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AdventureWorks ETL Teaching Lab

A reproducible ETL lab that demonstrates OLTP → dimensional modeling using AdventureWorks, Apache Airflow, and PostgreSQL. big_dataflow_chart(3)

Prerequisites

  • Ubuntu 22.04+ or Windows WSL2
  • Docker Engine ≥ 24
  • Python 3.12
  • ~4 GB RAM for containers

Source Database

The AdventureWorks .bak file is not included in this repository (too large for Git). You must download it into db-seed/ before running bootstrap.

Important: download the OLTP edition (AdventureWorks2025.bak), not the Data Warehouse edition (AdventureWorksDW2025.bak). The lab depends on the normalized transactional schema.

Official install guide: https://learn.microsoft.com/en-us/sql/samples/adventureworks-install-configure?view=sql-server-ver17&tabs=ssms

mkdir -p db-seed
curl -L -o db-seed/AdventureWorks2025.bak \
https://github.com/Microsoft/sql-server-samples/releases/download/adventureworks/AdventureWorks2025.bak

Note: the direct download URL above targets a specific GitHub release tag and may change. If it fails, find the latest .bak on the releases page: https://github.com/Microsoft/sql-server-samples/releases/tag/adventureworks

Quickstart

# 1. Clone and enter
git clone <repo-url>cd DataETL
# 2. Download the AdventureWorks backup (see "Source Database" above)
mkdir -p db-seed && curl -L -o db-seed/AdventureWorks2025.bak \
https://github.com/Microsoft/sql-server-samples/releases/download/adventureworks/AdventureWorks2025.bak
# 3. Bootstrap — creates .env from .env.example, installs deps, starts containers# Afterwards review .env if you changed any passwords from the defaults
./scripts/bootstrap.sh
# 4. Start Airflow
./scripts/start_airflow.sh
# 5. Open UI and trigger the DAG# http://localhost:8080 (admin / admin)# → Trigger: etl_dim_product# 6. Verifysource .env
.venv/bin/pytest tests/test_transform.py tests/test_extract.py -v
.venv/bin/pytest tests/test_load.py -v -m integration

Airflow Configuration

airflow/airflow.cfg is committed to this repository intentionally — it is a teaching artifact that makes Airflow settings visible and editable without students having to locate generated files.

All machine-specific paths in the file use ${AIRFLOW_HOME}, which Airflow expands at startup from the environment variable set by start_airflow.sh. No manual editing is needed after cloning.

Production note: in real deployments airflow.cfg should be excluded from version control (add to .gitignore). It is a generated file that may contain secrets. Use AIRFLOW__SECTION__KEY environment variables or a secrets backend instead.

Claude Code MCP Tool

The sql-query MCP server (tools/sql_query/) lets Claude Code query both databases directly.

.claude/settings.json is not tracked in git — it contains absolute paths specific to your machine. If you need the MCP tool, create .claude/settings.json and set the paths to match your repo location:

{
"mcpServers": {
"sql-query": {
"command": "/absolute/path/to/DataETL/.venv/bin/python",
"args": ["/absolute/path/to/DataETL/tools/sql_query/server.py"]
}
}
}

Architecture

ComponentTechnologyLocation
Source DBSQL Server 2022 (Docker, port 1433)AdventureWorks2025
WarehousePostgreSQL 16 (Docker, port 5432)dim schema
OrchestratorApache Airflow 3.2.0 (local)http://localhost:8080
SQL MCP ToolPython stdio MCP servertools/sql_query/

Repository Structure

DataETL/
docker/ Docker Compose + SQL Server restore script
airflow/dags/ Airflow DAG definitions
sql/ Extract SQL, warehouse DDL, transform reference SQL
tools/sql_query/ Universal SQL MCP server (pyodbc + psycopg2)
tests/ pytest suite — unit and integration
scripts/ bootstrap.sh / start_airflow.sh / reset_env.sh
docs/ Mapping spec, execution plan, restore guide
ralph/ Ralph autonomous agent runner
.claude/agents/ Ralph agent prompts (branch-master, hypervisor)

Tests

# Unit tests (no DB required)
.venv/bin/pytest tests/test_transform.py -v
# DB tests (requires containers up)source .env
.venv/bin/pytest tests/test_extract.py -v
# Integration tests (requires completed DAG run)
.venv/bin/pytest tests/test_load.py -v -m integration

Reset

./scripts/reset_env.sh # tears down volumes + Airflow state
./scripts/bootstrap.sh # full rebuild

Ralph Agents

# Git hygiene — groups changes into atomic conventional commits
/ralph-loop $(cat .claude/agents/branch-master.md) --completion-promise 'BRANCH CLEAN AND COMMITTED' --max-iterations 15
# Environment health check
/ralph-loop $(cat .claude/agents/hypervisor.md) --completion-promise 'ENVIRONMENT HEALTHY' --max-iterations 10

Docs

  • docs/source_to_target_mapping.md — column-level mapping for DimProduct
  • docs/etl_plan.md — execution plan and blocking dependency graph
  • docs/workflow_restore.md — restore guide for machine restarts and failures
  • PRD.md — full product requirements

Contributing

Branch off dev. Open PRs against main only from dev.

Commits must follow Conventional Commits: type(scope): description. One atomic commit per file or logical group.

Tests — run the unit suite before pushing:

.venv/bin/pytest tests/test_transform.py tests/test_transform_phase2.py -v

ETL changes — any DAG added, removed, or modified requires updating docs/dag_reference.md (canonical DAG catalog).

About

Reproducible ETL teaching lab - AdventureWorks OLTP -> star schema warehouse using Apache Airflow, SQL Server and PostgreSQL.

Topics

apache-airflowapache-airflow-etl-pipelinedata-engineeringdata-warehousedimensional-modelingetlpyteststar-schemateachingteaching-materials

Resources

Readme
Activity

Stars

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Watchers

0 watching

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Report repository

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GitHub - Kotmin/DataETL: Reproducible ETL teaching lab - AdventureWorks OLTP -> star schema warehouse using Apache Airflow, SQL Server and PostgreSQL. · GitHub
Skip to content
BranchesTags
Repository files navigation
AdventureWorks ETL Teaching Lab
A reproducible ETL lab that demonstrates OLTP → dimensional modeling using AdventureWorks, Apache Airflow, and PostgreSQL.
big_dataflow_chart(3)
Prerequisites
  • Ubuntu 22.04+ or Windows WSL2
  • Docker Engine ≥ 24
  • Python 3.12
  • ~4 GB RAM for containers
Source Database
The AdventureWorks .bak file is not included in this repository (too large for Git). You must download it into db-seed/ before running bootstrap.
Important: download the OLTP edition (AdventureWorks2025.bak), not the Data Warehouse edition (AdventureWorksDW2025.bak). The lab depends on the normalized transactional schema.
Official install guide: https://learn.microsoft.com/en-us/sql/samples/adventureworks-install-configure?view=sql-server-ver17&tabs=ssms
mkdir -p db-seed
curl -L -o db-seed/AdventureWorks2025.bak \
https://github.com/Microsoft/sql-server-samples/releases/download/adventureworks/AdventureWorks2025.bak
Note: the direct download URL above targets a specific GitHub release tag and may change. If it fails, find the latest .bak on the releases page:
https://github.com/Microsoft/sql-server-samples/releases/tag/adventureworks
Quickstart
# 1. Clone and enter
git clone <repo-url>cd DataETL
# 2. Download the AdventureWorks backup (see "Source Database" above)
mkdir -p db-seed && curl -L -o db-seed/AdventureWorks2025.bak \
https://github.com/Microsoft/sql-server-samples/releases/download/adventureworks/AdventureWorks2025.bak
# 3. Bootstrap — creates .env from .env.example, installs deps, starts containers# Afterwards review .env if you changed any passwords from the defaults
./scripts/bootstrap.sh
# 4. Start Airflow
./scripts/start_airflow.sh
# 5. Open UI and trigger the DAG# http://localhost:8080 (admin / admin)# → Trigger: etl_dim_product# 6. Verifysource .env
.venv/bin/pytest tests/test_transform.py tests/test_extract.py -v
.venv/bin/pytest tests/test_load.py -v -m integration
Airflow Configuration
airflow/airflow.cfg is committed to this repository intentionally — it is a teaching artifact that makes Airflow settings visible and editable without students having to locate generated files.
All machine-specific paths in the file use ${AIRFLOW_HOME}, which Airflow expands at startup from the environment variable set by start_airflow.sh. No manual editing is needed after cloning.
Production note: in real deployments airflow.cfg should be excluded from version control (add to .gitignore). It is a generated file that may contain secrets. Use AIRFLOW__SECTION__KEY environment variables or a secrets backend instead.
Claude Code MCP Tool
The sql-query MCP server (tools/sql_query/) lets Claude Code query both databases directly.
.claude/settings.json is not tracked in git — it contains absolute paths specific to your machine. If you need the MCP tool, create .claude/settings.json and set the paths to match your repo location:
{
"mcpServers": {
"sql-query": {
"command": "/absolute/path/to/DataETL/.venv/bin/python",
"args": ["/absolute/path/to/DataETL/tools/sql_query/server.py"]
}
}
}
Architecture
ComponentTechnologyLocation
Source DBSQL Server 2022 (Docker, port 1433)AdventureWorks2025
WarehousePostgreSQL 16 (Docker, port 5432)dim schema
OrchestratorApache Airflow 3.2.0 (local)http://localhost:8080
SQL MCP ToolPython stdio MCP servertools/sql_query/
Repository Structure
DataETL/
docker/ Docker Compose + SQL Server restore script
airflow/dags/ Airflow DAG definitions
sql/ Extract SQL, warehouse DDL, transform reference SQL
tools/sql_query/ Universal SQL MCP server (pyodbc + psycopg2)
tests/ pytest suite — unit and integration
scripts/ bootstrap.sh / start_airflow.sh / reset_env.sh
docs/ Mapping spec, execution plan, restore guide
ralph/ Ralph autonomous agent runner
.claude/agents/ Ralph agent prompts (branch-master, hypervisor)

Tests
# Unit tests (no DB required)
.venv/bin/pytest tests/test_transform.py -v
# DB tests (requires containers up)source .env
.venv/bin/pytest tests/test_extract.py -v
# Integration tests (requires completed DAG run)
.venv/bin/pytest tests/test_load.py -v -m integration
Reset
./scripts/reset_env.sh # tears down volumes + Airflow state
./scripts/bootstrap.sh # full rebuild
Ralph Agents
# Git hygiene — groups changes into atomic conventional commits
/ralph-loop $(cat .claude/agents/branch-master.md) --completion-promise 'BRANCH CLEAN AND COMMITTED' --max-iterations 15
# Environment health check
/ralph-loop $(cat .claude/agents/hypervisor.md) --completion-promise 'ENVIRONMENT HEALTHY' --max-iterations 10
Docs
  • docs/source_to_target_mapping.md — column-level mapping for DimProduct
  • docs/etl_plan.md — execution plan and blocking dependency graph
  • docs/workflow_restore.md — restore guide for machine restarts and failures
  • PRD.md — full product requirements
Contributing
Branch off dev. Open PRs against main only from dev.
Commits must follow Conventional Commits: type(scope): description. One atomic commit per file or logical group.
Tests — run the unit suite before pushing:
.venv/bin/pytest tests/test_transform.py tests/test_transform_phase2.py -v
ETL changes — any DAG added, removed, or modified requires updating docs/dag_reference.md (canonical DAG catalog).
About
Reproducible ETL teaching lab - AdventureWorks OLTP -> star schema warehouse using Apache Airflow, SQL Server and PostgreSQL. 
Topics
apache-airflowapache-airflow-etl-pipelinedata-engineeringdata-warehousedimensional-modelingetlpyteststar-schemateachingteaching-materials
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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, 'i');
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var __re = new RegExp('^' + ".*" + '
GitHub - Kotmin/DataETL: Reproducible ETL teaching lab - AdventureWorks OLTP -> star schema warehouse using Apache Airflow, SQL Server and PostgreSQL. · GitHub
Skip to content
BranchesTags
Repository files navigation
AdventureWorks ETL Teaching Lab
A reproducible ETL lab that demonstrates OLTP → dimensional modeling using AdventureWorks, Apache Airflow, and PostgreSQL.
big_dataflow_chart(3)
Prerequisites
  • Ubuntu 22.04+ or Windows WSL2
  • Docker Engine ≥ 24
  • Python 3.12
  • ~4 GB RAM for containers
Source Database
The AdventureWorks .bak file is not included in this repository (too large for Git). You must download it into db-seed/ before running bootstrap.
Important: download the OLTP edition (AdventureWorks2025.bak), not the Data Warehouse edition (AdventureWorksDW2025.bak). The lab depends on the normalized transactional schema.
Official install guide: https://learn.microsoft.com/en-us/sql/samples/adventureworks-install-configure?view=sql-server-ver17&tabs=ssms
mkdir -p db-seed
curl -L -o db-seed/AdventureWorks2025.bak \
https://github.com/Microsoft/sql-server-samples/releases/download/adventureworks/AdventureWorks2025.bak
Note: the direct download URL above targets a specific GitHub release tag and may change. If it fails, find the latest .bak on the releases page:
https://github.com/Microsoft/sql-server-samples/releases/tag/adventureworks
Quickstart
# 1. Clone and enter
git clone <repo-url>cd DataETL
# 2. Download the AdventureWorks backup (see "Source Database" above)
mkdir -p db-seed && curl -L -o db-seed/AdventureWorks2025.bak \
https://github.com/Microsoft/sql-server-samples/releases/download/adventureworks/AdventureWorks2025.bak
# 3. Bootstrap — creates .env from .env.example, installs deps, starts containers# Afterwards review .env if you changed any passwords from the defaults
./scripts/bootstrap.sh
# 4. Start Airflow
./scripts/start_airflow.sh
# 5. Open UI and trigger the DAG# http://localhost:8080 (admin / admin)# → Trigger: etl_dim_product# 6. Verifysource .env
.venv/bin/pytest tests/test_transform.py tests/test_extract.py -v
.venv/bin/pytest tests/test_load.py -v -m integration
Airflow Configuration
airflow/airflow.cfg is committed to this repository intentionally — it is a teaching artifact that makes Airflow settings visible and editable without students having to locate generated files.
All machine-specific paths in the file use ${AIRFLOW_HOME}, which Airflow expands at startup from the environment variable set by start_airflow.sh. No manual editing is needed after cloning.
Production note: in real deployments airflow.cfg should be excluded from version control (add to .gitignore). It is a generated file that may contain secrets. Use AIRFLOW__SECTION__KEY environment variables or a secrets backend instead.
Claude Code MCP Tool
The sql-query MCP server (tools/sql_query/) lets Claude Code query both databases directly.
.claude/settings.json is not tracked in git — it contains absolute paths specific to your machine. If you need the MCP tool, create .claude/settings.json and set the paths to match your repo location:
{
"mcpServers": {
"sql-query": {
"command": "/absolute/path/to/DataETL/.venv/bin/python",
"args": ["/absolute/path/to/DataETL/tools/sql_query/server.py"]
}
}
}
Architecture
ComponentTechnologyLocation
Source DBSQL Server 2022 (Docker, port 1433)AdventureWorks2025
WarehousePostgreSQL 16 (Docker, port 5432)dim schema
OrchestratorApache Airflow 3.2.0 (local)http://localhost:8080
SQL MCP ToolPython stdio MCP servertools/sql_query/
Repository Structure
DataETL/
docker/ Docker Compose + SQL Server restore script
airflow/dags/ Airflow DAG definitions
sql/ Extract SQL, warehouse DDL, transform reference SQL
tools/sql_query/ Universal SQL MCP server (pyodbc + psycopg2)
tests/ pytest suite — unit and integration
scripts/ bootstrap.sh / start_airflow.sh / reset_env.sh
docs/ Mapping spec, execution plan, restore guide
ralph/ Ralph autonomous agent runner
.claude/agents/ Ralph agent prompts (branch-master, hypervisor)

Tests
# Unit tests (no DB required)
.venv/bin/pytest tests/test_transform.py -v
# DB tests (requires containers up)source .env
.venv/bin/pytest tests/test_extract.py -v
# Integration tests (requires completed DAG run)
.venv/bin/pytest tests/test_load.py -v -m integration
Reset
./scripts/reset_env.sh # tears down volumes + Airflow state
./scripts/bootstrap.sh # full rebuild
Ralph Agents
# Git hygiene — groups changes into atomic conventional commits
/ralph-loop $(cat .claude/agents/branch-master.md) --completion-promise 'BRANCH CLEAN AND COMMITTED' --max-iterations 15
# Environment health check
/ralph-loop $(cat .claude/agents/hypervisor.md) --completion-promise 'ENVIRONMENT HEALTHY' --max-iterations 10
Docs
  • docs/source_to_target_mapping.md — column-level mapping for DimProduct
  • docs/etl_plan.md — execution plan and blocking dependency graph
  • docs/workflow_restore.md — restore guide for machine restarts and failures
  • PRD.md — full product requirements
Contributing
Branch off dev. Open PRs against main only from dev.
Commits must follow Conventional Commits: type(scope): description. One atomic commit per file or logical group.
Tests — run the unit suite before pushing:
.venv/bin/pytest tests/test_transform.py tests/test_transform_phase2.py -v
ETL changes — any DAG added, removed, or modified requires updating docs/dag_reference.md (canonical DAG catalog).
About
Reproducible ETL teaching lab - AdventureWorks OLTP -> star schema warehouse using Apache Airflow, SQL Server and PostgreSQL. 
Topics
apache-airflowapache-airflow-etl-pipelinedata-engineeringdata-warehousedimensional-modelingetlpyteststar-schemateachingteaching-materials
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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(function(){
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var __re = new RegExp('^' + ".*" + '
GitHub - Kotmin/DataETL: Reproducible ETL teaching lab - AdventureWorks OLTP -> star schema warehouse using Apache Airflow, SQL Server and PostgreSQL. · GitHub
Skip to content
BranchesTags
Repository files navigation
AdventureWorks ETL Teaching Lab
A reproducible ETL lab that demonstrates OLTP → dimensional modeling using AdventureWorks, Apache Airflow, and PostgreSQL.
big_dataflow_chart(3)
Prerequisites
  • Ubuntu 22.04+ or Windows WSL2
  • Docker Engine ≥ 24
  • Python 3.12
  • ~4 GB RAM for containers
Source Database
The AdventureWorks .bak file is not included in this repository (too large for Git). You must download it into db-seed/ before running bootstrap.
Important: download the OLTP edition (AdventureWorks2025.bak), not the Data Warehouse edition (AdventureWorksDW2025.bak). The lab depends on the normalized transactional schema.
Official install guide: https://learn.microsoft.com/en-us/sql/samples/adventureworks-install-configure?view=sql-server-ver17&tabs=ssms
mkdir -p db-seed
curl -L -o db-seed/AdventureWorks2025.bak \
https://github.com/Microsoft/sql-server-samples/releases/download/adventureworks/AdventureWorks2025.bak
Note: the direct download URL above targets a specific GitHub release tag and may change. If it fails, find the latest .bak on the releases page:
https://github.com/Microsoft/sql-server-samples/releases/tag/adventureworks
Quickstart
# 1. Clone and enter
git clone <repo-url>cd DataETL
# 2. Download the AdventureWorks backup (see "Source Database" above)
mkdir -p db-seed && curl -L -o db-seed/AdventureWorks2025.bak \
https://github.com/Microsoft/sql-server-samples/releases/download/adventureworks/AdventureWorks2025.bak
# 3. Bootstrap — creates .env from .env.example, installs deps, starts containers# Afterwards review .env if you changed any passwords from the defaults
./scripts/bootstrap.sh
# 4. Start Airflow
./scripts/start_airflow.sh
# 5. Open UI and trigger the DAG# http://localhost:8080 (admin / admin)# → Trigger: etl_dim_product# 6. Verifysource .env
.venv/bin/pytest tests/test_transform.py tests/test_extract.py -v
.venv/bin/pytest tests/test_load.py -v -m integration
Airflow Configuration
airflow/airflow.cfg is committed to this repository intentionally — it is a teaching artifact that makes Airflow settings visible and editable without students having to locate generated files.
All machine-specific paths in the file use ${AIRFLOW_HOME}, which Airflow expands at startup from the environment variable set by start_airflow.sh. No manual editing is needed after cloning.
Production note: in real deployments airflow.cfg should be excluded from version control (add to .gitignore). It is a generated file that may contain secrets. Use AIRFLOW__SECTION__KEY environment variables or a secrets backend instead.
Claude Code MCP Tool
The sql-query MCP server (tools/sql_query/) lets Claude Code query both databases directly.
.claude/settings.json is not tracked in git — it contains absolute paths specific to your machine. If you need the MCP tool, create .claude/settings.json and set the paths to match your repo location:
{
"mcpServers": {
"sql-query": {
"command": "/absolute/path/to/DataETL/.venv/bin/python",
"args": ["/absolute/path/to/DataETL/tools/sql_query/server.py"]
}
}
}
Architecture
ComponentTechnologyLocation
Source DBSQL Server 2022 (Docker, port 1433)AdventureWorks2025
WarehousePostgreSQL 16 (Docker, port 5432)dim schema
OrchestratorApache Airflow 3.2.0 (local)http://localhost:8080
SQL MCP ToolPython stdio MCP servertools/sql_query/
Repository Structure
DataETL/
docker/ Docker Compose + SQL Server restore script
airflow/dags/ Airflow DAG definitions
sql/ Extract SQL, warehouse DDL, transform reference SQL
tools/sql_query/ Universal SQL MCP server (pyodbc + psycopg2)
tests/ pytest suite — unit and integration
scripts/ bootstrap.sh / start_airflow.sh / reset_env.sh
docs/ Mapping spec, execution plan, restore guide
ralph/ Ralph autonomous agent runner
.claude/agents/ Ralph agent prompts (branch-master, hypervisor)

Tests
# Unit tests (no DB required)
.venv/bin/pytest tests/test_transform.py -v
# DB tests (requires containers up)source .env
.venv/bin/pytest tests/test_extract.py -v
# Integration tests (requires completed DAG run)
.venv/bin/pytest tests/test_load.py -v -m integration
Reset
./scripts/reset_env.sh # tears down volumes + Airflow state
./scripts/bootstrap.sh # full rebuild
Ralph Agents
# Git hygiene — groups changes into atomic conventional commits
/ralph-loop $(cat .claude/agents/branch-master.md) --completion-promise 'BRANCH CLEAN AND COMMITTED' --max-iterations 15
# Environment health check
/ralph-loop $(cat .claude/agents/hypervisor.md) --completion-promise 'ENVIRONMENT HEALTHY' --max-iterations 10
Docs
  • docs/source_to_target_mapping.md — column-level mapping for DimProduct
  • docs/etl_plan.md — execution plan and blocking dependency graph
  • docs/workflow_restore.md — restore guide for machine restarts and failures
  • PRD.md — full product requirements
Contributing
Branch off dev. Open PRs against main only from dev.
Commits must follow Conventional Commits: type(scope): description. One atomic commit per file or logical group.
Tests — run the unit suite before pushing:
.venv/bin/pytest tests/test_transform.py tests/test_transform_phase2.py -v
ETL changes — any DAG added, removed, or modified requires updating docs/dag_reference.md (canonical DAG catalog).
About
Reproducible ETL teaching lab - AdventureWorks OLTP -> star schema warehouse using Apache Airflow, SQL Server and PostgreSQL. 
Topics
apache-airflowapache-airflow-etl-pipelinedata-engineeringdata-warehousedimensional-modelingetlpyteststar-schemateachingteaching-materials
Resources
Readme
Activity
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
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var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
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var u = new URL(url, window.location.origin);
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observer.observe(document.body, { childList: true, subtree: true });
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} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - Kotmin/DataETL: Reproducible ETL teaching lab - AdventureWorks OLTP -> star schema warehouse using Apache Airflow, SQL Server and PostgreSQL. · GitHub
Skip to content
BranchesTags
Repository files navigation
AdventureWorks ETL Teaching Lab
A reproducible ETL lab that demonstrates OLTP → dimensional modeling using AdventureWorks, Apache Airflow, and PostgreSQL.
big_dataflow_chart(3)
Prerequisites
  • Ubuntu 22.04+ or Windows WSL2
  • Docker Engine ≥ 24
  • Python 3.12
  • ~4 GB RAM for containers
Source Database
The AdventureWorks .bak file is not included in this repository (too large for Git). You must download it into db-seed/ before running bootstrap.
Important: download the OLTP edition (AdventureWorks2025.bak), not the Data Warehouse edition (AdventureWorksDW2025.bak). The lab depends on the normalized transactional schema.
Official install guide: https://learn.microsoft.com/en-us/sql/samples/adventureworks-install-configure?view=sql-server-ver17&tabs=ssms
mkdir -p db-seed
curl -L -o db-seed/AdventureWorks2025.bak \
https://github.com/Microsoft/sql-server-samples/releases/download/adventureworks/AdventureWorks2025.bak
Note: the direct download URL above targets a specific GitHub release tag and may change. If it fails, find the latest .bak on the releases page:
https://github.com/Microsoft/sql-server-samples/releases/tag/adventureworks
Quickstart
# 1. Clone and enter
git clone <repo-url>cd DataETL
# 2. Download the AdventureWorks backup (see "Source Database" above)
mkdir -p db-seed && curl -L -o db-seed/AdventureWorks2025.bak \
https://github.com/Microsoft/sql-server-samples/releases/download/adventureworks/AdventureWorks2025.bak
# 3. Bootstrap — creates .env from .env.example, installs deps, starts containers# Afterwards review .env if you changed any passwords from the defaults
./scripts/bootstrap.sh
# 4. Start Airflow
./scripts/start_airflow.sh
# 5. Open UI and trigger the DAG# http://localhost:8080 (admin / admin)# → Trigger: etl_dim_product# 6. Verifysource .env
.venv/bin/pytest tests/test_transform.py tests/test_extract.py -v
.venv/bin/pytest tests/test_load.py -v -m integration
Airflow Configuration
airflow/airflow.cfg is committed to this repository intentionally — it is a teaching artifact that makes Airflow settings visible and editable without students having to locate generated files.
All machine-specific paths in the file use ${AIRFLOW_HOME}, which Airflow expands at startup from the environment variable set by start_airflow.sh. No manual editing is needed after cloning.
Production note: in real deployments airflow.cfg should be excluded from version control (add to .gitignore). It is a generated file that may contain secrets. Use AIRFLOW__SECTION__KEY environment variables or a secrets backend instead.
Claude Code MCP Tool
The sql-query MCP server (tools/sql_query/) lets Claude Code query both databases directly.
.claude/settings.json is not tracked in git — it contains absolute paths specific to your machine. If you need the MCP tool, create .claude/settings.json and set the paths to match your repo location:
{
"mcpServers": {
"sql-query": {
"command": "/absolute/path/to/DataETL/.venv/bin/python",
"args": ["/absolute/path/to/DataETL/tools/sql_query/server.py"]
}
}
}
Architecture
ComponentTechnologyLocation
Source DBSQL Server 2022 (Docker, port 1433)AdventureWorks2025
WarehousePostgreSQL 16 (Docker, port 5432)dim schema
OrchestratorApache Airflow 3.2.0 (local)http://localhost:8080
SQL MCP ToolPython stdio MCP servertools/sql_query/
Repository Structure
DataETL/
docker/ Docker Compose + SQL Server restore script
airflow/dags/ Airflow DAG definitions
sql/ Extract SQL, warehouse DDL, transform reference SQL
tools/sql_query/ Universal SQL MCP server (pyodbc + psycopg2)
tests/ pytest suite — unit and integration
scripts/ bootstrap.sh / start_airflow.sh / reset_env.sh
docs/ Mapping spec, execution plan, restore guide
ralph/ Ralph autonomous agent runner
.claude/agents/ Ralph agent prompts (branch-master, hypervisor)

Tests
# Unit tests (no DB required)
.venv/bin/pytest tests/test_transform.py -v
# DB tests (requires containers up)source .env
.venv/bin/pytest tests/test_extract.py -v
# Integration tests (requires completed DAG run)
.venv/bin/pytest tests/test_load.py -v -m integration
Reset
./scripts/reset_env.sh # tears down volumes + Airflow state
./scripts/bootstrap.sh # full rebuild
Ralph Agents
# Git hygiene — groups changes into atomic conventional commits
/ralph-loop $(cat .claude/agents/branch-master.md) --completion-promise 'BRANCH CLEAN AND COMMITTED' --max-iterations 15
# Environment health check
/ralph-loop $(cat .claude/agents/hypervisor.md) --completion-promise 'ENVIRONMENT HEALTHY' --max-iterations 10
Docs
  • docs/source_to_target_mapping.md — column-level mapping for DimProduct
  • docs/etl_plan.md — execution plan and blocking dependency graph
  • docs/workflow_restore.md — restore guide for machine restarts and failures
  • PRD.md — full product requirements
Contributing
Branch off dev. Open PRs against main only from dev.
Commits must follow Conventional Commits: type(scope): description. One atomic commit per file or logical group.
Tests — run the unit suite before pushing:
.venv/bin/pytest tests/test_transform.py tests/test_transform_phase2.py -v
ETL changes — any DAG added, removed, or modified requires updating docs/dag_reference.md (canonical DAG catalog).
About
Reproducible ETL teaching lab - AdventureWorks OLTP -> star schema warehouse using Apache Airflow, SQL Server and PostgreSQL. 
Topics
apache-airflowapache-airflow-etl-pipelinedata-engineeringdata-warehousedimensional-modelingetlpyteststar-schemateachingteaching-materials
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
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setTimeout(tryTheater, 500);
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - Kotmin/DataETL: Reproducible ETL teaching lab - AdventureWorks OLTP -> star schema warehouse using Apache Airflow, SQL Server and PostgreSQL. · GitHub
Skip to content
BranchesTags
Repository files navigation
AdventureWorks ETL Teaching Lab
A reproducible ETL lab that demonstrates OLTP → dimensional modeling using AdventureWorks, Apache Airflow, and PostgreSQL.
big_dataflow_chart(3)
Prerequisites
  • Ubuntu 22.04+ or Windows WSL2
  • Docker Engine ≥ 24
  • Python 3.12
  • ~4 GB RAM for containers
Source Database
The AdventureWorks .bak file is not included in this repository (too large for Git). You must download it into db-seed/ before running bootstrap.
Important: download the OLTP edition (AdventureWorks2025.bak), not the Data Warehouse edition (AdventureWorksDW2025.bak). The lab depends on the normalized transactional schema.
Official install guide: https://learn.microsoft.com/en-us/sql/samples/adventureworks-install-configure?view=sql-server-ver17&tabs=ssms
mkdir -p db-seed
curl -L -o db-seed/AdventureWorks2025.bak \
https://github.com/Microsoft/sql-server-samples/releases/download/adventureworks/AdventureWorks2025.bak
Note: the direct download URL above targets a specific GitHub release tag and may change. If it fails, find the latest .bak on the releases page:
https://github.com/Microsoft/sql-server-samples/releases/tag/adventureworks
Quickstart
# 1. Clone and enter
git clone <repo-url>cd DataETL
# 2. Download the AdventureWorks backup (see "Source Database" above)
mkdir -p db-seed && curl -L -o db-seed/AdventureWorks2025.bak \
https://github.com/Microsoft/sql-server-samples/releases/download/adventureworks/AdventureWorks2025.bak
# 3. Bootstrap — creates .env from .env.example, installs deps, starts containers# Afterwards review .env if you changed any passwords from the defaults
./scripts/bootstrap.sh
# 4. Start Airflow
./scripts/start_airflow.sh
# 5. Open UI and trigger the DAG# http://localhost:8080 (admin / admin)# → Trigger: etl_dim_product# 6. Verifysource .env
.venv/bin/pytest tests/test_transform.py tests/test_extract.py -v
.venv/bin/pytest tests/test_load.py -v -m integration
Airflow Configuration
airflow/airflow.cfg is committed to this repository intentionally — it is a teaching artifact that makes Airflow settings visible and editable without students having to locate generated files.
All machine-specific paths in the file use ${AIRFLOW_HOME}, which Airflow expands at startup from the environment variable set by start_airflow.sh. No manual editing is needed after cloning.
Production note: in real deployments airflow.cfg should be excluded from version control (add to .gitignore). It is a generated file that may contain secrets. Use AIRFLOW__SECTION__KEY environment variables or a secrets backend instead.
Claude Code MCP Tool
The sql-query MCP server (tools/sql_query/) lets Claude Code query both databases directly.
.claude/settings.json is not tracked in git — it contains absolute paths specific to your machine. If you need the MCP tool, create .claude/settings.json and set the paths to match your repo location:
{
"mcpServers": {
"sql-query": {
"command": "/absolute/path/to/DataETL/.venv/bin/python",
"args": ["/absolute/path/to/DataETL/tools/sql_query/server.py"]
}
}
}
Architecture
ComponentTechnologyLocation
Source DBSQL Server 2022 (Docker, port 1433)AdventureWorks2025
WarehousePostgreSQL 16 (Docker, port 5432)dim schema
OrchestratorApache Airflow 3.2.0 (local)http://localhost:8080
SQL MCP ToolPython stdio MCP servertools/sql_query/
Repository Structure
DataETL/
docker/ Docker Compose + SQL Server restore script
airflow/dags/ Airflow DAG definitions
sql/ Extract SQL, warehouse DDL, transform reference SQL
tools/sql_query/ Universal SQL MCP server (pyodbc + psycopg2)
tests/ pytest suite — unit and integration
scripts/ bootstrap.sh / start_airflow.sh / reset_env.sh
docs/ Mapping spec, execution plan, restore guide
ralph/ Ralph autonomous agent runner
.claude/agents/ Ralph agent prompts (branch-master, hypervisor)

Tests
# Unit tests (no DB required)
.venv/bin/pytest tests/test_transform.py -v
# DB tests (requires containers up)source .env
.venv/bin/pytest tests/test_extract.py -v
# Integration tests (requires completed DAG run)
.venv/bin/pytest tests/test_load.py -v -m integration
Reset
./scripts/reset_env.sh # tears down volumes + Airflow state
./scripts/bootstrap.sh # full rebuild
Ralph Agents
# Git hygiene — groups changes into atomic conventional commits
/ralph-loop $(cat .claude/agents/branch-master.md) --completion-promise 'BRANCH CLEAN AND COMMITTED' --max-iterations 15
# Environment health check
/ralph-loop $(cat .claude/agents/hypervisor.md) --completion-promise 'ENVIRONMENT HEALTHY' --max-iterations 10
Docs
  • docs/source_to_target_mapping.md — column-level mapping for DimProduct
  • docs/etl_plan.md — execution plan and blocking dependency graph
  • docs/workflow_restore.md — restore guide for machine restarts and failures
  • PRD.md — full product requirements
Contributing
Branch off dev. Open PRs against main only from dev.
Commits must follow Conventional Commits: type(scope): description. One atomic commit per file or logical group.
Tests — run the unit suite before pushing:
.venv/bin/pytest tests/test_transform.py tests/test_transform_phase2.py -v
ETL changes — any DAG added, removed, or modified requires updating docs/dag_reference.md (canonical DAG catalog).
About
Reproducible ETL teaching lab - AdventureWorks OLTP -> star schema warehouse using Apache Airflow, SQL Server and PostgreSQL. 
Topics
apache-airflowapache-airflow-etl-pipelinedata-engineeringdata-warehousedimensional-modelingetlpyteststar-schemateachingteaching-materials
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
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} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - Kotmin/DataETL: Reproducible ETL teaching lab - AdventureWorks OLTP -> star schema warehouse using Apache Airflow, SQL Server and PostgreSQL. · GitHub
Skip to content
BranchesTags
Repository files navigation
AdventureWorks ETL Teaching Lab
A reproducible ETL lab that demonstrates OLTP → dimensional modeling using AdventureWorks, Apache Airflow, and PostgreSQL.
big_dataflow_chart(3)
Prerequisites
  • Ubuntu 22.04+ or Windows WSL2
  • Docker Engine ≥ 24
  • Python 3.12
  • ~4 GB RAM for containers
Source Database
The AdventureWorks .bak file is not included in this repository (too large for Git). You must download it into db-seed/ before running bootstrap.
Important: download the OLTP edition (AdventureWorks2025.bak), not the Data Warehouse edition (AdventureWorksDW2025.bak). The lab depends on the normalized transactional schema.
Official install guide: https://learn.microsoft.com/en-us/sql/samples/adventureworks-install-configure?view=sql-server-ver17&tabs=ssms
mkdir -p db-seed
curl -L -o db-seed/AdventureWorks2025.bak \
https://github.com/Microsoft/sql-server-samples/releases/download/adventureworks/AdventureWorks2025.bak
Note: the direct download URL above targets a specific GitHub release tag and may change. If it fails, find the latest .bak on the releases page:
https://github.com/Microsoft/sql-server-samples/releases/tag/adventureworks
Quickstart
# 1. Clone and enter
git clone <repo-url>cd DataETL
# 2. Download the AdventureWorks backup (see "Source Database" above)
mkdir -p db-seed && curl -L -o db-seed/AdventureWorks2025.bak \
https://github.com/Microsoft/sql-server-samples/releases/download/adventureworks/AdventureWorks2025.bak
# 3. Bootstrap — creates .env from .env.example, installs deps, starts containers# Afterwards review .env if you changed any passwords from the defaults
./scripts/bootstrap.sh
# 4. Start Airflow
./scripts/start_airflow.sh
# 5. Open UI and trigger the DAG# http://localhost:8080 (admin / admin)# → Trigger: etl_dim_product# 6. Verifysource .env
.venv/bin/pytest tests/test_transform.py tests/test_extract.py -v
.venv/bin/pytest tests/test_load.py -v -m integration
Airflow Configuration
airflow/airflow.cfg is committed to this repository intentionally — it is a teaching artifact that makes Airflow settings visible and editable without students having to locate generated files.
All machine-specific paths in the file use ${AIRFLOW_HOME}, which Airflow expands at startup from the environment variable set by start_airflow.sh. No manual editing is needed after cloning.
Production note: in real deployments airflow.cfg should be excluded from version control (add to .gitignore). It is a generated file that may contain secrets. Use AIRFLOW__SECTION__KEY environment variables or a secrets backend instead.
Claude Code MCP Tool
The sql-query MCP server (tools/sql_query/) lets Claude Code query both databases directly.
.claude/settings.json is not tracked in git — it contains absolute paths specific to your machine. If you need the MCP tool, create .claude/settings.json and set the paths to match your repo location:
{
"mcpServers": {
"sql-query": {
"command": "/absolute/path/to/DataETL/.venv/bin/python",
"args": ["/absolute/path/to/DataETL/tools/sql_query/server.py"]
}
}
}
Architecture
ComponentTechnologyLocation
Source DBSQL Server 2022 (Docker, port 1433)AdventureWorks2025
WarehousePostgreSQL 16 (Docker, port 5432)dim schema
OrchestratorApache Airflow 3.2.0 (local)http://localhost:8080
SQL MCP ToolPython stdio MCP servertools/sql_query/
Repository Structure
DataETL/
docker/ Docker Compose + SQL Server restore script
airflow/dags/ Airflow DAG definitions
sql/ Extract SQL, warehouse DDL, transform reference SQL
tools/sql_query/ Universal SQL MCP server (pyodbc + psycopg2)
tests/ pytest suite — unit and integration
scripts/ bootstrap.sh / start_airflow.sh / reset_env.sh
docs/ Mapping spec, execution plan, restore guide
ralph/ Ralph autonomous agent runner
.claude/agents/ Ralph agent prompts (branch-master, hypervisor)

Tests
# Unit tests (no DB required)
.venv/bin/pytest tests/test_transform.py -v
# DB tests (requires containers up)source .env
.venv/bin/pytest tests/test_extract.py -v
# Integration tests (requires completed DAG run)
.venv/bin/pytest tests/test_load.py -v -m integration
Reset
./scripts/reset_env.sh # tears down volumes + Airflow state
./scripts/bootstrap.sh # full rebuild
Ralph Agents
# Git hygiene — groups changes into atomic conventional commits
/ralph-loop $(cat .claude/agents/branch-master.md) --completion-promise 'BRANCH CLEAN AND COMMITTED' --max-iterations 15
# Environment health check
/ralph-loop $(cat .claude/agents/hypervisor.md) --completion-promise 'ENVIRONMENT HEALTHY' --max-iterations 10
Docs
  • docs/source_to_target_mapping.md — column-level mapping for DimProduct
  • docs/etl_plan.md — execution plan and blocking dependency graph
  • docs/workflow_restore.md — restore guide for machine restarts and failures
  • PRD.md — full product requirements
Contributing
Branch off dev. Open PRs against main only from dev.
Commits must follow Conventional Commits: type(scope): description. One atomic commit per file or logical group.
Tests — run the unit suite before pushing:
.venv/bin/pytest tests/test_transform.py tests/test_transform_phase2.py -v
ETL changes — any DAG added, removed, or modified requires updating docs/dag_reference.md (canonical DAG catalog).
About
Reproducible ETL teaching lab - AdventureWorks OLTP -> star schema warehouse using Apache Airflow, SQL Server and PostgreSQL. 
Topics
apache-airflowapache-airflow-etl-pipelinedata-engineeringdata-warehousedimensional-modelingetlpyteststar-schemateachingteaching-materials
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
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style = document.createElement('style');
style.id = 'universal-dark-mode-style';
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background: #1a1a2e !important;
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filter: invert(1) hue-rotate(180deg) !important;
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[data-theme="light"], [data-theme="light"],
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filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
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::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
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});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
GitHub - Kotmin/DataETL: Reproducible ETL teaching lab - AdventureWorks OLTP -> star schema warehouse using Apache Airflow, SQL Server and PostgreSQL. · GitHub
Skip to content
BranchesTags
Repository files navigation
AdventureWorks ETL Teaching Lab
A reproducible ETL lab that demonstrates OLTP → dimensional modeling using AdventureWorks, Apache Airflow, and PostgreSQL.
big_dataflow_chart(3)
Prerequisites
  • Ubuntu 22.04+ or Windows WSL2
  • Docker Engine ≥ 24
  • Python 3.12
  • ~4 GB RAM for containers
Source Database
The AdventureWorks .bak file is not included in this repository (too large for Git). You must download it into db-seed/ before running bootstrap.
Important: download the OLTP edition (AdventureWorks2025.bak), not the Data Warehouse edition (AdventureWorksDW2025.bak). The lab depends on the normalized transactional schema.
Official install guide: https://learn.microsoft.com/en-us/sql/samples/adventureworks-install-configure?view=sql-server-ver17&tabs=ssms
mkdir -p db-seed
curl -L -o db-seed/AdventureWorks2025.bak \
https://github.com/Microsoft/sql-server-samples/releases/download/adventureworks/AdventureWorks2025.bak
Note: the direct download URL above targets a specific GitHub release tag and may change. If it fails, find the latest .bak on the releases page:
https://github.com/Microsoft/sql-server-samples/releases/tag/adventureworks
Quickstart
# 1. Clone and enter
git clone <repo-url>cd DataETL
# 2. Download the AdventureWorks backup (see "Source Database" above)
mkdir -p db-seed && curl -L -o db-seed/AdventureWorks2025.bak \
https://github.com/Microsoft/sql-server-samples/releases/download/adventureworks/AdventureWorks2025.bak
# 3. Bootstrap — creates .env from .env.example, installs deps, starts containers# Afterwards review .env if you changed any passwords from the defaults
./scripts/bootstrap.sh
# 4. Start Airflow
./scripts/start_airflow.sh
# 5. Open UI and trigger the DAG# http://localhost:8080 (admin / admin)# → Trigger: etl_dim_product# 6. Verifysource .env
.venv/bin/pytest tests/test_transform.py tests/test_extract.py -v
.venv/bin/pytest tests/test_load.py -v -m integration
Airflow Configuration
airflow/airflow.cfg is committed to this repository intentionally — it is a teaching artifact that makes Airflow settings visible and editable without students having to locate generated files.
All machine-specific paths in the file use ${AIRFLOW_HOME}, which Airflow expands at startup from the environment variable set by start_airflow.sh. No manual editing is needed after cloning.
Production note: in real deployments airflow.cfg should be excluded from version control (add to .gitignore). It is a generated file that may contain secrets. Use AIRFLOW__SECTION__KEY environment variables or a secrets backend instead.
Claude Code MCP Tool
The sql-query MCP server (tools/sql_query/) lets Claude Code query both databases directly.
.claude/settings.json is not tracked in git — it contains absolute paths specific to your machine. If you need the MCP tool, create .claude/settings.json and set the paths to match your repo location:
{
"mcpServers": {
"sql-query": {
"command": "/absolute/path/to/DataETL/.venv/bin/python",
"args": ["/absolute/path/to/DataETL/tools/sql_query/server.py"]
}
}
}
Architecture
ComponentTechnologyLocation
Source DBSQL Server 2022 (Docker, port 1433)AdventureWorks2025
WarehousePostgreSQL 16 (Docker, port 5432)dim schema
OrchestratorApache Airflow 3.2.0 (local)http://localhost:8080
SQL MCP ToolPython stdio MCP servertools/sql_query/
Repository Structure
DataETL/
docker/ Docker Compose + SQL Server restore script
airflow/dags/ Airflow DAG definitions
sql/ Extract SQL, warehouse DDL, transform reference SQL
tools/sql_query/ Universal SQL MCP server (pyodbc + psycopg2)
tests/ pytest suite — unit and integration
scripts/ bootstrap.sh / start_airflow.sh / reset_env.sh
docs/ Mapping spec, execution plan, restore guide
ralph/ Ralph autonomous agent runner
.claude/agents/ Ralph agent prompts (branch-master, hypervisor)

Tests
# Unit tests (no DB required)
.venv/bin/pytest tests/test_transform.py -v
# DB tests (requires containers up)source .env
.venv/bin/pytest tests/test_extract.py -v
# Integration tests (requires completed DAG run)
.venv/bin/pytest tests/test_load.py -v -m integration
Reset
./scripts/reset_env.sh # tears down volumes + Airflow state
./scripts/bootstrap.sh # full rebuild
Ralph Agents
# Git hygiene — groups changes into atomic conventional commits
/ralph-loop $(cat .claude/agents/branch-master.md) --completion-promise 'BRANCH CLEAN AND COMMITTED' --max-iterations 15
# Environment health check
/ralph-loop $(cat .claude/agents/hypervisor.md) --completion-promise 'ENVIRONMENT HEALTHY' --max-iterations 10
Docs
  • docs/source_to_target_mapping.md — column-level mapping for DimProduct
  • docs/etl_plan.md — execution plan and blocking dependency graph
  • docs/workflow_restore.md — restore guide for machine restarts and failures
  • PRD.md — full product requirements
Contributing
Branch off dev. Open PRs against main only from dev.
Commits must follow Conventional Commits: type(scope): description. One atomic commit per file or logical group.
Tests — run the unit suite before pushing:
.venv/bin/pytest tests/test_transform.py tests/test_transform_phase2.py -v
ETL changes — any DAG added, removed, or modified requires updating docs/dag_reference.md (canonical DAG catalog).
About
Reproducible ETL teaching lab - AdventureWorks OLTP -> star schema warehouse using Apache Airflow, SQL Server and PostgreSQL. 
Topics
apache-airflowapache-airflow-etl-pipelinedata-engineeringdata-warehousedimensional-modelingetlpyteststar-schemateachingteaching-materials
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