For decades, enterprise analytics relied on centralized relational database management systems (RDBMS) such as Oracle, SQL Server, or monolithic PostgreSQL instances. As transactional volumes explode and unstructured data becomes central to operations, these architectures become prohibitively expensive bottlenecks.
Migrating to a modern decoupled lakehouse is no longer a luxury; it is an operational imperative for organizations seeking real-time analytical capabilities.
Phase 1: Ingestion & Change Data Capture (CDC)
The cardinal rule of database migration is zero operational downtime. Rather than performing risky bulk database dumps during scheduled maintenance windows, we implement log-based Change Data Capture using Debezium and Apache Kafka.
By parsing the database transaction log (WAL or binlog), every row insertion, update, and deletion is streamed continuously to cloud object storage without introducing query load on production OLTP databases.
Phase 2: The Medallion Lakehouse Architecture
Once ingested into cloud storage (S3 or Google Cloud Storage), data is organized into three distinct tiers using Apache Iceberg or Delta Lake:
- Bronze (Raw): Append-only raw ingested events preserved in their original format for disaster recovery and full replayability.
- Silver (Cleaned): Deduplicated, schema-enforced, and structurally normalized tables ready for exploratory queries.
- Gold (Aggregated): High-performance dimensional models optimized for BI dashboards, reporting, and ML feature stores.