About This Architecture

Multi-cloud log and data pipeline ingesting logs and resource metrics from AWS, Azure, and GCP simultaneously into a unified Apache Pulsar-based message queue. NestJS microservices (Log Collector, Log Sinker, Data Processor, Data Collector) orchestrate ingestion, transformation, and storage across six Pulsar topics, with PostgreSQL primary-replica persistence and Redis caching for API performance. Python batch analyzers consume processed data to detect anomalies and train models, enabling real-time observability across heterogeneous cloud environments. Fork this diagram on Diagrams.so to customize topic schemas, add Kafka alternatives, or extend with additional cloud providers. The architecture demonstrates polyglot microservices (NestJS, Kotlin Spring, Python) coordinating through event streams—a best practice for scaling multi-tenant, multi-cloud observability platforms.

People also ask

How do you build a unified log aggregation and data pipeline across multiple cloud providers like AWS, Azure, and GCP?

This diagram shows a multi-cloud pipeline where AWS Cloud Logs, Azure Cloud Logs, and GCP Cloud Logs feed into a Log Collector (NestJS) that publishes to Apache Pulsar topics. NestJS microservices (Log Sinker, Data Processor, Data Collector) transform and enrich data across six topics, persist to PostgreSQL primary-replica, and enable Python batch analyzers to detect anomalies—enabling unified obs