Author: Kai Waehner

In the era of Industry 4.0, the fusion of smart, autonomous systems with digitization is propelling manufacturing and supply chain operations into uncharted efficiency. Industry 4.0, otherwise known as the Fourth Industrial Revolution, marks the normalization of the smart factory.

The global video game market is bigger than the music and film industry combined. It includes Triple-A, casual/mid-core, mobile, and multiplayer online games. Various business models exist, such as hardware […]

SAP is a German multinational software corporation that develops and markets enterprise software to manage business operations and customer relations. SAP is most famous for its enterprise resource planning (ERP) […]

This blog post presents the use cases and architectures of REST APIs and Confluent REST Proxy, and explores a new management API and improved integrations into Confluent Server and Confluent […]

With billions of Internet of Things (IoT) devices, achieving real-time interoperability has become a major challenge. Together, Confluent, Waterstream, and MQTT are accelerating Industry 4.0 with new Industrial IoT (IIoT) […]

The combination of streaming machine learning (ML) and Confluent Tiered Storage enables you to build one scalable, reliable, but also simple infrastructure for all machine learning tasks using the Apache […]

The relationship between Apache Kafka® and machine learning (ML) is an interesting one that I’ve written about quite a bit in How to Build and Deploy Scalable Machine Learning in […]

The Internet of Things (IoT) is getting more and more traction as valuable use cases come to light. A key challenge, however, is integrating devices and machines to process the […]

In the early days, many companies simply used Apache Kafka® for data ingestion into Hadoop or another data lake. However, Apache Kafka is more than just messaging. The significant difference […]

Microservices have a symbiotic relationship with domain-driven design (DDD)—a design approach where the business domain is carefully modeled in software and evolved over time, independently of the plumbing that makes […]

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine […]

Machine learning and the Apache Kafka® ecosystem are a great combination for training and deploying analytic models at scale. I had previously discussed potential use cases and architectures for machine […]

ksqlDB is the event streaming database purpose-built for stream processing applications that enables real-time data processing against Apache Kafka®. ksqlDB makes it easy to read, write and process streaming data […]

Typically, an enterprise service bus (ESB) or other integration solutions like extract-transform-load (ETL) tools have been used to try to decouple systems. However, the sheer number of connectors, as well […]