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Kafka

Confluent Cloud for Apache Flink | Simple, Serverless Stream Processing

Stream processing plays a critical role in the infrastructure stack for data streaming. Developers can use it to filter, join, aggregate, and transform their data streams on the fly to power real-time applications and streaming data pipelines. Among stream processing frameworks, Apache Flink has emerged as the de facto standard because of its performance and rich feature set. However, self-managing Flink (like self-managing other open source tools like Kafka) can be challenging due to its operational complexity, steep learning curve, and high costs for in-house support.

The Confluent Q1 '24 Launch

The Confluent Q1 ’24 Launch is packed with new features that enable customers to build, connect, and consume intelligent data pipelines seamlessly and securely Our quarterly launches provide a single resource to learn about the accelerating number of new features we’re bringing to Confluent Cloud, our cloud-native data streaming platform.

4 Key Types of Event-Driven Architecture

Adam Bellemare compares four main types of Event-Driven Architecture (EDA): Application Internal, Ephemeral Messaging, Queues, and Publish/Subscribe. Event-Driven Architectures have a long and storied history, and for good reason. They offer a powerful way to build scalable and decoupled architectures. But thanks to its long history, people often have different ideas of what EDA means depending on when they first encountered this architecture.

How to Evolve your Microservice Schemas | Microservices 101

Schema evolution is the act of modifying the structure of the data in our application, without impacting clients. This can be a challenging problem. However, it gets easier if we start with a flexible data format and take steps to avoid unnecessary data coupling. When we find ourselves having to make breaking changes, we can always fall back to creating new versions of our APIs and events to accommodate those changes.

What is a Kafka Consumer and How does it work?

Now that your data is inside your Kafka cluster, how do you get it out? In this video, Dan Weston covers the basics of Kafka Consumers: what consumers are, how they get your data flowing, and best practices for configuring consumers in a real-time data streaming system. You will also learn about offsets, consumer groups, and partition assignment.

#12 Kafka Live Stream | HTTP Sink Connector & Business Automation with Make

See the new Lenses Kafka to HTTP Sink Connector in action with Lenses.io and @itsmake. In this 30 minute session, we show you how to trigger APIs that automate your business processes: a message in Kafka calls a Make workflow, then triggering an automation in Salesforce.

What is the Listen to Yourself Pattern? | Microservices 101

The Listen to Yourself pattern is implemented by having a microservice emit an event to a platform such as Apache Kafka, and then consuming its own events to perform internal updates. It can be used as a solution to the dual-write problem since it separates Kafka and database writes into different processes. However, it also provides added benefits because it allows microservices to respond quickly to requests by deferring processing to a later time.