Lambda architecture is not dead. At Fresha, we serve ~1M daily bookings through a streaming platform that has evolved for over two years, and we are just getting started. This talk shares our journey of building a cost-effective, production-ready data platform on Kafka, Snowflake, and now Iceberg and StarRocks.

Pillar 1: Ingestion - Simple but Solid
From PostgreSQL to Debezium to Kafka to Snowpipe. Data lands in Snowflake in under 2 seconds. This layer has remained untouched since day one, and that stability enabled everything else.

Pillar 2: Consolidation - Cost Effective
Here is where Lambda architecture shines. We materialize tables every 20 minutes, then merge live CDC events at query time through views. This provides deduplication, schema evolution handling, and near-real-time freshness without running expensive compute 24/7. The pattern is old. It works.

Pillar 3: Consumption - The Clever Bit
Here is what we are proud of: we use Snowflake as an API to support production load, which Snowflake is not designed for. Through smart architecture (connection pooling, query optimisation, view-based routing), we achieve Enterprise-tier capabilities on a non-Enterprise Snowflake plan. When we needed more, we extended with StarRocks and Iceberg - not replacing Snowflake, but complementing it.

What you will learn:
- Implementing query-time deduplication in Snowflake with dbt and views
- Lambda architecture patterns that handle schema evolution gracefully
- How to push Snowflake beyond its intended use case without breaking the bank
- Extending your platform with Iceberg and StarRocks while keeping Snowflake in the mix

The takeaway: You do not need the most expensive tier to build a production-grade streaming platform. Smart architecture beats premium licensing. 2+ years in production. Real patterns. Real cost savings.