data lake Archives - Kai Waehner

Streaming Data Exchange with Apache Kafka and Data Mesh in Motion

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10 minute read

Streaming Data Exchange with Kafka and a Data Mesh in Motion

ByKai Waehner
14. November 2021

Data Mesh is a new architecture paradigm that gets a lot of buzzes these days. This blog post looks into this principle deeper to explore why no single technology is the perfect fit to build a Data Mesh. Examples show why an open and scalable decentralized real-time platform like Apache Kafka is often the heart of the Data Mesh infrastructure, complemented by many other data platforms to solve business problems.

Kappa Architecture vs Lambda Architecture for Apache Kafka Pulsar Data Lakes

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17 minute read

Kappa Architecture is Mainstream Replacing Lambda

ByKai Waehner
23. September 2021

Real-time data beats slow data. That’s true for almost every use case. Nevertheless, enterprise architects build new infrastructures with the Lambda architecture that includes separate batch and real-time layers. This blog post explores why a single real-time pipeline, called Kappa architecture, is the better fit. Real-world examples from companies such as Disney, Shopify, Uber, and Twitter explore the benefits of Kappa but also show how batch processing fits into this discussion positively without the need for Lambda.

Serverless Kafka for Data in Motion as Rescue for Data at Rest in the Data Lake

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12 minute read

Serverless Kafka in a Cloud-native Data Lake Architecture

ByKai Waehner
25. June 2021

Apache Kafka became the de facto standard for processing data in motion. Kafka is open, flexible, and scalable. Unfortunately, the latter makes operations a challenge for many teams. Ideally, teams can use a serverless Kafka SaaS offering to focus on business logic. However, hybrid scenarios require a cloud-native platform that provides automated and elastic tooling to reduce the operations burden. This blog post explores how to leverage cloud-native and serverless Kafka offerings in a hybrid cloud architecture. We start from the perspective of data at rest with a data lake and explore its relation to data in motion with Kafka.