This blog post explores why software vendors (try to) introduce new solutions for Reverse ETL, when Reverse ETL is really needed, and how it fits into the enterprise architecture. The involvement of event streaming to process data in motion is a key piece of Reverse ETL for real-time use cases.
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, and Uber explore the benefits of Kappa but also show how batch processing fits into this discussion positively without the need for Lambda.
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.
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