

Qlik Replicate and IBM Cloud Pak for Data both compete in the data integration and management space. Qlik Replicate seems to have the upper hand due to its strong real-time data capture and replication capabilities.
Features: Qlik Replicate is noted for its real-time change data capture, seamless data replication without impacting source databases, and strong data manipulation capabilities. It also offers easy data integration across various endpoints with robust logging functionalities. IBM Cloud Pak for Data stands out with advanced data preparation, AI integration through Watson Studio, and strong data governance via Knowledge Catalog. Its data virtualization feature enables efficient data analysis and connection to multiple data sources.
Room for Improvement: Qlik Replicate could enhance error message clarity, user interface, and connectivity with multiple data destinations. It also requires improvements in support services and API handling. IBM Cloud Pak for Data needs a more streamlined setup and deployment process, expanded connector availability, and better handling of heavy infrastructure demands. Enhancements in performance and support for diverse connectors are also necessary.
Ease of Deployment and Customer Service: Qlik Replicate functions well across public and on-premises environments and is generally praised for proactive support, though response times can improve. IBM Cloud Pak for Data primarily supports public and hybrid clouds but faces challenges with its user interface and infrastructure needs. Its customer service requires quick and decisive improvement.
Pricing and ROI: Qlik Replicate is viewed as costly for small businesses, with a core-based licensing model favoring large data sets but discouraging smaller deployments. Despite high costs, it offers ROI through reduced database usage and maintenance. IBM Cloud Pak for Data targets larger enterprises with high costs linked to advanced functionalities like AI, and is regarded as complex in pricing, yet it promises ROI via time-saving and operational efficiency.
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.
I conducted a cost comparison with the AWS service provider, and this option is much cheaper than the Kinesis service offered by AWS.
Customers have seen ROI with Qlik Replicate because they get their data for analysis faster, enabling quicker decision-making compared to traditional data sourcing methods.
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.
Even priority tickets, which should be resolved in minutes, can take days.
Support response times could be improved as there are sometimes delays in receiving replies to support cases.
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.
The system could be scaled to include more sources and functions.
The overall performance of IBM Cloud Pak for Data, particularly with IBM DataStage for ETL processes, is very good.
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.
It is a core-based licensing, which, especially in the banking industry, results in the system capacity being utilized up to a maximum of 60%.
Qlik Replicate could be improved in the next release by incorporating more monitoring options to monitor the logs.
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 is calculated based on the machine's total capacity rather than actual usage.
For Qlik Replicate, the setup cost includes the requirement of a server, which represents the hardware cost that must be covered.
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.
The most valuable feature of Qlik Replicate is their change data capture feature.
Data retrieved from the system can be pushed to multiple places, supporting various divisions such as marketing, loans, and others.
| Product | Mindshare (%) |
|---|---|
| IBM Cloud Pak for Data | 1.2% |
| Qlik Replicate | 1.4% |
| Other | 97.4% |

| Company Size | Count |
|---|---|
| Small Business | 9 |
| Large Enterprise | 15 |
| Company Size | Count |
|---|---|
| Small Business | 9 |
| Large Enterprise | 11 |
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.
Qlik Replicate offers log-based change data capture, supporting real-time data updates without affecting source databases. It manages schema changes automatically and ensures seamless data distribution. The platform is user-friendly, enables late-stage transformation, and supports incremental replication and real-time analytics.
Qlik Replicate is known for efficiently capturing data changes with minimal impact on source databases. Its log-based change data capture capabilities ensure quick propagation of updates in real-time while automatically handling schema changes, facilitating ease in data management. The system's seamless integration across endpoints and a user-friendly interface make it an invaluable tool for incremental replication and real-time analytics. Despite some challenges like UI freezing, complex licensing, and error handling, it is instrumental in enhancing business growth and operational efficiency. Users continuously seek improvements in error insights, data compression, and expanded API integration to better serve diverse data sources and platforms.
What are the key features of Qlik Replicate?Qlik Replicate is used across industries such as energy, banking, and semiconductors to modernize analytics environments and streamline data flows. It excels in data migration from systems like SAP HANA and Oracle to environments like AWS, significantly reducing downtime and boosting analytics capabilities. Organizations report advantages such as enhanced data accessibility and automated data modeling, which facilitates efficient change data capture and operational effectiveness.
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