Azure Data Factory vs IBM Cloud Pak for Data comparison

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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: March 2024).
765,234 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"I can do everything I want with SSIS and Azure Data Factory.""It is very modular. It works well. We've used Data Factory and then made calls to libraries outside of Data Factory to do things that it wasn't optimized to do, and it worked really well. It is obviously proprietary in regards to Microsoft created it, but it is pretty easy and direct to bring in outside capabilities into Data Factory.""This solution has provided us with an easier, and more efficient way to carry out data migration tasks.""One advantage of Azure Data Factory is that it's fast, unlike SSIS and other on-premise tools. It's also very convenient because it has multiple connectors. The availability of native connectors allows you to connect to several resources to analyze data streams.""The tool's most valuable features are its connectors. It has many out-of-the-box connectors. We use ADF for ETL processes. Our main use case involves integrating data from various databases, processing it, and loading it into the target database. ADF plays a crucial role in orchestrating these ETL workflows.""What I like best about Azure Data Factory is that it allows you to create pipelines, specifically ETL pipelines. I also like that Azure Data Factory has connectors and solves most of my company's problems.""Feature-wise, one of the most valuable ones is the data flows introduced recently in the solution.""The most valuable feature is the copy activity."

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"Its data preparation capabilities are highly valuable.""Cloud Pak's most valuable features are IBM MQ, IBM App Connect, IBM API Connect, and ISPF.""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.""The most valuable features are data virtualization and reporting.""Scalability-wise, I rate the solution a nine or ten out of ten.""DataStage allows me to connect to different data sources.""One of Cloud Pak's best features is the Watson Knowledge Catalog, which helps you implement data governance.""You can model the data there, connect the data models with the business processes and create data lineage processes."

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Cons
"Data Factory's monitorability could be better.""It's a good idea to take a Microsoft course. Because they are really helpful when you start from your journey with Data Factory.""The number of standard adaptors could be extended further.""Lacks in-built streaming data processing.""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.""It can improve from the perspective of active logging. It can provide active logging information.""The pricing model should be more transparent and available online.""There's no Oracle connector if you want to do transformation using data flow activity, so Azure Data Factory needs more connectors for data flow transformation."

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"The product is trying to be more maturity in terms of connectors. That, I believe, is an area where Cloud Pak can improve.""There is a solution that is part of IBM Cloud Pak for Data called Watson OpenScale. It is used to monitor the deployed models for the quality and fairness of the results. This is one area that needs a lot of improvement.""Cloud Pak would be improved with integration with cloud service providers like Cloudera.""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 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's user experience is an area that has room for improvement.""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 solution could have more connectors."

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Pricing and Cost Advice
  • "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."
  • "This is a cost-effective solution."
  • "The price you pay is determined by how much you use it."
  • "Understanding the pricing model for Data Factory is quite complex."
  • "I would not say that this product is overly expensive."
  • "The licensing is a pay-as-you-go model, where you pay for what you consume."
  • "Our licensing fees are approximately 15,000 ($150 USD) per month."
  • "The licensing cost is included in the Synapse."
  • More Azure Data Factory Pricing and Cost Advice →

  • "I think that this product is too expensive for smaller companies."
  • "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."
  • "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."
  • "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."
  • "It's quite expensive."
  • "The solution is expensive."
  • More IBM Cloud Pak for Data Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:AWS Glue and Azure Data factory for ELT best performance cloud services.
    Top Answer: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 and… more »
    Top Answer: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… more »
    Top Answer:DataStage allows me to connect to different data sources.
    Top Answer:The product must improve its performance. We see typical cloud-related issues in the solution. IBM can still focus more on keeping the performance up and keeping it 100% available all the time.
    Ranking
    1st
    out of 94 in Data Integration
    Views
    26,364
    Comparisons
    20,689
    Reviews
    40
    Average Words per Review
    495
    Rating
    8.0
    14th
    out of 94 in Data Integration
    Views
    4,189
    Comparisons
    2,763
    Reviews
    10
    Average Words per Review
    546
    Rating
    8.3
    Comparisons
    Also Known As
    Cloud Pak for Data
    Learn More
    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
    Qatar Development Bank, GuideWell, Skanderborg Music Festival
    Top Industries
    REVIEWERS
    Computer Software Company35%
    Insurance Company12%
    Manufacturing Company8%
    Financial Services Firm8%
    VISITORS READING REVIEWS
    Computer Software Company13%
    Financial Services Firm13%
    Manufacturing Company8%
    Healthcare Company7%
    VISITORS READING REVIEWS
    Financial Services Firm25%
    Computer Software Company11%
    Government9%
    Manufacturing Company8%
    Company Size
    REVIEWERS
    Small Business28%
    Midsize Enterprise19%
    Large Enterprise53%
    VISITORS READING REVIEWS
    Small Business18%
    Midsize Enterprise13%
    Large Enterprise70%
    REVIEWERS
    Small Business46%
    Large Enterprise54%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise7%
    Large Enterprise76%
    Buyer's Guide
    Azure Data Factory vs. IBM Cloud Pak for Data
    March 2024
    Find out what your peers are saying about Azure Data Factory vs. IBM Cloud Pak for Data and other solutions. Updated: March 2024.
    765,234 professionals have used our research since 2012.

    Azure Data Factory is ranked 1st in Data Integration with 79 reviews while IBM Cloud Pak for Data is ranked 14th in Data Integration with 11 reviews. Azure Data Factory is rated 8.0, while IBM Cloud Pak for Data is rated 8.0. The top reviewer of Azure Data Factory writes "The data factory agent is quite good but pricing needs to be more transparent". On the other hand, the top reviewer of IBM Cloud Pak for Data writes "A scalable data analytics and digital transformation tool that provides useful features and integrations". Azure Data Factory is most compared with Informatica PowerCenter, Alteryx Designer, Informatica Cloud Data Integration, Snowflake and Microsoft Azure Synapse Analytics, whereas IBM Cloud Pak for Data is most compared with IBM InfoSphere DataStage, Informatica Cloud Data Integration, Palantir Foundry, Denodo and IBM InfoSphere Information Server. See our Azure Data Factory vs. IBM Cloud Pak for Data report.

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    We monitor all Data Integration 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.