In this video, Wade explores the process of decomposing a monolith into a series of microservices. You'll see how Tributary bank extracts a variety of API methods from an existing monolith. Tributary Bank wants to decompose its monolith into a series of microservices. They are going to start with their Fraud Detection service. However, before they can start, they first have to untangle the existing code. They will need to define a clean API that will allow them to move the functionality to an asynchronous, event-driven microservice.

Learn how the Data Portal and Apache Flink in Confluent Cloud can help developers and data practitioners find the data they need to quickly create new data products.

How do you prevent hallucinations from large language models (LLMs) in GenAI applications? LLMs need real-time, contextualized, and trustworthy data to generate the most reliable outputs. Kai Waehner, Global Field CTO at Confluent, explains how RAG and a data streaming platform with Apache Kafka and Flink make that possible.

How do we know whether Event-Driven Microservices are the right solution? This is the question that Tributary Bank faced when they looked at modernizing their old fraud-detection system. They were faced with many challenges, including scalability, reliability, and security. Some members of their team felt that switching to an event-driven microservice architecture would be the magic bullet that would solve all of their problems. But is there any such thing as a magic bullet? Let's take a look at the types of decisions Tributary Bank had to make as they started down this path.

For their inaugural episode, Anna McDonald (the Duchess), Matthias J. Sax (the Doctor), and their extinct friend, Phil, wax rhapsodic about all things eventing: you’ll learn why events are a mindset, why the Duchess thinks you’ll find event immutability relaxing, and why your event streams might need some windows. The Duchess & The Doctor Show features a question-driven format that delivers substantial, yet easily comprehensible answers to user-submitted questions on all things events and eventing, including Apache Kafka, its ecosystem, and beyond!

Learn how Apache Flink® can handle hundreds or even thousands of compute nodes running 24/7 and still produce correct results.

Learn how consumer partition assignment works in Apache Kafka.

In this video, Adam Bellemare compares and contrasts Event-Driven and Request-Driven Architectures to give you a better idea of the tradeoffs and benefits involved with each. Many developers start in the synchronous request-response (RR) world, using REST and RPC to build inter-service communications. But tight service-to-service coupling, scalability, fan-out sensitivity, and data access issues can still remain.

Every company faces the perennial problem of data integration but often experiences data silos, data quality issues, and data loss from point-to-point, batch-based integrations. Connectors decouple data sources and sinks through Apache Kafka, simplifying your architecture while providing flexibility, resiliency, and reliability at a massive scale.

An Event-Driven Architecture is more than just a set of microservices. Event Streams should represent the central nervous system, providing the bulk of communication between all components in the platform. Unfortunately, many projects stall long before they reach this point.