Systems | Development | Analytics | API | Testing

Constantly starting and stopping Apache Kafka producers and consumers? That’s a recipe for high resource usage and inefficiency. Short-lived connections are heavy on resources, and can slow down your whole cluster. Keep them running to boost performance, cut latency, and get the most out of your Kafka setup.

Default settings in Apache Kafka work when you’re getting started, but aren't suited for production. Sticking with defaults, like a seven-day retention policy, or a replication factor of one, can cause storage issues, or data loss in case of failure. Learn why optimizing retention periods, replication factors, and partitions, is crucial for better Kafka performance and reliability.

Keeping your Apache Kafka clients up-to-date is critical for maximizing performance, security, and stability. In this video, we discuss why sticking with old versions could be putting you at risk, since it means you’re missing out on dozens of new features, and hundreds of bug fixes and security patches. Learn why upgrading is more than just a “nice-to-have”—it’s essential for a smoother and safer Kafka experience.

Setting up network connections can often seem difficult or time consuming. This video provides a wayfinding introduction to help you get networking up and running for all cluster types on Confluent Cloud, showing you your networking options for each cluster type when running on AWS, Azure, or Google Cloud, respectively.

In this short video, a Confluent Solutions Engineering will run through the high-level steps on how to get started with your migration. And even better, once you’re done watching, you can download our comprehensive migration kit for a step by step guide of everything I’ve talked about and more.

Kafka Connectors provide a way to get data flowing between your sources and sinks, and Confluent Cloud. This video reviews your options and shows you how to get started quickly.

This video provides a tour of the main Confluent Cloud interface, and shows you how to set up your first cluster.

Data visualization is cool, but have you tried setting up a chart of real-time data? In this video, Lucia Cerchie shows you how to create a live visualization of market data. She starts by producing data to a topic in Confluent Cloud from an Alpaca API websocket, then processes that data with Flink SQL, and finally uses a Streamlit component for a real-time visualization.

In this recap video from Current 2024, attendees share their favorite moments from the event. From insightful talks on data streaming innovation to hands-on workshops and networking opportunities, hear what participants found most valuable.

Apache Flink SQL makes it easy to implement analytics that summarize important attributes of real-time data streams. There are four different types of time-based windows in Flink SQL: tumbling, hopping, cumulating, and session. Learn how these various window types behave, and how to work with the table-valued functions that are at the heart of Flink SQL’s support for windowing.