Medallion Architecture
Bronze, silver and gold layers, and what each layer is allowed to guarantee.
5 to work through
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beginner Multiple choice
A platform lands raw order events in bronze, types and cleans them in silver, and builds a revenue table in gold. Finance finds that orders in three currencies have been converted at the wrong rate since July - the conversion is a CASE expression in the silver transformation. Which correction is the layering there to make possible?
2 min answer -
beginner
A team proposes bronze, silver and gold layers for a pipeline that ingests one CSV daily and produces one report. Is that right?
2 min answer -
intermediate
A data platform organises tables into bronze, silver and gold layers. What determines what belongs in each, and what is the common failure?
2 min answer -
intermediate
A team adopts bronze/silver/gold layering and every layer becomes a copy of the previous one with minor changes. What should each layer actually guarantee?
2 min answer -
intermediate
What does a layered bronze-silver-gold structure actually buy, and where does it become ceremony?
2 min answer
3 terms in this topic
Bronze Layer Contract
What the raw landing zone promises and refuses to promise — a faithful replayable copy of the source, with no correction applied.
conceptLayer Contract
The rule that a data layer is defined by the guarantee it makes to consumers, not by its position in a naming convention - without which the layers c…
patternMedallion Architecture
Layering a data platform into raw, cleaned and business-ready zones so that reprocessing is always possible and quality improves in defined steps.
1 artifact you would hand over
Neighbouring topics
Data Platform Architecture
General material on designing the analytical data estate end to end.
Open Table Formats
Iceberg, Delta and Hudi — transactions, snapshots and time travel over object storage.
Warehouse, Lake & Lakehouse
Three answers to where analytical data lives, and the workloads that separate them.
Storage Layout & Partitioning
Partition keys, clustering, and the scan the query planner is left able to skip.
File Formats & Compaction
Columnar formats, the small-file problem, and the maintenance nobody schedules.
Ingestion Patterns
Full load, incremental, append-only and merge, and the source system each one suits.
CDC Pipeline Design
Building on a change stream: snapshot plus delta, tombstones, and merge into the target.
Batch Orchestration
DAGs, dependencies, retries, and the difference between a schedule and an orchestration.
Workflow Schedulers
Airflow, Dagster and their kin — where the control plane sits and what it can recover.
Transformation Frameworks
Declarative SQL transformation with tests, lineage and versioned models.
Dimensional Modelling
Facts, dimensions, grain, and the star schema's continued relevance.
Data Vault Modelling
Hubs, links and satellites, and the auditability and load parallelism they buy.
Slowly Changing Dimensions
Overwriting, versioning or timestamping attribute history, and the reporting each enables.
Analytics Cost Control
Scanned bytes, idle warehouses, and the query nobody knew was running hourly.
Workload Isolation
Keeping an analyst's query off the pipeline's compute, and both off the dashboard's.
Data Platform Tenancy
Multiple domains on shared storage and compute, with separable access and cost.
Reverse ETL
Pushing modelled analytical data back into operational systems, and who owns it then.
Data Virtualisation
Querying across sources without moving data, and the performance ceiling that imposes.
Warehouse Migration
Moving off a legacy warehouse with thousands of reports pointed at it.