

Denodo and IBM Cloud Pak for Data are two prominent data integration platforms. Denodo generally receives more favorable feedback on its ease of deployment and customer service. IBM Cloud Pak for Data is often highlighted for its comprehensive set of features, making it a preferable choice for many users.
Features: Denodo is highly rated for its data virtualization capabilities, allowing seamless integration of distributed data. IBM Cloud Pak for Data offers a broader suite of tools, including AI and machine learning integrations, which users find valuable. Denodo excels in data connectivity, but IBM's extensive features provide an edge in overall functionality.
Room for Improvement: Users suggest that Denodo could enhance its performance at scale and improve its data cataloging features. IBM Cloud Pak for Data users point out that the platform could benefit from more intuitive administrative tools and better integration with non-IBM systems. Denodo could also work on providing better templating options, while IBM users mention the need for a smoother upgrade process.
Ease of Deployment and Customer Service: Denodo is praised for its straightforward deployment process and responsive customer service. IBM Cloud Pak for Data is also recognized for strong customer support but has a more complex deployment process due to its comprehensive feature set.
Pricing and ROI: Denodo is often seen as offering better initial setup costs and quicker ROI due to its streamlined deployment. IBM Cloud Pak for Data, while perceived as more expensive, justifies the cost with its extensive functionalities and longer-term ROI. Users find Denodo cost-effective in the short term, whereas IBM is considered worthwhile for its long-term benefits.
It provides a positive return on investment for those who can connect multiple data sources and make data-driven decisions easily.
If you don't need to write a whole ETL to make the data virtualization, you need way fewer people to write a query instead of writing an ETL pipeline.
I have seen a return on investment, which showed up in improved customer satisfaction scores.
We have been able to drive responsible, transparent, and explainable AI workflow to operationalize AI and mitigate risk and regulatory compliance easily.
It is easy to collect, organize, and analyze data no matter where it is, hence being able to make data-driven decisions.
They have a good methodology for learning how to use the tool.
Denodo's customer support team is very competent and responsive.
If we raise a ticket, it can be resolved or addressed within a reasonable time frame, so support is good.
Cloud Pak is a complicated system, and it's often difficult to find the right resource in IBM to help with specific issues.
The customer support for IBM Cloud Pak for Data is great and responsive.
The response time for IBM's technical support is excellent.
For huge data requests, it cannot scale automatically; admin action is required.
Denodo's scalability comes into play specifically when there is data transfer.
My client has almost 100 million records, and the performance was impacted in a way that required optimization.
I have not noticed any downtime or lagging, especially when dealing with large data, so it is relatively very scalable.
IBM Cloud Pak for Data's scalability is very good; it can be used by any size of organization.
I would rate it nine out of ten because it is very reliable, always performing as expected.
If JVM does not function properly, it may cause Denodo to fail to connect to different sources.
Denodo is stable and good.
The overall performance of IBM Cloud Pak for Data, particularly with IBM DataStage for ETL processes, is very good.
Ensuring data caching is up to date is critical.
Denodo needs better communication on how the product can be deployed for specific solutions.
The system has dependencies on other environments, like JVM, which can affect its performance.
Setting up the hybrid and multi-cloud environments is a long job and it takes time.
IBM Cloud Pak for Data can be improved because processing speeds are sometimes slow.
To improve IBM Cloud Pak for Data, I suggest more out-of-the-box integration.
For small companies, it's not a solution that most small companies can afford.
Denodo is considered pricey, limiting its use to large enterprises or government organizations that can afford its comprehensive setup.
Denodo's pricing is not affordable for small companies and is more suitable for medium to large enterprises.
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.
The list price is high, but the flexibility in pricing is adequate.
Denodo's ability to connect to multiple data sources and perform extract-transform-load (ETL) operations on the fly is noteworthy.
The most valuable feature of Denodo is its uniformity of self-site data access types, which allows it to connect to almost any storage technology and feed it transparently.
Denodo supports SQL base, so if you want to join two tables or two views, you can use SQL, which is an advantage as most developers or business people know SQL.
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 | Mindshare (%) |
|---|---|
| Denodo | 22.1% |
| IBM Cloud Pak for Data | 11.3% |
| Other | 66.6% |


| Company Size | Count |
|---|---|
| Small Business | 16 |
| Midsize Enterprise | 6 |
| Large Enterprise | 21 |
| Company Size | Count |
|---|---|
| Small Business | 9 |
| Large Enterprise | 15 |
Denodo specializes in data virtualization, data cataloging, and user-friendly interfaces. It's recognized for connecting disparate data sources, presenting unified data for analytics, and supporting efficient decision-making with agile analytics and robust data governance.
Denodo effectively aggregates data from multiple sources to offer a comprehensive understanding through its virtualization capabilities. It provides role-based access control, flexible query languages, performance optimization, and integration with databases. Enhancements are needed in its interface and documentation to ensure better user experiences. While the platform supports cloud migration, integration challenges with tools like Salesforce and MuleSoft exist. Improvements in data visualization, automation, and scalability, especially in large data environments, are critical areas for growth.
What are the key features of Denodo?In industries like finance, healthcare, and retail, Denodo plays a crucial role in data virtualization and integration. Organizations use it to unify disparate data systems, enabling real-time analytics and supporting cloud migrations. Denodo's platform is ideal for businesses needing to aggregate, transform, and utilize diverse data efficiently, optimizing operations and enhancing governance.
IBM Cloud Pak for Data is a comprehensive platform integrating data management, AI, and machine learning capabilities tailored for hybrid environments. It's renowned for enhancing productivity through efficient data analytics and management.
This platform offers data virtualization, robust analytics, and AI-driven processes. Its integration capabilities, including IBM MQ and App Connect, facilitate seamless data connections. Users benefit from containerization, data governance, and compatibility with hybrid systems, improving decision-making and management productivity. However, the requirement of extensive infrastructure and performance challenges can impact scalability for small businesses.
What are the key features of IBM Cloud Pak for Data?In the financial and banking sectors, IBM Cloud Pak for Data is utilized for data management tasks like spend analytics and contract leakage analysis. It's used for data integration, machine learning, and AI-driven analytics to transform data into valuable insights in industries such as FinTech and consultancy.
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