Data Architecture Consulting

A data lake stores raw, unprocessed data at scale but becomes a swamp without governance. A data warehouse stores structured, processed data optimised for SQL analytics but is rigid and costly to evolve. A data lakehouse (Databricks, Delta Lake) combines both: raw storage flexibility with ACID transactions, schema enforcement and warehouse-style querying — letting analytics and ML workloads run on the same data without duplication.

Medallion architecture organises data into three quality layers: Bronze (raw ingestion, nothing discarded), Silver (cleansed, conformed and joined), and Gold (business-ready aggregates for BI and ML). Each layer has explicit quality gates so issues are caught early. It replaces the ad-hoc data lake pattern where raw files and analytics tables live together with no clear ownership or quality standard.

Data mesh makes sense when multiple business domains (marketing, finance, logistics) each produce and consume significant data, and a central data team has become a bottleneck. The pattern distributes data ownership to domain teams while enforcing federated governance standards. It is an organisational and architectural decision — not just a technology one — so we always assess whether the team structure supports it before recommending it.

Databricks is the stronger choice when ML, data science and streaming workloads need to run on the same data as analytics, or when you need Delta Lake's unified batch and streaming architecture. Snowflake is better when SQL analytics and BI reporting are the primary use case and ML is secondary. Many organisations run both: Snowflake for governed analytics, Databricks for ML feature engineering and streaming.

A greenfield lakehouse implementation on Databricks with medallion architecture takes 10–16 weeks for a medium-complexity environment. If migrating from an existing data lake or warehouse, add 4–8 weeks for parallel-run validation. We always implement incrementally — Bronze layer first, then Silver and Gold — so early consumers get value before the full architecture is complete.