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Showing posts from March, 2023

Event Driven Microservice Architecture with AWS Kinesis

I won't get into details of what a microservice architecture is all about. There are plenty of resources on the net that can help you understand this architecture. One good resource is by Chis Richardson.  In my journey to achieving a loosely coupled services based architecture, I initially started with service decomposition and using a shared database. There are a lot of cons to this approach and it creates a single point of failure at runtime but it also creates dependencies to data models and structures especially if you don't segregate boundaries by creating different "databases", "schemas", or "namespaces" in that physically shared database.  There is a pattern called Database per-service  that provides a lot of capabilities since each service has its own database server when comes to keep your services independent and achieving greater scalability. There are some alternatives to this approach such as private tables o

Using AWS QuickSight and Athena to query DataDog Events

DataDog is an observability platform that provides a wide range of solutions for monitoring and alert of cloud scale applications. I have had the pleasure of working with DataDog these past years. I have used it to perform Application Performance Monitoring (APM), Database Monitoring (DB), Log Aggregation and recently started to use its Real User Monitoring (RUM) capabilities.  RUM is a performance monitoring process that collects detailed information about an end-users interactions with a given applications. You can use it to measure how an end-user is interacting with the page elements, detect their mouse movements and frustrations. Combined with APM tracing, RUM gives you a complete view of user activities, service traces and database interactions.  You can use all of this collected data and create dashboards and visualizations in your DataDog console. These can help anytime be alerted and you can use them to troubleshoot your applications very effectively. However, collected data i