Apache Flink

This blog details an end-to-end real-time prediction project leveraging the combined capabilities of Confluent Cloud stacks and Google Cloud Vertex AI. This project aims to deliver a streamlined solution for real-time prediction applications, catering to the evolving needs and challenges of moder...

The blog post provides a comprehensive overview of the Flink Table API, demonstrating how it enables developers to express complex data processing logic using Java or Python in a user-friendly manner. It also includes practical examples and guidance, making it a valuable resource for anyone...

Confluent は、Flink SQL の AI モデル推論により、データ処理と AI タスクの両方に統合されたプラットフォームを提供することで、RAG 対応の生成 AI アプリケーションの開発と展開を簡素化します。リアルタイムの航空会社データを使用して RAG 対応の Q&A チャットボットを構築する方法について学びます。

The Apache Flink® community released Apache Flink 1.20 this week. In this blog post, we highlight some of the most interesting additions and improvements.

Confluent Cloud for Apache Flink®️ supports AI model inference and enables the use of models as resources in Flink SQL, just like tables and functions. You can use a SQL statement to create a model resource and invoke it for inference in streaming queries.

Part two in the series on using FlinkSQL, Kafka, and Streamlit dives into async.io, FlinkSQL syntax, and Streamlit barchart component structure.

In part 1 of this series, we’ll make an app, powered by Kafka and FlinkSQL in Confluent Cloud and visualized with Streamlit, that allows a user to select a stock, in this case SPY, or the SPDR S&P 500 ETF Trust. Upon selection, a live chart of the stock’s bid prices, calculated every five seconds...

このたび、3つの主要なクラウドすべてで Confluent Cloud for Apache Flink の一般提供を開始したことをお知らせします。これにより、Kafka と Flink を、データをリアルタイムで接続・処理するための統合されたエンタープライズグレードのプラットフォームとして体験できるようになりました。

Check out all the highlights from the Apache Flink® 1.19 release!

Several key new features have been added to Confluent Cloud for Apache Flink this year including Topic Actions, Terraform support, and expansion into GCP and Azure. Let's take a look at these enhancements and how they empower users to harness the full potential of streaming data.

In this blog post, we will provide an overview of Confluent's new SQL Workspaces for Flink, which offer the same functionality for streaming data that SQL users have come to expect for batch-based systems.

The Apache Flink PMC is pleased to announce the release of Apache Flink 1.18.0. As usual, we are looking at a packed release with a wide variety of improvements and new features. Overall, 174 people contributed to this release completing 18 FLIPS and 700+ issues.

As of today, Confluent Cloud for Apache Flink® is available for preview in select regions on AWS. In this post, learn how we’ve re-architected Flink as a cloud-native service on Confluent Cloud.

Learn how to build a Java pipeline that consumes clickstream data from Apache Kafka®. Consuming clickstreams is something that many businesses have a use for and it can also be generalized to consuming other types of streaming data.