PART 0 — SERIES OVERVIEW

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A Complete, Modern Guide to Building a Data Warehouse and Data Lake (Warehouse + Lakehouse Architecture)

Deep Technical Series

Modern analytics platforms are no longer simple databases feeding dashboards. Today’s data teams must combine principles of dimensional modeling, cloud warehouses, lake storage, open table formats, scalable pipelines, governance, and performance engineering. The best systems blend classical data warehousing with modern lakehouse and cloud-native capabilities.

This long-form series teaches you how to build that system from scratch.

Readers will go from designing their first dimensional model all the way to implementing a real warehouse, a high-scale data lake, a lakehouse with Delta or Iceberg tables, automated pipelines, CDC ingestion, governance, and end-to-end performance tuning.

The entire series is built on modern tools like:

  • Snowflake
  • Microsoft Fabric
  • Azure Synapse
  • Databricks
  • AWS Glue + S3 + Redshift
  • Google BigQuery
  • dbt Core / dbt Cloud
  • Apache Spark
  • Delta Lake / Iceberg
  • Fivetran
  • Airbyte

Every step works across all major platforms.
Every part includes clickable inline references and a final reference list.

What You Will Build in This Series

The complete architecture includes:

1. A fully dimensional cloud data warehouse

Designed using Kimball principles. Includes conformed dimensions, SCD Type 2, fact tables, grain definitions, and surrogate keys.

2. A multi-zone data lake using medallion design

Bronze, Silver, Gold layers stored in:

  • Delta Lake (Databricks, Fabric)
  • Iceberg (Snowflake, AWS, BigQuery)
  • Parquet (universal format)

3. Automated ingestion pipelines

Using:

  • Fivetran (SaaS ingestion and CDC)
  • Airbyte (open source connectors)
  • Snowpipe (auto-ingest to Snowflake)
  • Databricks Autoloader (incremental file ingestion)
  • AWS Glue Jobs (ETL on S3)
  • Azure Data Factory or Fabric Data Pipelines
  • BigQuery Data Transfer Service
  • AWS DMS (Change Data Capture)

4. Transformation layer (SQL, dbt, Spark)

You will build transformations using:

  • dbt Core
  • Fabric Warehouse T-SQL
  • Snowflake SQL
  • BigQuery SQL
  • Databricks SQL and PySpark
  • AWS Glue ETL (PySpark)

5. A full lakehouse implementation

You will learn:

  • Delta Lake
  • Iceberg
  • Fabric Lakehouse SQL endpoint
  • Databricks Unity Catalog
  • OneLake in Fabric

6. End-to-end BI and semantic modeling

Including:

  • Power BI (Direct Lake, Import, DirectQuery)
  • Looker semantic models
  • Tableau extracts vs live connections
  • SQL-based semantic layers (Cube, dbt metrics)

7. Governance and security

Using:

  • Microsoft Purview
  • Databricks Unity Catalog
  • AWS Lake Formation
  • Collibra or Alation

8. Performance engineering

You will apply:

  • Snowflake clustering
  • BigQuery partitioning and clustering
  • Synapse distribution keys
  • Databricks Z-ordering
  • Fabric Warehouse indexes
  • Delta/Iceberg file size optimization
  • dbt incremental modeling
  • CDC design patterns

9. Final project

You will build an end-to-end enterprise-grade analytics platform from ingestion to BI.

Series Structure (Parts 0–11)

Each part is a standalone long-form article with clickable references and a multi-level workshop.

PART 1 — Dimensional Modeling and Warehouse Design

  • Kimball design
  • Grain, facts, dimensions
  • SCD Type 2
  • Bus matrix
  • Surrogate keys
  • Modeling examples
  • Full SQL examples
  • Workshops (novice, intermediate, advanced)
    Kimball Group

PART 2 — Choosing the Architecture: Warehouse, Lake, or Lakehouse

  • Snowflake vs Fabric vs Databricks vs BigQuery vs Synapse vs Redshift
  • When each architecture fits
  • Hybrid architectures
  • Warehouse-centric vs lake-centric vs lakehouse
  • Example topologies
  • Workshops
    Databricks Lakehouse

PART 3 — Building the Data Lake (Bronze, Silver, Gold)

  • Folder structure
  • Parquet, Delta, Iceberg
  • Raw, cleaned, dimensional layers
  • File size optimization
  • Partitioning strategies
  • Auto-compaction
  • Workshops
    Delta Lake
    Apache Iceberg

PART 4 — Ingesting Data (Fivetran, Airbyte, Snowpipe, Autoloader, Glue, ADF)

  • Batch vs CDC
  • Streaming ingestion
  • SaaS connector setup
  • ETL/ELT ingestion patterns
  • Workshops
    Fivetran
    Airbyte

PART 5 — Transformations (dbt, SQL, Spark, Glue ETL)

  • Staging, Core, Dim, Fact modeling
  • SCD Type 2 with dbt
  • Fact load strategies
  • Incremental modeling
  • Lakehouse transformation strategies
  • Workshops
    dbt

PART 6 — Implementing the Warehouse (Snowflake, Fabric, Synapse, BigQuery, Redshift)

  • Physical design (clustering, partitions, distribution keys)
  • Columnstore vs rowstore
  • Warehouse tuning
  • Pricing models
  • Scalability patterns
  • Workshops
    Snowflake Architecture

PART 7 — Implementing the Lakehouse (Fabric, Databricks, AWS)

PART 8 — Semantic Layer and BI (Power BI, Looker, Tableau)

  • Semantic modeling
  • Metric definitions
  • Direct Lake vs Import vs DirectQuery
  • BI performance tuning
  • Workshops
    Power BI Semantic Models

PART 9 — Performance Optimization and Cost Engineering

  • Snowflake clustering
  • BigQuery partitioning
  • Synapse distribution keys
  • Databricks optimization
  • File pruning and compression
  • Cost strategies
  • Workshops
    BigQuery Performance

PART 10 — Governance, Security, and Metadata (Purview, Unity Catalog, Lake Formation)

  • RBAC and ABAC
  • Lineage
  • Data classification
  • Multi-cloud governance
  • Workshops
    Azure Purview

PART 11 — Full End-to-End Project (Novice → Enterprise)

You will build:

  • A working Bronze/Silver/Gold lake
  • A full dimensional warehouse
  • Automated CDC ingestion
  • dbt Core transformations
  • Delta/Iceberg-based lakehouse
  • Semantic model + BI dashboard
  • Governance layer
  • Observability and monitoring

Who This Series Is For

This series is written for:

  • SQL developers becoming data engineers
  • Data engineers becoming architects
  • BI engineers needing to modernize
  • Cloud engineers needing analytical patterns
  • Senior architects building lakehouses
  • Team leads building new analytics platforms
  • Anyone preparing for a modern data engineering job

Workshop Structure for Every Part

Each part includes three workshops:

Novice

Hands-on basics with guided code.

Intermediate

Real transformations or architecture patterns.

Advanced

Performance tuning, CDC, governance, or lakehouse patterns.

This helps all skill levels follow the series.

References (Clickable)

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