Data Blueprint

Why blueprint before implementation¶

Most data projects fail on architecture, not technology. Team picks Snowflake, starts building pipelines, and after 6 months: - Nobody knows what’s source of truth for “revenue” - 3 teams have 3 different definitions of “active customer” - Data quality is a disaster, nobody trusts dashboards - Pipelines fail silently, nobody knows why

Blueprint solves these problems upfront.

Discovery process¶

Week 1-2: Data Landscape Mapping - Inventory of all data sources (ERP, CRM, e-shop, DMS, spreadsheets) - Data flow mapping (who sends what where, how often, through which channel) - Consumer identification (who needs data, in what form, how often) - Qualitative assessment (where are problems, what hurts most)

Week 3: Architecture Design - Source of Truth definition for key entities (customer, order, product) - Medallion architecture (Bronze → Silver → Gold) - Technology selection based on requirements - Data governance model (ownership, quality SLA, access control)

Week 4: Roadmap - Use case prioritization by business value and technical feasibility - MVP pipeline definition (most painful use case) - Timeline and resource estimate - Risk assessment and mitigation

Medallion Architecture Design¶

For every project we design three layers:

Bronze (Raw): Exact copy of source data. Immutable, append-only. No transformation. Purpose: audit trail, reprocessing, debugging.

Silver (Cleaned): Cleaned, validated, standardized data. Defined schema, data types, constraints. Quality gates automatically monitor completeness and consistency.

Gold (Business-ready): Denormalized views optimized for consumers. Semantic layer with business metric definitions. Access controls per role/team.

Technology Selection¶

We don’t pick technology based on hype. We decide based on:

Result: architecture that makes sense for your situation, not for vendor sales team.

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