Discover how GEP, an AI-powered procurement company, utilized Confluent's data streaming platform to transform its generative AI capabilities. Integrating real-time data into their AI models enabled GEP to provide a contextual chat-based service. This chatbot allowed GEP customers to build their own tools simply by communicating in English with a chatbot.

Learn how replication works in Apache Kafka. Deep dive into its critical aspects, including: Whether you're a systems architect, developer, or just curious about Kafka, this video provides valuable insights and hands-on examples. Don't forget to check out our GitHub repo to get all of the code used in the demo, and to contribute your own enhancements.

Watermarks are at the heart of what makes Apache Flink’s streaming SQL engine different from batch-oriented SQL processors, like databases. Join David Anderson as he explains the ins and outs of watermarks in Flink SQL.

Booking.com wanted to give people a “connected trip” experience, allowing customers to seamlessly book flights, accommodations, car rentals, and excursions in one visit. The company realized the value of data streaming early on in reaching this goal, but the operational effort had become overwhelming. Learn how Booking.com found the answer in Confluent’s data streaming platform. With its automated configuration that required no ongoing maintenance, the team was able to prioritize innovation with data and provide the comprehensive booking experience they had been searching for.

This is a one-minute video showing an animated architectural diagram of an integration between Amazon DynamoDB and Confluent Cloud using an open-source Kafka connector. The integration allows you to avoid maintaining custom code, and gives you the ability to automatically discover and adapt to changes in DynamoDB tables. All details are provided.

Apache Flink SQL has a lot in common with SQL databases, but in several fundamental ways it’s actually quite different. Learn how Apache Flink has adapted concepts like queries, materialized views, catalogs, and ACID guarantees from SQL databases to fit into the world of stream processing.

Using Apache Flink watermarks can create havoc when you have a stream that has significant delays between records. In this video we’ll talk about a possible solution to this situation using a batch close record, which will ensure that your data is up to date.

This is a one-minute video showing an animated architectural diagram of an integration between Amazon DynamoDB and Confluent Cloud using Kinesis Data Streams and the Kinesis Data Streams connector. It’s a fully managed and serverless solution that reduces operational complexity and leverages scalability and cost-effectiveness.