Sadhana Nandakumar

Sadhana is a Specialist Solutions Architect at Red Hat. She has extensive experience in creating solutions for Banking, Healthcare and Insurance customers across the globe. With Interests ranging from Automation to Enterprise Integration, her passion is creating solutions and documentation around technology and real-world problems in a manner that is easy to assimilate and extend.

Recent Posts

Event streaming and data federation: A citizen integrator’s story

Event streaming and data federation: A citizen integrator’s story

Businesses are seeking to benefit from every customer interaction with real-time personalized experience. Targeting each customer with relevant offers can greatly improve customer loyalty, but we must first understand the customer. We have to be able to draw on data and other resources from diverse systems, such as marketing, customer service, fraud, and business operations. With the advent of modern technologies and agile methodologies, we also want to be able to empower citizen integrators (typically business users who understand business and client needs) to create custom software. What we need is one single functional domain where the information is harmonized in a homogeneous way.

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Monitor business metrics with Red Hat Process Automation Manager, Elasticsearch, and Kibana

Monitor business metrics with Red Hat Process Automation Manager, Elasticsearch, and Kibana

Red Hat Process Automation Manager is a platform for developing containerized microservices and applications that automate business decisions and processes. Combining process- and task-level SLA metrics plus case-related breakdowns can be beneficial for identifying trends and reorganizing the workforce as necessary. So, a critical piece of a business process system is having real-time insights into what is happening, and both monitoring KPI metrics and responding to problem trends is an integral part of operations.

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Dynamic case management in the event-driven era

Dynamic case management in the event-driven era

Case management applications are designed to handle a complex combination of human and automated tasks. All case updates and case data are captured as a case file, which acts as a pivot for the management. This then serves as a system of record for future audits and tracking. The key characteristic of these workflows is that they are ad hoc in nature. There is no single resolution, and often, one size doesn’t fit all.

Case management does not have structured time bounds. All cases typically don’t resolve at the same time. Consider examples like client onboarding, dispute resolution, fraud investigations, etc., which, by virtue, try to provide customized solutions based on the specific use case. With the advent of more modern technological frameworks and practices like microservices and event-driven processing, the potential of case management solutions opens up even further. This article describes how you can make use of case management for dynamic workflow processing in this modern era, including components such as Red Hat OpenShift, Red Hat AMQ Streams, Red Hat Fuse, and Red Hat Process Automation Manager.

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CDC pipeline with Red Hat AMQ Streams and Red Hat Fuse

CDC pipeline with Red Hat AMQ Streams and Red Hat Fuse

Change Data Capture (CDC) is a pattern that enables database changes to be monitored and propagated to downstream systems. It is an effective way of enabling reliable microservices integration and solving typical challenges, such as gradually extracting microservices from existing monoliths.

With the release of Red Hat AMQ Streams 1.2, Red Hat Integration now includes a developer preview of CDC features based on upstream project Debezium.

This article explains how to make use of Red Hat Integration to create a complete CDC pipeline. The idea is to enable applications to respond almost immediately whenever there is a data change. We capture the changes as they occur using Debezium and stream it using Red Hat AMQ Streams. We then filter and transform the data using Red Hat Fuse and send it to Elasticsearch, where the data can be further analyzed or used by downstream systems.

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