#confluent

Executive Summary Hyper-personalization in 2026 is the ability to act on a user's current...

Getting raw documents, scanned PDFs, and complex images ready for RAG and agentic AI in real time...

Executive Summary The problem. Predictive ML pipelines that maintain separate batch and...

Adding large language models (LLMs) and artificial intelligence (AI) to real-time event streams comes...

Stream processing (like Apache Flink®) performs continuous, deterministic precomputation as data...

Moving a retrieval-augmented generation (RAG) prototype from a Python notebook into production isn't...

The EU AI Act's general provisions are already in force, and high-risk AI system obligations apply...

Digging into a new universe of data streaming after IBM’s recent accquisition of...

Understanding why even Kafka exists!? Introduction Since the recent acquisition of...

IBM is solving real-time data for agents. AWS is solving inference speed. Both are...

In this article I show how to build a production-ready Kafka event-driven microservice using Spring Boot, Confluent Schema Registry, Avro serialization and PostgreSQL persistence. A practical, production-style demo using Spring Boot, Apache Kafka, Confluent Schema Registry, Avro serialization and PostgreSQL.

Table of Contents Introduction A brief overview on Avro, JSON, and Protobuf What is Confluent's...