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Azure Data Factory vs IBM InfoSphere DataStage comparison

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Featured Review
Find out what your peers are saying about Azure Data Factory vs. IBM InfoSphere DataStage and other solutions. Updated: January 2022.
563,148 professionals have used our research since 2012.
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 features are data transformations.""It has built-in connectors for more than 100 sources and onboarding data from many different sources to the cloud environment.""Its integrability with the rest of the activities on Azure is most valuable.""The best part of this product is the extraction, transformation, and load.""One of the most valuable features of Azure Data Factory is the drag-and-drop interface. This helps with workflow management because we can just drag any tables or data sources we need. Because of how easy it is to drag and drop, we can deliver things very quickly. It's more customizable through visual effect.""An excellent tool for pipeline orchestration.""The security of the agent that is installed on-premises is very good.""The solution can scale very easily."

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"Offers great flexibility.""The most valuable feature is the data integration for data warehousing.""As a data integration platform, it is easy to use. It is quite robust and useful for volumetric analysis when you have huge volumes of data. We have tested it for up to ten million rows, and it is robust enough to process ten million rows internally with its parallel processing. Its error logging mechanism is far simpler and easier to understand than other data integration tools. The newer version of InfoSphere has the data catalog and IDC lineage. They are helpful in the easy traceability of columns and tables.""It's a robust solution.""Once you have Infosphere up and running properly, it is stable.""We are mostly using transmission rules. It has a lot of functions and logic related to transmission. It is a user-friendly tool with in-built functions.""It is quite useful and powerful.""The Hierarchical Data Stage is good."

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Cons
"The pricing scheme is very complex and difficult to understand.""There is no built-in function for automatically adding notifications concerning the progress or outline of a pipeline run.""It can improve from the perspective of active logging. It can provide active logging information.""Azure Data Factory should be cheaper to move data to a data center abroad for calamities in case of disasters.""We are too early into the entire cycle for us to really comment on what problems we face. We're mostly using it for transformations, like ETL tasks. I think we are comfortable with the facts or the facts setting. But for other parts, it is too early to comment on.""Some of the optimization techniques are not scalable.""The support and the documentation can be improved.""Integration of data lineage would be a nice feature in terms of DevOps integration. It would make implementation for a company much easier. I'm not sure if that's already available or not. However, that would be a great feature to add if it isn't already there."

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"Its documentation is not up to the mark. While building APIs, we had a lot of problems trying to get around it because it is not very user-friendly. We tried to get hold of API documentation, but the documentation is not very well thought out. It should be more structured and elaborate. In terms of additional features, I would like to see good reporting on performance and performance-tuning recommendations that can be based on AI. I would also like to see better data profiling information being reported on InfoSphere.""Currently lacking virtualization ability.""The interface needs improvement.""The pricing should be lower.""It would be useful to provide support for Python, AR, and Java.""It doesn't have any big data connections. It would be good to have them because most of the systems are moving towards big data. There should also be a user-friendly way to interact with the cloud. Its loading process is very slow. It takes a lot of time for around 5 or 6 million records, and we are not able to provide real-time data to the vendors due to this delay. Its performance needs to be improved. It is also like a legacy system. It is not updated much. In higher versions, they only do small changes. We would like to have new features and new technologies.""The initial setup could be more straightforward.""The setup is extremely difficult."

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Pricing and Cost Advice
  • "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."
  • "It's not particularly expensive."
  • More Azure Data Factory Pricing and Cost Advice →

  • "The cost is too high."
  • "It's very expensive."
  • "Our internal team takes care of group licensing and cost. We don't have individual licenses. We have group licensing at the company level. Usually, IBM doesn't charge anything separately on the licensing side. For storage and everything else, we are paying around $6,000 per month, which is not very high. It includes Linux data storage, execution, and licensing. They're charging $40 for one-hour execution. Based on that, we are spending around $2,000 on the production environment and $1,000 on the lower environment for testing and development-side executions. For the mainframe, we are using the Db2 mainframe database, and we are spending around $1,000 on the Db2 mainframe database as well. All this comes out to be around $6,000. We, however, would like to have some cost reduction."
  • "The price is expensive but there are no licensing fees."
  • "It is quite expensive."
  • "It's quite expensive."
  • More IBM InfoSphere DataStage 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: 
    Comparable solutions will have common disadvantages, which is the total cost of the project. It's quite expensive.
    Top Answer: 
    From a practice point of view, solutions such as IBM InfoSphere DataStage and Oracle Data Integrator are losing ground, whereas open-source solutions are becoming increasingly powerful. For example… more »
    Ranking
    2nd
    Views
    27,271
    Comparisons
    22,407
    Reviews
    23
    Average Words per Review
    506
    Rating
    7.7
    6th
    Views
    15,130
    Comparisons
    12,447
    Reviews
    8
    Average Words per Review
    434
    Rating
    7.5
    Comparisons
    Learn More
    Overview

    Create, schedule, and manage your data integration at scale with Azure Data Factory - a hybrid data integration (ETL) service. Work with data wherever it lives, in the cloud or on-premises, with enterprise-grade security.

    IBM InfoSphere DataStage integrates data across multiple systems using a high performance parallel framework, and it supports extended metadata management and enterprise connectivity. The scalable platform provides more flexible integration of all types of data, including big data at rest (Hadoop-based) or in motion (stream-based), on distributed and mainframe platforms.
    Offer
    Learn more about Azure Data Factory
    Learn more about IBM InfoSphere DataStage
    Sample Customers
    Milliman, Pier 1 Imports, Rockwell Automation, Ziosk, Real Madrid
    Dubai Statistics Center, Etisalat Egypt
    Top Industries
    REVIEWERS
    Computer Software Company29%
    Insurance Company12%
    Healthcare Company6%
    Logistics Company6%
    VISITORS READING REVIEWS
    Computer Software Company32%
    Comms Service Provider14%
    Financial Services Firm7%
    Energy/Utilities Company6%
    REVIEWERS
    Computer Software Company63%
    Aerospace/Defense Firm13%
    Healthcare Company13%
    Financial Services Firm13%
    VISITORS READING REVIEWS
    Computer Software Company28%
    Comms Service Provider14%
    Financial Services Firm13%
    Insurance Company6%
    Company Size
    REVIEWERS
    Small Business23%
    Midsize Enterprise28%
    Large Enterprise49%
    VISITORS READING REVIEWS
    Small Business18%
    Midsize Enterprise7%
    Large Enterprise75%
    REVIEWERS
    Small Business42%
    Midsize Enterprise4%
    Large Enterprise54%
    Find out what your peers are saying about Azure Data Factory vs. IBM InfoSphere DataStage and other solutions. Updated: January 2022.
    563,148 professionals have used our research since 2012.

    Azure Data Factory is ranked 2nd in Data Integration Tools with 22 reviews while IBM InfoSphere DataStage is ranked 6th in Data Integration Tools with 8 reviews. Azure Data Factory is rated 7.6, while IBM InfoSphere DataStage is rated 7.6. The top reviewer of Azure Data Factory writes "Easy to bring in outside capabilities, flexible, and works well". On the other hand, the top reviewer of IBM InfoSphere DataStage writes "Robust, easy to use, has a simple error logging mechanism, and works very well for huge volumes of data". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Talend Open Studio, Palantir Foundry and Oracle Data Integrator (ODI), whereas IBM InfoSphere DataStage is most compared with SSIS, Talend Open Studio, Informatica PowerCenter, AWS Glue and IBM InfoSphere Information Server. See our Azure Data Factory vs. IBM InfoSphere DataStage report.

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