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Also known as: Processed Layer, Clean Layer, Stage 2
The cleansed and conformed data layer that standardizes, validates, and deduplicates data for broad reuse.
DESCRIPTION
The Silver layer curates raw Bronze data into quality-assured, standardized datasets. Typical tasks include schema harmonization, deduplication, type casting, and applying business rules. It often produces conformed dimensions and well-structured facts ready for analytics and data science. Silver serves as the main sharing layer across domains, reducing duplication of cleansing logic. Good practice includes data quality checks, contracts, and versioning. From Silver, downstream Gold aggregates and marts are derived.
ALTERNATIVE DEFINITIONS
Databricks
The silver layer contains cleaned and enriched data that has been transformed from the raw bronze layer.
Positions Silver as cleaned and enriched relative to Bronze.
Databricks Glossary
Silver tables standardize and refine data, improving quality by removing duplicates and applying validations.
Emphasizes deduplication and validation.
Delta Lake
Medallion layers progress data from raw (bronze) to refined (silver) to business-ready (gold) for reliable analytics.
Describes the progressive refinement pattern including Silver.
RELATED TERMS
Medallion Architecture Data Quality Entity Matching Feature Scaling Dimension Data Validation Delta Lake Kimball Methodology
COMPETING TERMS
RELATIONS
- Broader: Layer
KNOWLEDGE AREAS
Data Engineering Data Architecture Data Management Data Warehousing & BI
TARGET AUDIENCE
Data Engineer Analytics Engineer Data Architect Data Steward BI Specialist
EXTERNAL SOURCES
- Wikipedia - Delta Lake
- Databricks - Reference for silver layer practices
- Delta Lake - Learning resources on layered lakes
TOP QUESTIONS
What transformations belong in the Silver Layer?
Deduplication, type normalization, validation, standardization, and applying business rules to achieve reusable, high-quality datasets.
Category: Practice
How does the Silver Layer support reuse?
By centralizing cleansing and conformance, it avoids repeated logic across teams and accelerates consumption.
Category: Relevancy
What quality checks are essential in the Silver Layer?
Uniqueness, referential integrity, null thresholds, range checks, and schema conformance are typical.
Category: BestPractices
What are risks in the Silver Layer?
Overfitting transformations to one use case can reduce generality. Missing lineage complicates audits.
Category: Risk
How does Silver differ from Bronze?
Bronze lands raw data; Silver cleans and conforms it for broad use.
Category: Comparison
How does Silver relate to dimensional models?
Silver can produce conformed dimensions and prepared facts that feed star schemas in Gold.
Category: Example
How to version Silver datasets?
Use table versioning, semantic versioning of schemas, and change logs to manage evolution safely.
Category: Management
What tools help validate Silver Layer data?
Great Expectations, Deequ, dbt tests, and Delta constraints enforce quality contracts.
Category: Tools
What is an alternative approach to a distinct Silver Layer?
Some teams co-locate cleaning with domain-owned data products, but layering remains common for governance.
Category: Alternatives