Azure Data Factory vs IBM InfoSphere Information Server comparison

Azure Data Factory vs. IBM InfoSphere Information Server

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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

4th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (5th)

IBM InfoSphere Information ...

Ranking in Data Integration

33rd

Average Rating

8.2

Reviews Sentiment

5.8

Number of Reviews

Ranking in other categories

Metadata Management (7th)

Mindshare comparison

As of June 2026, in the Data Integration category, the mindshare of Azure Data Factory is 2.3%, down from 8.1% compared to the previous year. The mindshare of IBM InfoSphere Information Server is 0.9%, up from 0.7% 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

"The most valuable feature of Azure Data Factory is the core features that help you through the whole Azure pipeline or value chain."

"Azure Data Factory is easy to use and integrates well, providing good classification to bring data from diverse parts of the data infrastructure, whether from CSV files coming from the Cisco server, MySQL, Excel machines, or local network data from remote files."

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

"Data Factory itself is great, it's pretty straightforward, you can easily add sources, join and lookup information, etc., and the ease of use is pretty good."

"Data Factory's best features include its data source connections, GUI for building data pipelines, and target loading within Azure."

"I find that the solution integrates well with cloud technologies, which we are using for different clouds like Snowflake and AWS."

"The data copy template is a valuable feature."

"The solution can scale very easily."

More Azure Data Factory pros

"IBM InfoSphere Information Server is stable."

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

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

"Deploying the solution is straightforward for me."

"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."

"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."

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

"This solution is extremely flexible and scalable."

More IBM InfoSphere Information Server pros

Cons

"When the record fails, it's tough to identify and log."

"I would like to see this time travel feature in Snowflake added to Azure Data Factory."

"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."

"The speed and performance need to be improved."

"The performance and stability are touch and go."

"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."

"When working with AWS, we have noticed that the difference between ADF and AWS is that AWS is more customer-focused. They're more responsive compared to any other company. ADF is not as good as AWS, but it should be. If AWS is ten out of ten, ADF is around eight out of ten. I think AWS is easier to understand from the GUI perspective compared to ADF."

"Understanding the pricing model for Data Factory is quite complex. It needs to be simplified, and easier to understand."

More Azure Data Factory cons

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

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

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

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

"Customer Service: It's poor."

"Their technical support needs improvement."

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

"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."

Pricing and Cost Advice

"The price you pay is determined by how much you use it."

"Data Factory is expensive."

"Azure Data Factory gives better value for the price than other solutions such as Informatica."

"ADF is cheaper compared to AWS."

"In terms of licensing costs, we pay somewhere around S14,000 USD per month. There are some additional costs. For example, we would have to subscribe to some additional computing and for elasticity, but they are minimal."

"While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products."

"Data Factory is affordable."

"My company is on a monthly subscription for Azure Data Factory, but it's more of a pay-as-you-go model where your monthly invoice depends on how many resources you use. On a scale of one to five, pricing for Azure Data Factory is a four. It's just the usage fees my company pays monthly."

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%

Computer Software Company

Manufacturing Company

Construction Company

Financial Services Firm

15%

Construction Company

12%

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.

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...

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...

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...

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.

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...

Snowflake vs Azure Data Factory

Comparisons

Compared 5% of the time

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

Compared 4% of the time

Databricks 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 10% 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 6% of the time

Informatica PowerCenter vs IBM InfoSphere Information Server

Compared 6% of the time

Oracle Data Integrator (ODI) vs IBM InfoSphere Information Server

Compared 3% of the time

More IBM InfoSphere Information Server Competitors

Product Reports

Azure Data Factory

Download Azure Data Factory product report

Data Integration

Download IBM InfoSphere Information Server product report

IBM InfoSphere Information Server 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

Azure Data Factory vs. IBM InfoSphere Information Server