Architecture Patterns
Detailed reference diagrams for the data architectures and platform migrations we design and deliver — combining Snowflake, Databricks, Kafka, dbt, and more.
Lakehouse Architecture
Unified platform — lake economics + warehouse performance
Combines the low-cost scalable storage of a data lake with the ACID transactions, schema enforcement, and query performance of a data warehouse. Open table formats (Iceberg, Delta Lake) sit atop object storage, serving SQL analytics, ML, and BI from a single copy of data.
Medallion Architecture
Bronze → Silver → Gold progressive data refinement
A layered data quality pattern where raw data lands in Bronze, is cleaned and conformed in Silver, and aggregated into trusted business metrics in Gold. Each layer is independently queryable, enabling traceability back to raw source data at any point.
Data Mesh
Domain-owned data products with federated governance
Decentralises data ownership to business domains (Marketing, Sales, Product, Finance), each producing and owning their own data products. A federated governance layer enforces shared standards and contracts without centralising control — enabling scale without a monolithic data team.
Streaming Lakehouse
Real-time ingestion into the lakehouse — sub-second analytics
Extends the lakehouse with a streaming layer: events flow from producers through Apache Kafka, are processed and enriched by Flink or Spark Streaming, and land directly into Iceberg or Delta Lake tables. The same tables serve both real-time dashboards and offline ML training.
Composable Data Platform
Best-of-breed modular stack — each layer independently swappable
Replaces monolithic data platforms with a pipeline of best-of-breed, independently swappable modules: Airbyte for ingestion, Snowflake/Databricks for storage, dbt for transformation, Airflow for orchestration, and Hex/Metabase for serving. No vendor lock-in at any layer.
Data Fabric
AI-powered unified data management across hybrid infrastructure
A metadata-driven architecture that uses AI and machine learning to discover, classify, and connect data across cloud, on-premise, and SaaS systems. A central active metadata hub (Atlan, Collibra, DataHub) provides lineage, governance, and policy enforcement across all silos.
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