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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Azure Data Factory

    Score8 out of 10
    N/AMicrosoft's Azure Data Factory is a service built for all data integration needs and skill levels. It is designed to allow the user to easily construct ETL and ELT processes code-free within the intuitive visual environment, or write one's own code. Visually integrate data sources using more than 80 natively built and maintenance-free connectors at no added cost. Focus on data—the serverless integration service does the rest.N/A

    IBM MQ

    Score9 out of 10
    N/AIBM MQ (formerly WebSphere MQ and MQSeries) is messaging middleware.N/A
    Pricing
    Azure Data FactoryIBM MQ
    Editions & Modules
    No answers on this topic
    No answers on this topic
    Offerings
    Pricing Offerings
    Azure Data FactoryIBM MQ
    Free Trial
    NoYes
    Free/Freemium Version
    NoYes
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details——
    More Pricing Information
    Features
    Azure Data FactoryIBM MQ
    Data Source Connection
    Comparison of Data Source Connection features of Azure Data Factory and IBM MQ
    Feature
    Azure Data Factory
    8.5
    10 Ratings
    1% above category average
    IBM MQ
    -
    Ratings
    Connect to traditional data sources9.010 Ratings00 Ratings
    Connecto to Big Data and NoSQL8.110 Ratings00 Ratings
    Data Transformations
    Comparison of Data Transformations features of Azure Data Factory and IBM MQ
    Feature
    Azure Data Factory
    7.8
    10 Ratings
    4% below category average
    IBM MQ
    -
    Ratings
    Simple transformations8.610 Ratings00 Ratings
    Complex transformations7.010 Ratings00 Ratings
    Data Modeling
    Comparison of Data Modeling features of Azure Data Factory and IBM MQ
    Feature
    Azure Data Factory
    6.2
    10 Ratings
    25% below category average
    IBM MQ
    -
    Ratings
    Data model creation4.37 Ratings00 Ratings
    Metadata management5.48 Ratings00 Ratings
    Business rules and workflow5.910 Ratings00 Ratings
    Collaboration6.99 Ratings00 Ratings
    Testing and debugging6.410 Ratings00 Ratings
    Data Governance
    Comparison of Data Governance features of Azure Data Factory and IBM MQ
    Feature
    Azure Data Factory
    5.6
    10 Ratings
    36% below category average
    IBM MQ
    -
    Ratings
    Integration with data quality tools4.210 Ratings00 Ratings
    Integration with MDM tools7.09 Ratings00 Ratings
    Best Alternatives
    Azure Data FactoryIBM MQ
    Small Businesses
    Skyvia
    Score10 out of 10
    No answers on this topic
    Medium-sized Companies
    IBM InfoSphere Information Server
    Score10 out of 10
    Apache Kafka
    Score8.9 out of 10
    Enterprises
    SolarWinds Task Factory
    Score8.3 out of 10
    Apache Kafka
    Score8.9 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Azure Data FactoryIBM MQ
    Likelihood to Recommend
    7.3
    (10 ratings)
    10.0
    (48 ratings)
    Likelihood to Renew
    -
    (0 ratings)
    9.1
    (1 ratings)
    Usability
    7.6
    (3 ratings)
    9.1
    (7 ratings)
    Availability
    -
    (0 ratings)
    9.5
    (29 ratings)
    Support Rating
    7.0
    (1 ratings)
    9.1
    (27 ratings)
    User Testimonials
    Azure Data FactoryIBM MQ
    Likelihood to Recommend
    Microsoft
    Best scenario is for ETL process. The flexibility and connectivity is outstanding. For our environment, SAP data connectivity with Azure Data Factory offers very limited features compared to SAP Data Sphere. Due to the limited modelling capacity of the tool, we use Databricks for data modelling and cleaning. Usage of multiple tools could have been avoided if adf has modelling capabilities.
    Incentivized
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    IBM
    In the context of Internet of Things (IoT) applications, IBM MQ plays a pivotal role in managing the substantial data streams emanating from interconnected devices. Its primary function is to guarantee the dependable transmission and processing of data, catering to a diverse range of IoT use cases, including but not limited to smart city initiatives, healthcare monitoring systems, and industrial automation solutions. In the telecommunications sector, IBM MQ is employed for message routing, call detail record (CDR) processing, and network management to ensure real-time data exchange and fault tolerance. When managing the supply chain and logistics, IBM MQ is used to ensure timely and accurate communication between different entities, including suppliers, warehouses, and transportation providers. IBM MQ can be cost-prohibitive for smaller organizations due to licensing and maintenance costs. In such cases, open-source or lightweight messaging solutions may be more appropriate. For scenarios requiring extremely low-latency, real-time data exchange, and high throughput, other messaging technologies, like Apache Kafka, may be more suitable due to their specialized design for such use cases.
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    Pros
    Microsoft
    • Data Ingestion - it works very well with numerous data sources.
    • Data pipeline orchestration: It is a generic, popular tool for orchestrating data pipelines.
    • Works well in Azure ecosystem, Azure services and data platforms like Databricks.
    • It is a serverless and scalable solution for cloud data integration.
    Incentivized
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    IBM
    • The documentation is very clear,It is understandable and the support helps to configure it in the best way.
    • Server guidelines make it possible to get the most out of work management. It's broad, we can work with different operating systems, I really recommend using linux.
    • It is highly compatible with systems, brockers, applications, and data accumulation programs, it is possible to configure everything so that after the installation of programs, they can communicate with each other and then throw data to an external program that accumulates it and represents in clear details of steps to follow and make business decisions.
    Incentivized
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    Cons
    Microsoft
    • Granularity of Errors: Sometimes, Azure Data Factory provides error messages that are too generic or vague for us, making it challenging to pinpoint the exact cause of a pipeline failure. Enhanced error messages with more actionable details would greatly assist us as users in debugging their pipelines.
    • Pipeline Design UI: In my experience, the visual interface for designing pipelines, especially when dealing with complex workflows or numerous activities, can become cluttered. I think a more intuitive and scalable design interface would improve usability. In my opinion, features like zoom, better alignment tools, or grouping capabilities could make managing intricate designs more manageable.
    • Native Support: While Azure Data Factory does support incremental data loads, in my experience, the setup can be somewhat manual and complex. I think native and more straightforward support for Change Data Capture, especially from popular databases, would simplify the process of capturing and processing only the changed data, making regular data updates more efficient
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    IBM
    • There is limitation on number of svrconn connections you can have to MQ on the mainframe which has been an major issue for us. This has been an issue for us for over 4 years and still no fix although I am aware IBM have been working on a solution over the last year.
    • When upgrading to MQ V9.3 on our MQ appliances there is no fall-back option. This was the same for MQ V9.2 upgrade from MQ V9.0. For production upgrades this I believe is not acceptable.
    • AMS is not supplied as part of the standard mainframe MQ licence. You need an extra licence. IBM tell customers how important security and protecting data is yet they still want to charge for this software. The cost of MQ on the mainframe is not cheap so I would expect AMS to be part of the base product.
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    Usability
    Microsoft
    So far product has performed as expected. We were noticing some performance issues, but they were largely Synapse related. This has led to a shift from Synapse to Databricks. Overall this has delayed our analytic platform. Once databricks becomes fully operational, Azure Data Factory will be critical to our environment and future success.
    Incentivized
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    IBM
    I give it a nine because it has significantly improved my team's data reliability and operational efficiency. Its great security features give us peace of mind, knowing our sensitive data is well protected. While the setup might initially be complex, I believe the long-term benefits far outweigh this hurdle.
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    Reliability and Availability
    Microsoft
    No answers on this topic
    IBM
    The messages are delivered instantly with this software and it integrates with our technology stack, in terms of availability we only had one failure when we were doing some testing and integration with third parties, the features of this software make it always available and its deployment is easy for the company, it does not generate expenses due to failures
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    Support Rating
    Microsoft
    We have not had need to engage with Microsoft much on Azure Data Factory, but they have been responsive and helpful when needed. This being said, we have not had a major emergency or outage requiring their intervention. The score of seven is a representation that they have done well for now, but have not proved out their support for a significant issue
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    IBM
    There are very specific things that must be elevated to more specialized areas of support, but the common support is very agile when receiving questions or when we leave concerns in real time. I recommend the support of the program in this regard.
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    Alternatives Considered
    Microsoft
    Azure Data Factory helps us automate to schedule jobs as per customer demands to make ETL triggers when the need arises. Anyone can define the workflow with the Azure Data Factory UI designer tool and easily test the systems. It helped us automate the same workflow with programming languages like Python or automation tools like ansible. Numerous options for connectivity be it a database or storage account helps us move data transfer to the cloud or on-premise systems.
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    IBM
    We found IBM MQ very easy to get started and quick to learn by the new users with a short learning curve and seamlessly integrates with IBM products, and quick to perform self-service analytics and make informed business decisions. IBM MQ is also very straightforward in creating simple and best reports, which are very profitable and productive.
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    Return on Investment
    Microsoft
    • Facilitate better decision-making and improve business processes.
    • Optimize business process outcomes by increasing internal efficiency and operational effectiveness.
    • Boosts revenue growth while improving business process agility.
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    IBM
    • Positive- Message Reliability and Reduced downtime, increases the ROI many times.
    • Positive- Increased stability and enhanced customer experience
    • Negative- cost is very high - Both licensing and integration cost
    • Negative- Learning and training cost of IBM MQ is high as its complex to use and integrate
    Incentivized
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