Apache Spark vs IBM InfoSphere BigInsights [EOL] comparison

Why PeerSpot?

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

8,627 Views
3,623 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

October 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

"Aggregations are very fast in our project since we started to use Spark."

"It is useful for handling large amounts of data, and it is very useful for scientific purposes."

"The most valuable feature of Apache Spark is its ease of use."

"We have built a product called NetBot where we take any form of data, such as large email data, images, videos, or transactional data, and transform unstructured textual and video data into structured transactional data to create an enterprise-wide smart data grid that is then used by downstream analytics tools."

"Spark replaced other costly products, reducing costs by thirty-eight percent."

"The product is useful for analytics."

"We have 1000x improvement in performance over other techniques."

"Apache Spark, specifically PySpark and the tools available there, have been quite helpful in my event analysis work."

More Apache Spark pros

"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."

"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."

"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."

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

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

"The thing that I have found most valuable in this solution is the BIQSQL implementation which is fully SQL ANSI compliant."

Cons

"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."

"We are building our own queries on Spark, and it can be improved in terms of query handling."

"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."

"It's not easy to install."

"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."

"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."

"The Spark solution could improve in scheduling tasks and managing dependencies."

"Apart from the restrictions that come with its in-memory implementation. It has been improved significantly up to version 3.0, which is currently in use."

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."

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

"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."

"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”."

"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

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

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

"Spark is an open-source solution, so there are no licensing costs."

"Apache Spark is an expensive solution."

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

"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

16%

Outsourcing Company

10%

Comms Service Provider

Construction Company

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...

See all answers

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

Cloudera Distribution for Hadoop vs Apache Spark

Compared 7% of the time

AWS Lambda vs Apache Spark

Compared 6% of the time

Amazon EC2 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 45% of the time

Amazon EMR vs IBM InfoSphere BigInsights [EOL]

Compared 10% of the time

IBM Spectrum Computing vs IBM InfoSphere BigInsights [EOL]

Compared 9% of the time

Datameer vs IBM InfoSphere BigInsights [EOL]

Compared 8% of the time

Cloudera Data Platform vs IBM InfoSphere BigInsights [EOL]

Compared 8% of the time

More IBM InfoSphere BigInsights [EOL] Competitors

Product Reports

Buyer's Guide

Apache Spark

October 2026

Download Apache Spark product report

Buyer's Guide

Hadoop

October 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: October 2026.

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