Azure Data Factory vs IBM InfoSphere Information Server comparison

Microsoft and IBM are both solutions in the Data Integration category. Microsoft is ranked #5 with an average rating of 7.9, while IBM is ranked #34 with an average rating of 8.0. Microsoft holds a 2.3% mindshare in DI, compared to IBM’s 0.9% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 100% of IBM users who would recommend it.

Azure Data Factory

Read 96 Azure Data Factory reviews

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9,728 Comparison Views

92% willing to recommend

IBM InfoSphere Information ...

Read 9 IBM InfoSphere Information Server reviews

2,948 Views
2,064 Comparison Views

100% willing to recommend

Azure Data Factory

IBM InfoSphere Information ...

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Executive SummaryUpdated on Dec 19, 2024

IBM InfoSphere Information Server and Azure Data Factory compete in the data integration and management space. Azure Data Factory appears to have the upper hand due to its comprehensive features and overall value.

Features: IBM InfoSphere Information Server offers data integration capabilities, data quality tools, and metadata management. Azure Data Factory provides cloud data integration, supports a vast range of data sources, and offers scalable orchestration tools.

Room for Improvement: IBM InfoSphere Information Server could benefit from simplification in deployment, enhancing user experience, and reducing resource intensity. Azure Data Factory may enhance its integration with certain on-premise systems, improve pipeline monitoring tools, and offer more advanced data transformation capabilities.

Ease of Deployment and Customer Service: IBM InfoSphere Information Server's deployment is complex but offers strong enterprise support. Azure Data Factory provides smoother cloud-based deployment with robust Microsoft support.

Pricing and ROI: IBM InfoSphere Information Server has higher initial setup costs, impacting early-stage ROI but offering long-term value for on-prem solutions. Azure Data Factory's pay-as-you-go pricing model provides quicker ROI, aligning costs with growth.

To learn more, read our detailed Azure Data Factory vs. IBM InfoSphere Information Server Report (Updated: August 2026).

Buyer's Guide

Azure Data Factory vs. IBM InfoSphere Information Server

August 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

Azure Data Factory

Ranking in Data Integration

5th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (7th)

IBM InfoSphere Information ...

Ranking in Data Integration

34th

Average Rating

8.2

Reviews Sentiment

5.8

Number of Reviews

Ranking in other categories

Metadata Management (8th)

Mindshare comparison

As of August 2026, in the Data Integration category, the mindshare of Azure Data Factory is 2.3%, down from 7.2% compared to the previous year. The mindshare of IBM InfoSphere Information Server is 0.9%, up from 0.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration Mindshare Distribution
Product	Mindshare (%)
Azure Data Factory	2.3%
IBM InfoSphere Information Server	0.9%
Other	96.8%

Data Integration

Featured Reviews

KandaswamyMuthukrishnan

Director at a computer software company with 1,001-5,000 employees

Integrates diverse data sources and streamlines ETL processes effectively

Regarding potential areas of improvement for Azure Data Factory, there is a need for better data transformation, especially since many people are now depending on DataBricks more for connectivity and data integration. Azure Data Factory should consider how to enhance integration or filtering for more transformations, such as integrating with Spark clusters. I am satisfied with Azure Data Factory so far, but I suggest integrating some AI functionality to analyze data during the transition itself, providing insights such as null records, common records, and duplicates without running a separate pipeline or job. The monitoring tools in Azure Data Factory are helpful for optimizing data pipelines; while the current feature is adequate, they can improve by creating a live dashboard to see the online process, including how much percentage has been completed, which will be very helpful for people who are monitoring the pipeline.

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MOHAMMAD-IQBAL

Senior Data Engineer at Mohammed Mansour Alrumiah

Faced challenges with customer support and documentation but have benefited from reliable data integration over the years

As for utilizing the platform's metadata management feature, I have not worked on that feature yet, but personally, I have done that. To evaluate the effectiveness of IBM InfoSphere Information Server's data integration capabilities, if IBM is providing all the solutions we are using, then it is definitely a helpful thing. Mostly, the other thing is that it is a big area including data governance, data lineage, data management, and metadata, but every customer is not putting that much effort and money on that. They mostly migrate the data, use it, and forget it, but slowly things are changing. I am working in Saudi Arabia, so here also data governance, data management, and those kinds of things are getting attention. Regarding how scalable IBM InfoSphere Information Server is, I need to learn how to tune performance and scalability on the cloud. I am familiar with localized hardware, but on the cloud, I still have to do the work around it. In the beginning, we estimate the load and based on that, we put the hardware, but if there is continuous increase, I believe IBM also faces problems. Scalability needs to be improved because once the demand comes, you should be able to improve it, but for that, documentation on how to add hardware or resources to the software needs to be proper. I do not have much hands-on experience with that.

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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

"Azure Data Factory became more user-friendly when data-flows were introduced."

"The data flows were beneficial, allowing us to perform multiple transformations."

"Its integrability with the rest of the activities on Azure is most valuable."

"The solution handles large volumes of data very well. One of its best features is its ability to integrate data end-to-end, from pulling data from the source to accessing Databricks. This makes it quite useful for our needs."

"The user interface is very good. It makes me feel very comfortable when I am using the tool."

"It is beneficial that the solution is written with Spark as the back end."

"The two most valuable features of Azure Data Factory are that it's very scalable and that it's also highly reliable."

"Azure Data Factory is a very easy to use ETL tool for loading and transforming data from one location to another."

More Azure Data Factory pros

"Stability-wise, I rate the solution a ten out of ten."

"IBM InfoSphere Information Server is stable."

"Reduces the loading and development time for Datawarehouse ETL."

"Data connections, data partitioning, flexibility, and performance are the most valuable features."

"Deploying the solution is straightforward for me."

"Over the years of working with IBM InfoSphere Information Server, I see basically the strength of the tool, capability, and load balancing, which I see is really good."

"The initial IBM InfoSphere Information Server is straightforward and you can choose what type of installation you want, such as a customized installation, with clear-cut documentation that, if followed, works fine and the installation has not given us issues."

"The integration with different technologies is the most valuable feature."

More IBM InfoSphere Information Server pros

Cons

"There are limitations when processing more than one GD file."

"It does not appear to be as rich as other ETL tools. It has very limited capabilities."

"The performance could be better. It would be better if Azure Data Factory could handle a higher load. I have heard that it can get overloaded, and it can't handle it."

"I have not found any real shortcomings within the product."

"Data Factory has so many features that it can be a little difficult or confusing to find some settings and configurations. I'm sure there's a way to make it a little easier to navigate."

"I did not see any positive impact from Azure Data Factory overall."

"It can improve from the perspective of active logging. It can provide active logging information."

"The inability to connect local VMs and local servers into the data flow is a limitation that prevents giving Azure Data Factory a perfect score."

More Azure Data Factory cons

"Heavy use of scratch disk which sometimes leads to failure."

"Unlike other tools, IBM tools do not provide much help from the internet, so additional support should be available."

"Customer Service: It's poor."

"There are certain shortcomings in the cloud side of the solution, where improvements are required."

"This solution would benefit from the engine being made more lightweight."

"IBM InfoSphere Information Server should be more scalable. It should have the option to change the configuration to run on a single, non-multiple node, or multi-threading processing."

"Their technical support needs improvement."

"We have decided to decrease the usage of metadata management because we did not see any significant advantages."

Pricing and Cost Advice

"It seems very low initially, but as the data grows, the solution’s bills grow exponentially."

"Our licensing fees are approximately 15,000 ($150 USD) per month."

"I would rate Data Factory's pricing nine out of ten."

"Data Factory is expensive."

"Azure products generally offer competitive pricing, suitable for diverse budget considerations."

"The solution's pricing is competitive."

"The cost is based on the amount of data sets that we are ingesting."

"Pricing appears to be reasonable in my opinion."

More Azure Data Factory pricing and cost advice

"The licensing cost of IBM InfoSphere Information Server depends on how many users there are."

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Top Industries

By visitors reading reviews

Financial Services Firm

12%

Manufacturing Company

Computer Software Company

Construction Company

Financial Services Firm

16%

Construction Company

13%

Comms Service Provider

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	31
Midsize Enterprise	21
Large Enterprise	63

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	1
Large Enterprise	5

Questions from the Community

How do you select the right cloud ETL tool?

AWS Glue and Azure Data factory for ELT best performance cloud services.

See all answers

How does Azure Data Factory compare with Informatica PowerCenter?

Azure Data Factory is flexible, modular, and works well. In terms of cost, it is not too pricey. It offers the stability and reliability I am looking for, good scalability, and is easy to set up an...

See all answers

How does Azure Data Factory compare with Informatica Cloud Data Integration?

Azure Data Factory is a solid product offering many transformation functions; It has pre-load and post-load transformations, allowing users to apply transformations either in code by using Power Q...

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What needs improvement with IBM InfoSphere Information Server?

We are using the on-premises version of IBM InfoSphere Information Server, but we feel that all new development is mainly for the cloud. We receive corrections of errors, but we do not see new func...

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What is your primary use case for IBM InfoSphere Information Server?

My usual use case for IBM InfoSphere Information Server is ETL, where we take data from one source to another data warehouse solution.

See all answers

What advice do you have for others considering IBM InfoSphere Information Server?

We are about to change our platform from IBM AIX to SUSE Linux, as our whole platform is changing, so everyone should change from IBM to SUSE Linux. It would be very difficult for us to have a diff...

See all answers

Comparisons

Snowflake vs Azure Data Factory

Compared 4% of the time

Databricks vs Azure Data Factory

Compared 4% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs Azure Data Factory

Compared 4% of the time

Palantir Foundry vs Azure Data Factory

Compared 3% of the time

Oracle Data Integrator (ODI) vs Azure Data Factory

Compared 3% of the time

More Azure Data Factory Competitors

IBM InfoSphere DataStage vs IBM InfoSphere Information Server

Compared 9% of the time

Informatica PowerCenter vs IBM InfoSphere Information Server

Compared 9% of the time

Oracle GoldenGate vs IBM InfoSphere Information Server

Compared 7% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs IBM InfoSphere Information Server

Compared 7% of the time

Fivetran vs IBM InfoSphere Information Server

Compared 3% of the time

More IBM InfoSphere Information Server Competitors

Product Reports

Buyer's Guide

Azure Data Factory

August 2026

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Buyer's Guide

Data Integration

July 2026

IBM InfoSphere Information Server report

Download IBM InfoSphere Information Server product report

Also Known As

No data available

InfoSphere Information Server, IBM Information Server

Overview

Azure Data Factory efficiently manages and integrates data from various sources, enabling seamless movement and transformation across platforms. Its valuable features include seamless integration with Azure services, handling large data volumes, flexible transformation, user-friendly interface, extensive connectors, and scalability. Users have experienced improved team performance, workflow simplification, enhanced collaboration, streamlined processes, and boosted productivity.

Microsoft

IBM InfoSphere Information Server integrates seamlessly with both structured and unstructured data environments, offering advanced ETL capabilities and efficient data handling for large-scale enterprise applications.

IBM InfoSphere Information Server is designed for enterprise-level data integration with a focus on efficient ETL processes. It excels in moving data between sources and data warehouses, particularly valuable in sectors such as retail banking. Users leverage its robust Parallel Extender for improved processing efficiency and DataStage administration for comprehensive task management. However, areas like technical support and scalability require growth, especially for cloud-based deployments. While the Cloud Pak for Data enables acceleration on the cloud, the on-premises approach often remains tied to traditional hardware configurations.

What are the crucial features?

Parallel Extender: Enhances data processing efficiency.
DataStage Administration: Facilitates comprehensive management of ETL tasks.
Change Capture: Monitors and tracks data modifications effectively.
Handling Partitioned Databases: Enables efficient data management across partitions.
Metadata Management: Despite limitations, offers basic insights and tracking.

What benefits or ROI should be evaluated?

Cloud Pak for Data: Accelerates data transformation in cloud environments.
ETL Capabilities: Provides robust extraction, transformation, and loading processes.
Versatility: Handles both structured and unstructured data efficiently.
Bulk Loads Parameter Sets: Enhances loading efficiency with bulk operations.

IBM InfoSphere Information Server is widely implemented in industries that require heavy data transformation, such as retail and financial services. Its robust ETL processes are essential for moving critical data between systems, ensuring streamlined data flow and integration across various platforms.

IBM

Sample Customers

1. Adobe 2. BMW 3. Coca-Cola 4. General Electric 5. Johnson & Johnson 6. LinkedIn 7. Mastercard 8. Nestle 9. Pfizer 10. Samsung 11. Siemens 12. Toyota 13. Unilever 14. Verizon 15. Walmart 16. Accenture 17. American Express 18. AT&T 19. Bank of America 20. Cisco 21. Deloitte 22. ExxonMobil 23. Ford 24. General Motors 25. IBM 26. JPMorgan Chase 27. Microsoft (Azure Data Factory is developed by Microsoft) 28. Oracle 29. Procter & Gamble 30. Salesforce 31. Shell 32. Visa

Canadian National Railway Company, Chickasaw Nation Division of Commerce, Swedish Armed Forces, BG RCI, Janata Sahakari Bank Ltd., University of Arizona, Biogrid Australia

Buyer's Guide

Azure Data Factory vs. IBM InfoSphere Information Server

August 2026

Free Report: Azure Data Factory vs. IBM InfoSphere Information Server

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