1. The problem — VMageste — High-Volume Marketing Analytics SaaS
Agencies and performance teams live switching between Meta, Google and TikTok dashboards, reconciling numbers that never quite match. The platform brings those sources into one place.

2. The constraints — VMageste — High-Volume Marketing Analytics SaaS
Ingest ~3 million daily events from Meta, Google, and TikTok APIs into a multi-tenant platform while keeping OLTP latency under control and real-time dashboards responsive without bottlenecks.

3. The decision — VMageste — High-Volume Marketing Analytics SaaS
The Strangler Fig pattern allowed replacing the monolith piece by piece, keeping the old system running while each slice migrated — instead of a big-bang rewrite, which is where this kind of project usually dies.

4. The solution — VMageste — High-Volume Marketing Analytics SaaS
I led the redesign from a Node.js monolith to an event-driven microservices architecture. Kafka decouples ingestion from processing; Golang consumers handle stream processing at scale; ClickHouse absorbs analytics queries so PostgreSQL handles only transactional workloads. Strangler Fig pattern for zero-downtime migration.

5. The lesson — VMageste — High-Volume Marketing Analytics SaaS
Asynchronous ingestion over Kafka and analytical querying on a columnar store. The two workloads stopped competing for the same resource, which was the real cause of the slowness.
