Part 0 — Series Overview: A Complete Guide to Building a Modern Data Warehouse and Lakehouse
Modern analytics platforms are no longer simple databases feeding dashboards. Today’s data teams must combine dimensional modeling, cloud warehouses, lake storage, open table formats, scalable pipelines, governance, and performance engineering into a single coherent system. The best platforms blend classical data warehousing principles with modern lakehouse and cloud-native capabilities.
This series teaches you how to build that system from scratch — from your first dimensional model through to a fully governed, production-grade analytics platform. Every concept is taught with working code examples that run on all major platforms. Every part includes three workshop tiers so readers at every skill level can follow along and build real things.
Who this series is for: SQL Developers Data Engineers BI Engineers Data Architects Platform Engineers — anyone building or modernizing an analytical data platform, from first data warehouse through to enterprise lakehouse.
The Complete Series — Parts 0 Through 14
The series is structured so each part builds on the previous one — but every article is also a standalone reference you can read independently. Parts 1 through 6 establish the foundation. Parts 7 and 8 implement the physical platforms. Parts 9 through 13 add the operational layers. Part 14 assembles everything into a complete end-to-end project.
What You Will Build in This Series
By the end of Part 14 you will have built — from scratch — a complete, production-grade analytics platform. Here is exactly what that includes:
Dimensional Cloud Data Warehouse
Kimball-designed star schema with conformed dimensions, SCD Type 2, fact tables, grain definitions, and surrogate keys. Implemented across Snowflake, Fabric, BigQuery, and Redshift.
Multi-Zone Data Lake (Medallion)
Bronze, Silver, and Gold layers in Delta Lake (Databricks, Fabric), Iceberg (Snowflake, AWS, BigQuery), and Parquet. Production folder structure, partitioning, and compaction.
Automated Ingestion Pipelines
Fivetran, Airbyte, Snowpipe, Databricks Autoloader, AWS Glue, Azure Data Factory, BigQuery Transfer Service, and AWS DMS for CDC. All patterns including batch, incremental, and streaming.
Transformation Layer
dbt Core staging, incremental, and mart models. SQL MERGE patterns for Snowflake, Fabric, BigQuery. PySpark transformations for Databricks and AWS Glue.
Full Lakehouse Implementation
Delta Lake with ACID transactions, Iceberg table design, Fabric Lakehouse SQL endpoint, Databricks Unity Catalog, and OneLake. Multi-cloud patterns covered.
Semantic Layer and BI
Power BI semantic models with DAX, Looker LookML, dbt Metrics with MetricFlow, Databricks SQL views. Direct Lake, Import, and DirectQuery patterns explained.
Governance and Security
Microsoft Purview, Databricks Unity Catalog, AWS Lake Formation. RBAC, RLS, CLS, data masking, PII tagging, lineage, audit logging, and compliance frameworks.
Performance Engineering
Snowflake clustering, BigQuery partitioning, Databricks Z-ordering, Fabric Warehouse indexes, Delta file compaction, materialized views, warehouse sizing, cost optimization.
The End-to-End Architecture
Every part of this series maps to one or more layers in the architecture below. The medallion pattern — Bronze, Silver, Gold — is the universal structure that works across every platform covered. Whether you implement it in Databricks, Fabric, Snowflake, or BigQuery, the layers and their responsibilities are the same.
The Tools Covered in This Series
Every step in this series works across all major platforms. Examples are given for each platform so you can follow along regardless of which cloud or warehouse you use.
Warehouses and Lakehouses
Storage Formats and Table Layers
Ingestion and Pipelines
Transformations and Modeling
BI and Semantic Layers
Orchestration, Quality, and Governance
Why This Approach — Key Principles of the Series
Every architectural decision in this series follows the same set of principles. Understanding these principles helps you adapt the patterns to your own environment rather than copying them blindly.
| Principle | Why It Matters |
|---|---|
| ELT over ETL | Modern warehouses and lakehouses handle heavy transforms efficiently. Push computation to the engine closest to the data. |
| Medallion architecture is universal | Bronze → Silver → Gold works on every platform. It unlocks reliability, readability, governance, and flexibility regardless of the tool. |
| Dimensional modeling still matters | Even in lakehouses and ML systems, a clean dimensional model makes analytics faster, more consistent, and easier to govern. |
| The semantic layer owns the metrics | Business logic must not live in dashboards. Define KPIs once in the semantic layer and reuse them everywhere. |
| Data quality must be automated | Manual validation does not scale. Quality gates block bad data before it reaches Gold. |
| Governance is not optional | Security, masking, lineage, and auditing form the backbone of organizational trust in data. |
| Performance engineering is deliberate | Partitioning, compaction, and clustering decisions made at design time prevent expensive rewrites later. |
| Orchestration is the glue | Every pipeline fails without reliable scheduling, dependency management, and automated recovery. |
How Workshops Work in This Series
Every part includes three workshop tiers. Each tier is a hands-on exercise that applies the concepts from that part. You can start at any tier — the novice workshop teaches the same concepts as the advanced, just with more guidance and a simpler scope.
Novice
Guided hands-on basics. Focused scope. Step-by-step instructions with expected output at each step. Designed for someone building this concept for the first time.
Intermediate
Real transformation or architecture patterns. Assumes comfort with SQL and basic platform knowledge. Introduces incremental logic, CDC patterns, or multi-table designs.
Advanced
Performance tuning, CDC, governance, or lakehouse patterns at production scale. Mirrors what top-tier enterprises deploy. Designed for senior engineers and architects.
Recommended Reading Order
If you are new to data warehousing, start at Part 1 and read in order through Part 6. These parts build the conceptual and technical foundation that everything else depends on. Parts 7 and 8 are where you pick your platform and implement the physical system. Parts 9 through 13 add the operational layers. Part 14 is the capstone project that ties everything together.
If you are an experienced data engineer looking for specific reference material, every part is designed as a standalone article. Jump directly to the part most relevant to your current problem. The series index at the top of every article links back to this page so you can always find related content.
Platform coverage: This series covers Snowflake, Microsoft Fabric, Databricks, Google BigQuery, Azure Synapse, and Amazon Redshift. Code examples are provided for each platform where the implementation differs. You do not need access to all platforms — follow the examples for the platform you use and the concepts transfer directly.
References
- Kimball Group — Dimensional Modeling Techniques
- Databricks — Medallion Architecture
- Delta Lake Documentation
- Apache Iceberg
- Apache Parquet
- Snowflake — Key Architecture Concepts
- Microsoft Fabric — Lakehouse Overview
- Databricks — Lakehouse Platform
- Google BigQuery Documentation
- dbt Documentation
- AWS Glue Documentation
- Apache Airflow Documentation
- Great Expectations Documentation
- Databricks — Unity Catalog
- Microsoft Purview
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