SQL

Building Event-Driven Microservices - Chapter 12 - Lightweight Framework Microservices

Building Event-Driven Microservices - Chapter 12 - Lightweight Framework Microservices

Translations: RU
The 4th pattern to build microservices is to use Lightweight Frameworks. Lightweight Frameworks provide similar functionality to Heavyweight Frameworks, but they heavily rely on: the event broker the container management system (CMS) In many cases they exceed Heavyweight Frameworks. Different apps can use any/different resources from the cluster which are better fit their needs. While still provide Scaling and Recovering from Failures (again by heavily relying on event broker and CMS).
Building Event-Driven Microservices - Chapter 11 - Heavyweight Framework Microservices

Building Event-Driven Microservices - Chapter 11 - Heavyweight Framework Microservices

Translations: RU
Heavyweight Stream Processing Frameworks are another foundation/pattern to build your microservices. These frameworks are highly scalable and allow you to efficiently solve many analytical tasks. But they are not always good for stateful event-driven microservice application patterns. Heavyweight frameworks operate using centralized resource clusters, which may require additional operational overhead, monitoring, and coordination to integrate successfully into a microservice framework. However, recent innovations move these frameworks toward container management solutions (CMS) such as Kubernetes that should reduce your efforts.
Designing Data-Intensive Applications - Chapter 2 - Data Models and Query Languages

Designing Data-Intensive Applications - Chapter 2 - Data Models and Query Languages

Translations: RU
Earlier this year the book club of our company has studied excellent book: Martin Kleppmann - Designing Data-Intensive Applications This is the best book I have read about building complex scalable software systems. 💪 As usually I prepared an overview and mind-map. Chapter 2: What is data model. Different relations between the data. Relational, Document, Graph data models. Which one is better and when. Schema-on-write, schema-on-read (schemaless). Data locality.