Kai Waehner

314 posts
builds cloud-native event streaming infrastructures for real-time data processing and analytics
Real Time Logistics Transportation Shipping with Apache Kafka
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  • 7 minute read

Real-Time Logistics, Shipping, and Transportation with Apache Kafka

Logistics, shipping, and transportation require real-time information to build efficient applications and innovative business models. Data streaming enables correlated decisions, recommendations, and alerts. Kafka is everywhere across the industry. This blog post explores several real-world case studies from companies such as USPS, Swiss Post, Austrian Post, DHL, and Hermes. Use cases include cloud-native middleware modernization, track and trace, and predictive routing and ETA planning.
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Real-Time Supply Chain Control Tower with Apache Kafka
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  • 6 minute read

A Real-Time Supply Chain Control Tower powered by Kafka

A modern supply chain requires just-in-time production, global logistics, and complex manufacturing processes. This blog post explores a solution that ingests all information flows into a unified central nervous system. The idea of the Supply Chain Control Tower becomes a reality: An integrated data cockpit with real-time access to all levels and systems of the supply chain.
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Real-Time Sports and Gaming with Data Streaming powered by Apache Kafka
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  • 6 minute read

Reimagine sports and gaming with data streaming: A table tennis success story built with Apache Kafka

The sports world is changing. Digitalization is everywhere. Cameras and sensors analyze matches. Stadiums get connected and incorporate mobile apps and location-based services. Players use social networks to influence and market themselves and consumer products. Real-time data processing is crucial for most innovative sports use cases. This blog post explores how data streaming with Apache Kafka helps reimagine the sports industry, showing a concrete example from the worldwide table tennis organization.
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Is Amazon MSK Serverless for Apache Kafka a Self-Driving Car or just a Car Engine
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  • 13 minute read

When NOT to choose Amazon MSK Serverless for Apache Kafka?

Apache Kafka became the de facto standard for data streaming. Various cloud offerings emerged and improved in the last years. Amazon MSK Serverless is the latest Kafka product from AWS. This blog post looks at its capabilities to explore how it relates to “the normal” partially managed Amazon MSK, when the serverless version is a good choice, and when other fully-managed cloud services like Confluent Cloud are the better option.
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Migration from Amazon Kinesis and SQS to Apache Kafka and Flink in the Cloud on AWS
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Why DoorDash migrated from Cloud-native Amazon SQS and Kinesis to Apache Kafka and Flink

Even digital natives – that started their business in the cloud without legacy applications in their own data centers – need to modernize their cloud-native enterprise architecture to improve business processes, reduce costs, and provide real-time information to their downstream applications. This blog post explores the benefits of an open and flexible data streaming platform compared to a proprietary message queue and data ingestion cloud services. A concrete example shows how DoorDash replaced cloud-native AWS SQS and Kinesis with Apache Kafka and Flink.
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Kafka versus HTTP REST API
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  • 11 minute read

Request-Response with REST/HTTP vs. Data Streaming with Apache Kafka – Friends, Enemies, Frenemies?

Request-response communication with REST / HTTP is simple, well understood, and supported by most technologies, products, and SaaS cloud services. Contrarily, data streaming with Apache Kafka is a fundamental change to process data continuously. HTTP and Kafka complement each other in various ways. This post explores the architectures and use cases to leverage request-response together with data streaming in the control plane for management or in the data plane for producing and consuming events.
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The Heart of the Data Mesh Beats Real Time with Apache Kafka
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The Heart of the Data Mesh Beats Real-Time with Apache Kafka

If there were a buzzword of the hour, it would undoubtedly be “data mesh”! This new architectural paradigm unlocks analytic and transactional data at scale and enables rapid access to an ever-growing number of distributed domain datasets for various usage scenarios. The data mesh addresses the most common weaknesses of the traditional centralized data lake or data platform architecture. And the heart of a decentralized data mesh infrastructure must be real-time, reliable, and scalable. Learn how the de facto standard for data streaming, Apache Kafka, plays a crucial role in building a data mesh.
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Best Practices for Data Analytics with AWS Azure Googel BigQuery Spark Kafka Confluent Databricks
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  • 10 minute read

Best Practices for Building a Cloud-Native Data Warehouse or Data Lake

The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems. Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. Let’s explore this dilemma in a blog series. This is part 5: Best Practices for Building a Cloud-Native Data Warehouse or Data Lake.
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Case Studies for Cloud Native Analytics with Data Warehouse Data Lake Data Streaming Lakehouse
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  • 7 minute read

Case Studies: Cloud-native Data Streaming for Data Warehouse Modernization

The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems. Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. Let’s explore this dilemma in a blog series. This is part 4: Case Studies for cloud-native data streaming and data warehouses.
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Data Warehouse and Data Lake Modernization with Data Streaming
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  • 9 minute read

Data Warehouse and Data Lake Modernization: From Legacy On-Premise to Cloud-Native Infrastructure

The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems. Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. Let’s explore this dilemma in a blog series. This is part 3: Data Warehouse Modernization: From Legacy On-Premise to Cloud-Native Infrastructure.
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