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

7,446 Views
2,829 Comparison Views

90% willing to recommend

IBM InfoSphere BigInsights ...

Read 7 IBM InfoSphere BigInsights [EOL] reviews

83% willing to recommend

Apache Spark

IBM InfoSphere BigInsights ...

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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

July 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

Ranking in other categories

Hadoop (1st), Compute Service (6th), Java Frameworks (2nd)

IBM InfoSphere BigInsights ...

Average Rating

7.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Featured Reviews

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

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

"The scalability has been the most valuable aspect of the solution."

"We are using Apache Spark, for large volume interactive data analysis."

"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."

"This solution provides a clear and convenient syntax for our analytical tasks."

"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."

"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."

"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."

"Now, when we're tackling sentiment analysis using NLP technologies, we deal with unstructured data—customer chats, feedback on promotions or demos, and even media like images, audio, and video files. For processing such data, we rely on PySpark. Beneath the surface, Spark functions as a compute engine with in-memory processing capabilities, enhancing performance through features like broadcasting and caching. It's become a crucial tool, widely adopted by 90% of companies for a decade or more."

More Apache Spark pros

"It integrates with JSqsh, enabling us to submit long-running exports from the shell."

"This helped us to serve our customers better by giving real-time suggestions and proactive maintenance."

"Watson is the perfect engine for text analysis for us, but in 2014 it doesn’t support the Russian language."

"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."

"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."

"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 gives us the option of extending our analytics system."

Cons

"I would like to see integration with data science platforms to optimize the processing capability for these tasks."

"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."

"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 management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."

"The basic improvement would be to have integration with these solutions."

"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."

"Apache Spark should add some resource management improvements to the algorithms."

"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."

More Apache Spark cons

"I have found a lot of issues in Fluid Query and BigInsights Applications to move data in the enterprise version."

"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."

"Initial setup is rather complex in comparison with Cloudera."

"The UI was not interactive: Responses used to be very slow and hang up at times."

"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”."

"Unfortunately the stability of the platform was an issue."

"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)."

Pricing and Cost Advice

"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."

"It is an open-source platform. We do not pay for its subscription."

"We are using the free version of the solution."

"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."

"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."

"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."

"Apache Spark is an open-source tool."

More Apache Spark pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Construction Company

Manufacturing Company

Comms Service Provider

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	28
Midsize Enterprise	16
Large Enterprise	33

By reviewers
Company Size	Count
Small Business	3
Large Enterprise	4

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

Compared 7% of the time

Amazon EC2 vs Apache Spark

Compared 7% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 6% of the time

Apache NiFi vs Apache Spark

Compared 5% of the time

AWS Batch vs Apache Spark

Compared 5% of the time

More Apache Spark Competitors

Cloudera Distribution for Hadoop vs IBM InfoSphere BigInsights [EOL]

Compared 75% of the time

More IBM InfoSphere BigInsights [EOL] Competitors

Product Reports

Buyer's Guide

Apache Spark

August 2026

Download Apache Spark product report

Buyer's Guide

Hadoop

July 2026

Download 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

Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop. Updated: July 2026.

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