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IBM App Connect definitely saves significant time, approximately 50 to 60%.
Reliable data plus less human intervention and less error result in a strong return on investment.
When opening a ticket with the global team, problems are resolved promptly and effectively.
The customer support is available 24/7.
The technical support from IBM is good.
The support for SAS in Brazil is not the best one, but the support in Sweden is really good, as they visit the company and work to solve the issues.
I would rate the scalability of IBM App Connect as nine out of ten.
IBM App Connect demonstrates good scalability.
IBM App Connect is very scalable and a flexible tool.
IBM App Connect occasionally crashes for various reasons, requiring problem-solving intervention.
Better debugging and observability would help us track any single transaction end-to-end across steps and connectors.
Version 13 includes around 200 features with cloud platform compatibility.
I find it particularly good for on-premises and now cloud use.
There is significant room for improvement, especially with regard to using a hybrid approach that involves both CAS and persistent storage.
SAS Data Management can be improved in terms of the learning curve.
For insurance companies with simple JDBC connections, the process is straightforward.
From my experience, SAS Data Management is an expensive tool.
Overall, 50 to 60% of the time is saved when using IBM App Connect.
The transformation capabilities in IBM App Connect are particularly beneficial.
The features I find most valuable are message routing, message transformation, and protocol translation.
It is very reliable, very time-saving, and the chances of error are minimal.
SAS Data Management stands out because of its data standardization, transformation, and verification capabilities.
The metadata management feature of SAS Data Management helps a lot; creating your data marts or data lake with good naming conventions, library conventions, and so on is very important because it allows easy queries to find the whole structure, though I think metadata governance also depends on first definitions, not only on the tool.


| Company Size | Count |
|---|---|
| Small Business | 4 |
| Midsize Enterprise | 4 |
| Large Enterprise | 21 |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 2 |
| Large Enterprise | 8 |
IBM App Connect provides efficient and secure app integration with expansive connectors and a low-code interface that simplifies complex integrations and supports Kubernetes scalability.
IBM App Connect is designed for seamless integration of applications, offering extensive security and scalability features. Its intuitive interface leverages a low-code approach to save time and simplify integrations. The platform supports hybrid environments with adapters that reduce custom API work, while advanced features such as message routing, transformation, and protocol translation enhance its functionality. Built-in error handling and comprehensive documentation aid in efficient operations. Users highlight areas for improvement in command line integration, community support, and CI/CD capabilities. More connectors and enhanced event streaming can better meet modern needs.
What are the key features of IBM App Connect?Organizations use IBM App Connect to integrate applications, orchestrate data, and implement enterprise service bus functionalities. It connects applications, validates data, and facilitates communication between diverse systems. Serving as an integration hub, it supports cloud and on-premises infrastructures crucial in sectors like banking, e-commerce, and CRM. Many users rely on it for API development, ETL tasks, and automating workflows with both cloud and on-premises setups.
SAS Data Management provides data integration, governance, and robust reporting tools. It connects to diverse data sources, ensuring quality management and enabling data analysis for technical and non-technical users.
SAS Data Management features flexible data flow creation, scheduling, and ETL control. It enhances data integration and metadata management with tools that support data standardization. Users benefit from its importing and exporting capabilities, connecting to multiple sources. It facilitates improved data quality management and offers a flexible language for diverse needs. Data visualization capabilities further support decision-making across industries, automating reports and data warehouses.
What are the key features of SAS Data Management?SAS Data Management helps industries like finance integrate diverse data sources for analytics and reporting. It is used for tasks such as financial reporting, credit risk analysis, and data cleansing. Through user-driven automation, it aids in aligning data warehouses and generating insightful visual outputs, making it ideal for analyzing structured data from sources like Excel and CSV files.
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