Let’s discuss the Lambda and Kappa architectural styles for data processing at the edge and describe a retail banking customer experience example for Kappa.
Join the DZone community and get the full member experience. Banks are accelerating their modernization effort to rapidly develop and deliver top-notch digital experiences for their customers. To achieve the best possible customer experience, decisions need to be made at the edge where customers interact. It is critical to access associated data to make decisions. Traversing the bank’s back-end systems, such as mainframes, from the digital experience layer is not an option if the goal is to provide the customers the best digital experience. Therefore, for making decisions fast without much latency, associated data should be available closer to the customer experience layer. Thankfully, over the last few years, the data processing architecture has evolved from ETL-centric data processing to real-time or near real-time streaming data processing architecture. Such patterns as change data capture (CDC) and command query responsibility segregation (CQRS) have evolved with architecture styles like Lambda and Kappa. While both architecture styles have been extensively used to bring data to the edge and process, over a period of time data architects and designers have adopted Kappa architecture over Lambda architecture for real-time processing of data. Combining the architecture style with advancements in event streaming, Kappa architecture is gaining traction in consumer-centric industries. This has greatly helped them to improve customer experience, and, especially for large banks, it is helping them to remain competitive with FinTech, which has already aggressively adopted event-driven data streaming architecture to drive their digital (only) experience. In this article, we discuss the two architectural styles (Lambda and Kappa) for data processing at the edge and describe an example of real-life implementation of Kappa Architecture in a retail banking customer experience scenario. Both the Lambda and Kappa architecture styles were developed to support big data analytics in processing and handling a large volume of data at a high velocity.
