

Find out in this report how the two Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
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.
The incidents have disappeared completely since I have been using Striim.
Striim's continuous pipeline safely processes millions of daily transactions without data loss; my organization had millions of transactions every day and even in real-time, so it was quite good.
Since it is now one to two hours, I would say it has saved employee hours and time.
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.
They are all knowledgeable about what they do.
When you contact them, they give you a response straight away and help you identify the issue and fix it.
The customer support has been excellent, and their engineers actually understand z/OS architecture and DB2 logs.
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.
Our licensing was based on the number of cores, so even if we have a high number of events on any given day, our license cost would not go high.
I would describe the scalability of Striim as very good, as it adapts well.
Striim can handle the data volumes effectively, but it can struggle a little bit if the data volume is too high.
The overall performance of IBM Cloud Pak for Data, particularly with IBM DataStage for ETL processes, is very good.
That problem has been completely resolved with Striim.
Striim was very stable.
In my experience, Striim is mostly stable.
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.
I believe if Striim could use some part of AWS secrets or its own secret mechanism to store the password, that would really save a lot of time so that I don't have to keep updating the password whenever there is any change.
They could improve the documentation by showing how to configure with different platforms.
I think Striim could be improved with better pricing and enhanced documentation.
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.
Licensing was a bit more expensive because Striim has to read from Oracle GoldenGate trail files and also integrate them.
My experience with the pricing, implementation cost, and licensing of Striim is that it is somewhat expensive.
It's very fair.
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.
Striim is capable of absorbing a large number of transactions, and the difference between the two databases is always less than a second, which demonstrates efficiency.
There were significant improvements because once we enabled change data capture, the database was not going down at all.
It reduces manual intervention because it automatically syncs the data from the warehouse.
| Product | Mindshare (%) |
|---|---|
| IBM Cloud Pak for Data | 1.0% |
| Striim | 0.6% |
| Other | 98.4% |

| Company Size | Count |
|---|---|
| Small Business | 10 |
| Large Enterprise | 20 |
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.
Striim offers a comprehensive platform for real-time data integration and streaming analytics, designed to streamline data processes for enterprise-level solutions.
Striim enables seamless migration and integration of data across cloud and on-premises environments, making it ideal for businesses looking to leverage real-time analytics. Its capabilities support continuous data flow, reducing latency and enhancing decision-making. Designed for scalable and secure data management, Striim facilitates effective data-driven strategies.
What are some key features of Striim?In industries like finance, Striim supports real-time fraud detection by providing uninterrupted data streaming between transaction systems. In healthcare, it enables rapid data processing for patient monitoring, improving service delivery. Manufacturing uses Striim to enhance supply chain visibility through real-time data analytics.
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