Azure Data Factory vs IBM Cloud Pak for Data comparison

Microsoft and IBM are both solutions in the Data Integration category. Microsoft is ranked #1 with an average rating of 8.0, while IBM is ranked #18 with an average rating of 8.3. Microsoft holds a 11.1% mindshare in DI, compared to IBM’s 1.7% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 85% of IBM users who would recommend it.

Azure Data Factory

Read 86 Azure Data Factory reviews

23,087 Views
18,331 Comparison Views

92% willing to recommend

IBM Cloud Pak for Data

Read 12 IBM Cloud Pak for Data reviews

3,491 Views
2,410 Comparison Views

85% willing to recommend

Azure Data Factory

IBM Cloud Pak for Data

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Azure Data Factory and IBM Cloud Pak for Data based on real PeerSpot user reviews.

Find out in this report how the two Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Azure Data Factory vs. IBM Cloud Pak for Data Report (Updated: October 2024).

Buyer's Guide

Azure Data Factory vs. IBM Cloud Pak for Data

October 2024

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Categories and Ranking

Azure Data Factory

Ranking in Data Integration

1st

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (3rd)

IBM Cloud Pak for Data

Ranking in Data Integration

18th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Data Virtualization (3rd)

Mindshare comparison

As of November 2024, in the Data Integration category, the mindshare of Azure Data Factory is 11.1%, down from 13.3% compared to the previous year. The mindshare of IBM Cloud Pak for Data is 1.7%, down from 1.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration

Featured Reviews

Thulani David Mngadi

Data architect at Old Mutual

Data flow feature is valuable for data transformation tasks

The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed. The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem.

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Murali B

Associate Manager at Ernst & Young

Provides IBM Watson Catalog and data pipelines, but catalog searching needs to be improved

If people are with the existing stuff, I would definitely suggest they go with IBM Cloud Pak for Data. I usually recommend the solution for the financial sector, where I worked for about ten years. I worked with IBM for almost eight years. Unless they want to migrate to a new product completely, I recommend IBM Cloud Pak for Data to explore current business. It is easy to integrate the tool with other solutions. Except for metadata queries, metadata validations, and metadata integrations, I don't see any issues with the solution. I would recommend the solution to other users if it supports their existing infrastructure. Some people don't want to put their data in the cloud because they are concerned about how the data is secured with encryption and decryption. For such cases, we have listed out all the pros and cons of the solution to suggest them to users. Overall, I rate the solution a seven out of ten.

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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

"Data Factory allows you to pull data from multiple systems, transform it according to your business needs, and load it into a data warehouse or data lake."

"ADF is another ETL tool similar to Informatica that can transform data or copy it from on-prem to the cloud or vice versa. Once we have the data, we can apply various transformations to it and schedule our pipeline according to our business needs. ADF integrates with Databricks. We can call our Databricks notebooks and schedule them via ADF."

"I like how you can create your own pipeline in your space and reuse those creations. You can collaborate with other people who want to use your code."

"The most important feature is that it can help you do the multi-threading concepts."

"The data factory agent is quite good and programming or defining the value of jobs, processes, and activities is easy."

"The best part of this product is the extraction, transformation, and load."

"The data copy template is a valuable feature."

"UI is easy to navigate and I can retrieve VTL code without knowing in-depth coding languages."

More Azure Data Factory pros

"DataStage allows me to connect to different data sources."

"You can model the data there, connect the data models with the business processes and create data lineage processes."

"The most valuable features of IBM Cloud Pak for Data are the Watson Studio, where we can initiate more groups and write code. Additionally, Watson Machine Learning is available with many other services, such as APIs which you can plug the machine learning models."

"What I found most helpful in IBM Cloud Pak for Data is containerization, which means it's easy to shift and leave in terms of moving to other clouds. That's an advantage of IBM Cloud Pak for Data."

"It is a scalable solution, and we have had no issues with its scalability in our company. I rate the solution's scalability a nine out of ten."

"The most valuable feature of IBM Cloud Pak for Data is the Modeler flows. The ability to develop models using a graphical approach and the capability to connect to various sources, as well as the data virtualization capabilities, allow me to easily access and utilize data that is dispersed across different sources."

"The most valuable features are data virtualization and reporting."

"Scalability-wise, I rate the solution a nine or ten out of ten."

More IBM Cloud Pak for Data pros

Cons

"Data Factory could be improved in terms of data transformations by adding more metadata extractions."

"Data Factory's performance during heavy data processing isn't great."

"Data Factory's cost is too high."

"Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities. This is something that is just not fully covered. Additionally, the integration could improve for other tools, such as Azure Data Catalog."

"On the UI side, they could make it a little more intuitive in terms of how to add the radius components. Somebody who has been working with tools like Informatica or DataStage gets very used to how the UI looks and feels."

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

"Sometimes I need to do some coding, and I'd like to avoid that. I'd like no-code integrations."

"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."

More Azure Data Factory cons

"The solution could have more connectors."

"The product must improve its performance."

"One thing that bugs me is how much infrastructure Cloud Pak requires for the initial deployment. It doesn't allow you to start small. The smallest permitted deployment is too big. It's a huge problem that prevents us from implementing the solution in many scenarios."

"The product is trying to be more maturity in terms of connectors. That, I believe, is an area where Cloud Pak can improve."

"One challenge I'm facing with IBM Cloud Pak for Data is native features have been decommissioned, such as XML input and output. Too many changes have been made, and my company has around one hundred thousand mappings, so my team has been putting more effort into alternative ways to do things. Another area for improvement in IBM Cloud Pak for Data is that it's more complicated to shift from on-premise to the cloud. Other vendors provide secure agents that easily connect with your existing setup. Still, with IBM Cloud Pak for Data, you have to perform connection migration steps, upgrade to the latest version, etc., which makes it more complicated, especially as my company has XML-based mappings. Still, the XML input and output capabilities of IBM Cloud Pak for Data have been discontinued, so I'd like IBM to bring that back."

"The interface could improve because sometimes it becomes slow. Sometimes there is a delay between clicks when using the software, which can make the development process slow. It can take a few seconds to complete one action, and then a few more seconds to do the next one."

"Cloud Pak would be improved with integration with cloud service providers like Cloudera."

"The solution's user experience is an area that has room for improvement."

More IBM Cloud Pak for Data cons

Pricing and Cost Advice

"The pricing is a bit on the higher end."

"The licensing model for Azure Data Factory is good because you won't have to overpay. Pricing-wise, the solution is a five out of ten. It was not expensive, and it was not cheap."

"ADF is cheaper compared to AWS."

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

"The licensing is a pay-as-you-go model, where you pay for what you consume."

"Pricing is comparable, it's somewhere in the middle."

"Data Factory is affordable."

"The solution's fees are based on a pay-per-minute use plus the amount of data required to process."

More Azure Data Factory pricing and cost advice

"Cloud Pak's cost is a little high."

"IBM Cloud Pak for Data is expensive. If we include the training time and the machine learning, it's expensive. The cost of the execution is more reasonable."

"I don't have the exact licensing cost for IBM Cloud Pak for Data, as my company is still finalizing requirements, including monthly, yearly, and three-year licensing fees. Still, on a scale of one to five, I'd rate it a three because, compared to other vendors, it's more complicated."

"The solution is expensive."

"It's quite expensive."

"I think that this product is too expensive for smaller companies."

"The solution's pricing is competitive with that of other vendors."

"For the licensing of the solution, there is a yearly payment that needs to be made. Also, since it is expensive, cost-wise, I rate the solution an eight or nine out of ten."

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816,406 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

13%

Computer Software Company

12%

Manufacturing Company

Healthcare Company

Financial Services Firm

28%

Computer Software Company

11%

Manufacturing Company

Government

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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 do you like most about IBM Cloud Pak for Data?

DataStage allows me to connect to different data sources.

See all answers

What is your experience regarding pricing and costs for IBM Cloud Pak for Data?

The solution's pricing is competitive with that of other vendors. The pricing also depends on the number of users.

See all answers

What needs improvement with IBM Cloud Pak for Data?

Previously, we used to extract the information in the DSX and the XML formats. IBM Cloud Pak for Data exports information mostly on the ISX, which is an encrypted format. The only challenge with th...

See all answers

Comparisons

Informatica PowerCenter vs Azure Data Factory

Compared 14% of the time

Snowflake vs Azure Data Factory

Compared 11% of the time

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

Compared 8% of the time

Alteryx Designer vs Azure Data Factory

Compared 7% of the time

IBM InfoSphere DataStage vs Azure Data Factory

Compared 4% of the time

More Azure Data Factory Competitors

IBM InfoSphere DataStage vs IBM Cloud Pak for Data

Compared 42% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs IBM Cloud Pak for Data

Compared 19% of the time

Denodo vs IBM Cloud Pak for Data

Compared 8% of the time

Palantir Foundry vs IBM Cloud Pak for Data

Compared 5% of the time

Informatica PowerCenter vs IBM Cloud Pak for Data

Compared 3% of the time

More IBM Cloud Pak for Data Competitors

Product Reports

Buyer's Guide

Azure Data Factory

November 2024

Download Azure Data Factory product report

Buyer's Guide

IBM Cloud Pak for Data

November 2024

Download IBM Cloud Pak for Data product report

Also Known As

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Cloud Pak for Data

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Microsoft

IBM

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.

IBM Cloud Pak® for Data is a fully-integrated data and AI platform that modernizes how businesses collect, organize and analyze data to infuse AI throughout their organizations. Cloud-native by design, the platform unifies market-leading services spanning the entire analytics lifecycle. From data management, DataOps, governance, business analytics and automated AI, IBM Cloud Pak for Data helps eliminate the need for costly, and often competing, point solutions while providing the information architecture you need to implement AI successfully.

Building on the streamlined hybrid-cloud foundation of Red Hat® OpenShift®, IBM Cloud Pak for Data takes advantage of the underlying resource and infrastructure optimization and management. The solution fully supports multicloud environments such as Amazon Web Services (AWS), Azure, Google Cloud, IBM Cloud™ and private cloud deployments. Find out how IBM Cloud Pak for Data can lower your total cost of ownership and accelerate innovation.

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

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Buyer's Guide

Azure Data Factory vs. IBM Cloud Pak for Data

October 2024

Free Report: Azure Data Factory vs. IBM Cloud Pak for Data

Find out what your peers are saying about Azure Data Factory vs. IBM Cloud Pak for Data and other solutions. Updated: October 2024.

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