The imperative for real-time data processing continues to grow, yet many organizations find their legacy mainframes ill-equipped to handle the demands of modern data-driven applications. This session delves into a the best practices of liberating mainframe data and integrating it seamlessly into modern data architectures using Apache Kafka, Apache Flink, Apache Spark illustrated with compelling business use cases.

We'll explore strategies for selectively offloading mainframe workloads to cloud-native environments, leveraging Kafka's high-throughput, scalable, and fault-tolerant architecture. Key topics include:

Data Extraction Strategies: Examining techniques for efficiently extracting data from mainframes, including change data capture (CDC) mechanisms and message queuing integrations.
Schema Evolution and Data Transformation: Addressing the challenges of data consistency and format discrepancies between mainframe and modern systems using schema registries and stream processing frameworks.
Building Real-time Data Pipelines: Constructing robust and scalable data pipelines with Kafka Connect, enabling seamless data flow between mainframes and cloud-native applications.
Security and Governance: Implementing robust security measures to protect sensitive mainframe data throughout the migration and integration process, ensuring compliance with industry regulations.

Real-world Use Cases: such as:

Financial Services: Enabling real-time fraud detection by integrating mainframe transaction data with modern fraud analytics platforms.

This session provides a technical roadmap for architects and engineers seeking to modernize their mainframe data infrastructure. We'll discuss best practices, architectural patterns, and real-world examples to illustrate how Kafka can unlock the full potential of your mainframe data in the age of real-time insights.