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 8.1, while IBM is ranked #19 with an average rating of 8.3. Microsoft holds a 11.0% 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

21,615 Views
17,232 Comparison Views

92% willing to recommend

IBM Cloud Pak for Data

Read 12 IBM Cloud Pak for Data reviews

3,325 Views
2,309 Comparison Views

85% willing to recommend

Azure Data Factory

IBM Cloud Pak for Data

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 19, 2024

Azure Data Factory and IBM Cloud Pak for Data are competing in the data integration and analytics category. Azure Data Factory has an edge due to its extensive integration features and more flexible pricing model, making it more accessible to a broader range of users.

Features: Azure Data Factory offers flexibility and integrability with over 100 built-in connectors, simplifying data integration. Key features like data flow assist in creating ETL pipelines, and Databricks integration enhances data transformation. It also boasts a drag-and-drop feature, facilitating intuitive orchestration. IBM Cloud Pak for Data offers Watson Studio and Machine Learning capabilities for data governance and AI model development. The combination of AI tools presents comprehensive data handling and analysis capabilities, enhancing its value.

Room for Improvement: Azure Data Factory could improve its integration with existing Azure services, streamline user-friendliness, and simplify its pricing model. Users request more out-of-the-box connectors, better real-time processing features, and comprehensive documentation. IBM Cloud Pak for Data needs a simplified setup process, more connectors, and enhanced performance stability. Better integration with other cloud providers and improved data curation features are also areas for development.

Ease of Deployment and Customer Service: Azure Data Factory excels in public cloud environments providing a scalable solution but often requires external expertise. Support is generally responsive but with mixed community feedback. IBM Cloud Pak for Data supports public and hybrid cloud deployments but faces challenges in starting small due to infrastructure needs. Simplified customer service could improve user experience as the setup can be complex.

Pricing and ROI: Azure Data Factory's pay-as-you-go model aligns costs with consumption, making it affordable but sometimes unpredictable. It provides value through workflow efficiency, though advanced services might increase costs. IBM Cloud Pak for Data requires substantial investment, justified by its capabilities, primarily benefiting larger enterprises. Its high cost can limit accessibility for smaller businesses compared to Azure's flexible pricing.

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

Buyer's Guide

Azure Data Factory vs. IBM Cloud Pak for Data

December 2024

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Categories and Ranking

Azure Data Factory

Ranking in Data Integration

1st

Average Rating

8.0

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (3rd)

IBM Cloud Pak for Data

Ranking in Data Integration

19th

Average Rating

8.0

Reviews Sentiment

6.6

Number of Reviews

Ranking in other categories

Data Virtualization (3rd)

Mindshare comparison

As of December 2024, in the Data Integration category, the mindshare of Azure Data Factory is 11.0%, 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

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

"Most of our customers are Microsoft shops and prefer Azure Data Factory because they have good licensing options and a trust factor with Microsoft."

"The feature I found most helpful in Azure Data Factory is the pipeline feature, including being able to connect to different sources. Azure Data Factory also has built-in security, which is another valuable feature."

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

"Data Factory's best feature is the ease of setting up pipelines for data and cloud integrations."

"An excellent tool for pipeline orchestration."

"The platform excels in data transformation with its user-friendly interface and robust monitoring capabilities, making ETL processes seamless."

"The scalability of the product is impressive."

More Azure Data Factory pros

"One of Cloud Pak's best features is the Watson Knowledge Catalog, which helps you implement data governance."

"IBM Watson Catalog and data pipelines are the most valuable features of the solution."

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

"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."

"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."

"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."

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

"Cloud Pak's most valuable features are IBM MQ, IBM App Connect, IBM API Connect, and ISPF."

More IBM Cloud Pak for Data pros

Cons

"The initial setup is not very straightforward."

"There aren't many third-party extensions or plugins available in the solution."

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

"There is room for improvement primarily in its streaming capabilities. For structured streaming and machine learning model implementation within an ETL process, it lags behind tools like Informatica."

"The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."

"DataStage is easier to learn than Data Factory because it's more visual. Data Factory has some drag-and-drop options, but it's not as intuitive as DataStage. It would be better if they added more drag-and-drop features. You can start using DataStage without knowing the code. You don't need to learn how the code works before using the solution."

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

"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."

More Azure Data Factory cons

"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 technical support could be a little better."

"The solution's catalog searching or map search needs to be improved."

"The product must improve its performance."

"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."

"The tool depends on the control plane, an OpenShift container platform utilized as an orchestration layer...So, we have communicated this issue to IBM and asked if it is feasible to adapt the solution to work on a Kubernetes platform that we support."

"The solution could have more connectors."

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

More IBM Cloud Pak for Data cons

Pricing and Cost Advice

"The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper."

"I am aware of the pricing of Azure Data Factory, but I prefer not to disclose specific details."

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

"ADF is cheaper compared to AWS."

"I rate the product price as six on a scale of one to ten, where one is low price and ten is high price."

"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."

"Understanding the pricing model for Data Factory is quite complex."

"This is a cost-effective solution."

More Azure Data Factory pricing and cost advice

"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."

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

"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."

"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."

"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."

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824,053 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

29%

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 13% of the time

Snowflake vs Azure Data Factory

Compared 10% 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 41% of the time

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

Compared 20% 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 4% of the time

More IBM Cloud Pak for Data Competitors

Product Reports

Buyer's Guide

Azure Data Factory

December 2024

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Buyer's Guide

IBM Cloud Pak for Data

December 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

December 2024

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

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