In this video, we'll look at some techniques for evolving events by analyzing a specific use case in a banking fraud detection system.

When developing or debugging a stream processing pipeline with Flink SQL, it’s common to inspect each processing step's output to ensure data is being transformed properly. However, comprehending the resulting data stream's structure, distribution, and characteristics entails executing multiple ad-hoc SQL queries, which can be time-consuming and tedious. Additionally, isolating specific subsets of the stream for analysis or debugging often involves even more queries, adding to the complexity and time required.

Learn how to leverage the native monitoring capabilities of the Python Kafka producer along with Confluent Cloud’s Metrics API while exploring how linger.ms affects latency and batch sizes.

When building a distributed system, developers are often faced with something known as the dual-write problem. It occurs whenever the system needs to perform individual writes to separate systems that can't be transactionally linked. This situation creates the potential for data loss if the developer isn't careful. However, techniques such as the Transactional Outbox Pattern and Event Sourcing can be used to guard against the potential for data loss while also providing added resilience to the system.

Tabs or spaces? Merge vs. rebase? Flink SQL vs. KStreams? Let’s Settle This is powered by a new Kafka JavaScript client from Confluent: confluent-kafka-javascript (early access). Find out how Lucia used it to make the website in the video above.

The headless data architecture. Is it a fad? Some marketecture? Or something real? In this video, Adam Bellemare takes you through the basics of the headless data architecture and why it’s beginning to emerge as its own respective pattern. Driven by the decoupling of data computation from storage, the headless data architecture provides the basis for a modular data ecosystem. Stream your data for near real-time low latency use cases, or convert it to an Iceberg table for analytical use cases.

In this video, you will see an example of how Tributary bank uses asynchronous events to enrich its domain and protect its fraud detection system from failures.

In this video, we discuss one way a business can approach decomposing a monolith using a series of clearly defined steps and robust monitoring.

Join Lucia Cerchie in a coding walkthrough, bridging the gap between REST APIs and data streaming. Together we’ll transform the OpenSky Network's live API into a data stream using Kafka and Flink SQL. Not only do we change the REST API into a data stream in this walkthrough, but we clean up the data on the way! We use Flink SQL to make it more readable and clean, and in that way we keep more of the business logic away from the client code.

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.