

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.
Our stakeholders and clients have expressed satisfaction with Azure Data Factory's efficiency and cost-effectiveness.
We have been able to drive responsible, transparent, and explainable AI workflow to operationalize AI and mitigate risk and regulatory compliance easily.
The technical support from Microsoft is rated an eight out of ten.
The technical support is responsive and helpful
The technical support for Azure Data Factory is generally acceptable.
Cloud Pak is a complicated system, and it's often difficult to find the right resource in IBM to help with specific issues.
I would rate IBM's support at about a seven or eight out of ten because we have good support coverage owing to our long association with IBM.
Customer support should be more responsive and reach and respond on time.
Azure Data Factory is highly scalable.
I have not noticed any downtime or lagging, especially when dealing with large data, so it is relatively very scalable.
The solution has a high level of stability, roughly a nine out of ten.
Incorporating more dedicated API sources to specific services like HubSpot CRM or Salesforce would be beneficial.
Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically.
There is a problem with the integration with third-party solutions, particularly with SAP.
Setting up the hybrid and multi-cloud environments is a long job and it takes time.
I would love Cloud Pak to come with a demo database that illustrates the different components of data management in a logical way, so I can see the whole picture instead of just the area I'm specializing in.
I do not know if Cognos has all the features that users are looking for since we provide it as our standard and do not maintain infrastructure for other tools.
The pricing is cost-effective.
It is considered cost-effective.
The setup cost is very expensive.
Regarding my experience with pricing, setup cost, and licensing, for a small organization, the price might be relatively high, but for huge enterprises such as ours, the price is relatively affordable.
It connects to different sources out-of-the-box, making integration much easier.
The platform excels in handling major datasets, particularly when working with Power BI for reporting purposes.
Regarding the integration feature in Azure Data Factory, the integration part is excellent; we have major source connectors, so we can integrate the data from different data sources and also perform basic transformation while transforming, which is a great feature in Azure Data Factory.
From there, I can work my way into a more granular level, applying all of that information on top of my actual data to understand what my data looks like, where it came from, and where it went wrong, managing it throughout the cycle.
The benefits of choosing IBM Cognos, in addition to saving on cost, include having institutional knowledge about maintaining this infrastructure and enough people who have developed on Cognos in the past, which creates comfort in its use.
We have been able to save approximately 80 percent of our time. We are not doing data analysis manually, so this relieves our data department of dealing with data.
| Product | Market Share (%) |
|---|---|
| Azure Data Factory | 3.2% |
| IBM Cloud Pak for Data | 1.3% |
| Other | 95.5% |


| Company Size | Count |
|---|---|
| Small Business | 31 |
| Midsize Enterprise | 19 |
| Large Enterprise | 57 |
| Company Size | Count |
|---|---|
| Small Business | 7 |
| Large Enterprise | 12 |
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.
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