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

7,872 Views
3,070 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 ...

Comparison Buyer's Guide

Download the report

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.

To learn more, read our detailed Hadoop Report (Updated: September 2026).

Buyer's Guide

Hadoop

September 2026

Download the complete report

Helped 913,806 peers since 2012

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.

Read full review

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.

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"The solution is scalable."

"The features we find most valuable are the machine learning, data learning, and Spark Analytics."

"The 2.3 version is quite stable, all of our customers use it, there are around 100,000+ users, and it runs 24/7."

"The product's initial setup phase was easy."

"We are able to solve problems, e.g., reporting on big data, that we were not able to tackle in the past."

"Spark is used for transformations from large volumes of data, and it is usefully distributed."

"It's a nice system for batch processing huge data."

"There's a lot of functionality."

More Apache Spark pros

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

"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."

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

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

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

"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."

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

Cons

"One limitation is that not all machine learning libraries and models support it."

"It should support more programming languages."

"From my perspective, the only thing that needs improvement is the interface, as it was not easily understandable."

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

"Very often in many of my experiments, the data set has had to be partitioned, and there have been issues in handling very large data sets, with most of my work done using Python machine learning libraries, requiring chunking, and speed of prediction has been an issue of concern in some experiments where we have had to shut down processes due to CPU requirements, then restart with different Apache configurations, and resourcing support is a major determinant if I were to name a constraint in terms of running machine learning experiments."

"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 is very difficult to use. It would require a data engineer."

"Dynamic DataFrame options are not yet available."

More Apache Spark cons

"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 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."

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

"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”."

Pricing and Cost Advice

"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."

"The solution is affordable and there are no additional licensing costs."

"They provide an open-source license for the on-premise version."

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

"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."

"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."

More Apache Spark pricing and cost advice

Information not available

See which vendors are best for you

Use our free recommendation engine to learn which Hadoop solutions are best for your needs.

See recommendations

913,806 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

17%

Outsourcing Company

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...

See all answers

Ask a question

Earn 20 points

Comparisons

AWS Lambda vs Apache Spark

Compared 7% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 7% 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 73% of the time

More IBM InfoSphere BigInsights [EOL] Competitors

Product Reports

Buyer's Guide

Apache Spark

September 2026

Download Apache Spark product report

Buyer's Guide

Hadoop

September 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: September 2026.

DOWNLOAD NOW

913,806 professionals have used our research since 2012.

See our list of best Hadoop vendors.

We monitor all Hadoop reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.