> tpl_aim_003
Enterprise Data Architecture Document
End-to-end data architecture blueprint establishing lakehouse storage tiers, stream and batch ingestion pipelines, medallion data modeling, and semantic layers.
Data architecture specification covering Medallion lakehouse tiers (Bronze/Silver/Gold), Iceberg table formats, and unified semantic metrics.
Important Tech Document Template & Operational Notice
TinyCTO.tv Tech Document Template Notice: This template is a general educational and operational starting point. It is not legal, tax, accounting, investment, procurement, regulatory, security or certification advice. Requirements vary by jurisdiction, organization, contract and risk. Review and adapt it with qualified professionals before relying on it.
Problem Solved
Siloed data lakes and brittle point-to-point ETL pipelines create high cloud compute costs, stale BI dashboards, and schema drift breakages across business units.
When to Use
- •Architecting or modernizing an enterprise data platform toward Lakehouse or Data Mesh
- •Implementing Apache Iceberg or Delta Lake open table formats to decouple compute from storage
- •Designing real-time event streaming architectures with Kafka/Flink and dbt transformations
When NOT to Use
- •For single transactional PostgreSQL schema design
- •For simple Google Analytics dashboard setup
5 Template Sections & Structural Outline
Single source of truth, separation of storage and compute, open formats, and zero copy clones.
Change Data Capture (CDC via Debezium), Kafka event buses, and serverless S3/GCS landings.
Bronze (Raw validated), Silver (Cleansed, conformant, deduped), Gold (Aggregated domain business marts).
dbt Core/Cloud modular data modeling, star schemas, dimensional modeling, and Cube/Semantic layer.
Role-based access control, column-level masking, automated partitioning, and compaction.
Completion Instructions
Independent Review Checklist
- All mandatory sections completed
- No secrets or passwords included
- Executive sponsor sign-off obtained
Enterprise Data Architecture Document - Worked Case Study
Fictional Entity: AetherData Lakehouse Platform
Real-world production case study demonstrating complete operational adoption for AetherData Lakehouse Platform.
- •Unified 18 source databases into Apache Iceberg storage format on AWS S3
- •Standardized dbt semantic metrics reducing financial reporting variance to zero
- •Cut query execution latency by 74% with automated Z-order clustering
Frequently Asked Questions
Why choose Apache Iceberg over traditional Hive or parquet partitions?
Iceberg provides ACID transactions, full schema evolution without rewrites, hidden partitioning, and time-travel querying.
What is the role of dbt in this architecture?
dbt manages the modular SQL/Python transformations from Silver to Gold tiers with built-in data quality testing and documentation.
How is data quality verified before landing in the Gold tier?
Automated dbt and Great Expectations tests validate uniqueness, referential integrity, and null checks on every build.
Download Tech Document Pack
Auth RequiredDownload all blank templates, worked scenarios, and verification manifests in a single verified archive.
Authoritative Sources
- The Medallion ArchitectureDatabricks • OFFICIAL REQUIREMENT
- Apache Iceberg: The Definitive GuideApache Software Foundation • OFFICIAL REQUIREMENT