Informatica Intelligent Data Management Cloud (IDMC) and Azure Data Factory are significant players in the data management and integration market. Despite IDMC's flexibility and comprehensive toolset, Azure Data Factory holds an advantage due to its seamless integration with the Azure ecosystem, benefiting organizations invested in Microsoft technologies.
Features: IDMC is notable for its robust integration capabilities, a flexible data model, and tools supporting data quality, master data management, and governance. It excels at managing complex data workflows and offers functionalities such as data lineage, matching, and merging. Azure Data Factory is recognized for its intuitive ETL processes and strong integration with Azure services, providing efficient data flow management.
Room for Improvement: IDMC could benefit from enhancements in its user interface, more preconfigured business rules, and improved data stewardship functionalities. Additionally, better support for SAP and more coverage for new data domains and regions is needed. Azure Data Factory should improve its integration with non-Microsoft platforms like SAP, Oracle, and AWS, as well as its capabilities in big data processing and machine learning integrations. Simplified logging and monitoring features could also be upgraded to meet enterprise demands.
Ease of Deployment and Customer Service: IDMC supports on-premises, hybrid, and cloud deployments, praised for its knowledgeable customer support, although time zone delays can occur. Azure Data Factory, as a cloud-native solution, efficiently integrates with Azure services, and its customer support is satisfactory, with potential improvements in responsiveness and specialized help.
Pricing and ROI: IDMC's pricing reflects its extensive features and capabilities, leading to complex licensing and higher costs, yet offering substantial ROI through enhanced data management and quality. Azure Data Factory uses a pay-as-you-go model, generally viewed as more predictable and affordable, especially for extensive Azure users. Despite potential high costs with large-scale data processing, both platforms promise significant ROI when thoughtfully deployed.
Our stakeholders and clients have expressed satisfaction with Azure Data Factory's efficiency and cost-effectiveness.
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.
Due to the tool's maturity limitations, solutions are not always simple and often require workarounds.
The response time is pretty good because we have someone in-house, who is an expert from Informatica, in our team who can help us with any sort of queries usually.
Azure Data Factory is highly scalable.
As a SaaS platform, IDMC is quite scalable and provides complete flexibility.
The solution has a high level of stability, roughly a nine out of ten.
Stability is crucial because IDMC holds business-critical data, and it needs to be available all the time for business users.
There is a problem with the integration with third-party solutions, particularly with SAP.
When using Git services, there are challenges with linked services and triggers getting overridden when moving between different environments (Dev, UAT, Prod).
Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically.
I also want to see integration with other Informatica products, such as IICS, to leverage the metadata from EDC.
The tool needs to mature in terms of category-specific attributes or dynamic attributes.
The pricing is cost-effective.
It is considered cost-effective.
It ranges from a quarter million to a couple of million a year.
I think the costs are reasonable for the kinds of features that Informatica Intelligent Data Management Cloud (IDMC) has.
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.
The interface of Azure Data Factory is very usable with a more interactive visual experience, making it easier for people who are not as experienced in coding to work with.
The platform's ability to pull in data from other platforms without the need for an additional integration tool enhances its appeal.
Informatica Intelligent Data Management Cloud (IDMC) can connect to pretty much any application, including Oracle Analytics and Power BI, and it works quite seamlessly.
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.
Informatica Intelligent Data Management Cloud (IDMC) is a robust platform used by banks, financial institutions, and health sector organizations for data management, governance, and compliance.
IDMC provides comprehensive tools for data discovery, profiling, masking, and transformation. It supports Salesforce integration, real-time data streaming, and scalable data management solutions. Health organizations manage national product catalogs while financial entities focus on data protection and regulatory compliance. Its intuitive interface, flexible features, and robust tools make it valuable across sectors, though enhancements in data integration and human workflow are being sought.
What are the most important features?
What benefits and ROI should be considered?
Banks and financial institutions use IDMC for data masking, transformation, and compliance, while health sector organizations leverage it for national product catalogs. Industry applications focus on automating business processes, centralizing data, and managing data catalogs to meet regulatory demands and ensure data protection.
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