache Spark Logo
Apache NiFi vs Apache Spark
Compared 5% of the time
Spring Boot vs Apache Spark Logo
Spring Boot vs Apache Spark
Compared 5% of the time
More Apache Spark Competitors
Cloudera Distribution for Hadoop vs IBM InfoSphere BigInsights [EOL] Logo
Cloudera Distribution for Hadoop vs IBM InfoSphere BigInsights [EOL]
Compared 100% of the time
More IBM InfoSphere BigInsights [EOL] Competitors
 

Product Reports

Buyer's Guide
Apache Spark
June 2026
Apache Spark reportDownload Apache Spark product report
Buyer's Guide
Hadoop
May 2026
IBM InfoSphere BigInsights [EOL] reportDownload IBM InfoSphere BigInsights [EOL] product report
 

Also Known As

No data available
InfoSphere BigInsights
 

Overview

Apache Spark is a leading open-source processing tool known for scalability and speed in managing large datasets. It supports both real-time and batch processing and is widely used for building data pipelines, machine learning applications, and analytics.

Apache Spark's strengths lie in its ability to process large data volumes efficiently through real-time and batch capabilities. With in-memory computation, it ensures fast data processing and significant performance gains. Its wide range of APIs, including those for machine learning, SQL, and analytics, make it versatile in handling complex data operations. While popular for ease of use and fault tolerance, Spark's management, debugging, and user-friendliness could benefit from improvements. Better GUIs, integration with BI tools, and enhanced monitoring are desired, alongside shuffling optimization and compatibility with more programming languages.

What are Apache Spark's key features?
  • Scalability: Efficiently manages large datasets across nodes.
  • Performance: In-memory computation for faster data processing.
  • Real-time Processing: Supports real-time analytics and data streaming.
  • APIs: Offers extensive APIs for machine learning, SQL, and analytics.
What benefits or ROI should users look for in reviews?
  • Ease of Use: Simplifies complex data tasks through intuitive operations.
  • Fault Tolerance: Ensures data reliability and continuous operations.
  • Integration Flexibility: Easily integrates with big data platforms and tools.

Organizations use Apache Spark predominantly for in-memory data processing, enabling seamless integration with big data frameworks. It's applied in security analytics, predictive modeling, and helps facilitate secure data transmissions in AI deployments. Industries leverage Spark's speed for sentiment analysis, data integration, and efficient ETL transformations.

Apache
IBM BigInsights delivers a rich set of advanced analytics capabilities that allows enterprises to analyze massive volumes of structured and unstructured data in its native format. The software combines open source Apache Hadoop with IBM innovations including sophisticated text analytics, IBM BigSheets for data exploration, IBM Big SQL for SQL access to data in Hadoop, and a range of performance, security and administrative features. The result is a cost-effective and user-friendly solution for complex, big data analytics.
IBM
 

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
Coherent Path Inc., Optibus, Delhaize America, Diyotta Inc., Ernst & Young, Teikoku Databank Ltd., NCSU, Vestas
Buyer's Guide
Hadoop
May 2026
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