Transformation: dbt

Overview

This directory contains a data transformation pipeline that:

1. Takes data from Iceberg tables in the landing zone

2. Transforms it using dbt (data build tool)

3. Creates analytics-ready tables in staging and mart schemas

The pipeline runs as an AWS ECS Fargate task using a Docker container.

How It Works

Infrastructure Components

• AWS Athena: SQL query engine for data transformation

• Amazon S3: Hosts the Iceberg tables for both source and transformed data

• AWS Glue: Provides the catalog for Iceberg tables

• Amazon ECS: Orchestrates the dbt container execution

• Amazon ECR: Stores the dbt docker container image

• Terraform: Provisions and manages all infrastructure

Project Structure

pipelines/
├── transform/                     # dbt project root
│   ├── Dockerfile
│   ├── dbt_project.yml            # dbt project configuration
│   ├── sources/
│   │   ├──<source_name>.yml       # List all landing tables for a source
│   ├── models/
│   │   ├── staging/               # Staging models (first transformation layer)
│   │   └── mart/                  # Final business-ready models
│   └── ...
└── ecs_task_dbt.tf                # Terraform creating the ECS task

Data Transformation Flow

The pipeline follows these transformation layers:

1. Sources: Raw data from landing tables created by ingestion pipelines

2. Staging: Initial cleaning, type conversion, deduplication and renaming

3. Mart: Final models organized by business domain, ready for analytics and reporting

Sources

Sources are defined in the sources/ folder and reference the landing tables created by the ingestion pipelines:

sources/<source_name>.yml

sources:
  - name: <source_name>
    schema: <landing_schema>
    tables:
      - name: <source_name>__dlt_version
      - name: <source_name>__dlt_loads
      ...

You can generate this file automatically using the BoringData CLI:

cd pipelines/transform
uvx boringdata dbt import-source --source ../ingest/<source_name>-schema/

Models Structure

The dbt models follow a layered architecture pattern:

• Each folder in the models directory corresponds to a distinct schema in Athena

• models/staging/ ➡️ <environment>_staging schema in Athena

• models/mart/ ➡️ <environment>_mart schema in Athena

Development Guide

Option 1: Execute dbt Locally

For rapid development with local dbt execution:

1. Setup your environment:

   uv venv --python=python3.12
   uv pip install -r requirements.txt
   uv run dbt deps

2. Configure dbt profile: Create or update ~/.dbt/profiles.yml with:

   local:
     target: <environment>
     outputs:
       <environment>:
         type: athena
         database: awsdatacatalog
         region_name: "{{ env_var('AWS_REGION') }}"
         schema: "<environment>_staging"
         s3_staging_dir: "s3://<environment>-<region>-staging-bucket/athena"
         s3_data_dir: "s3://<environment>-<region>-staging-bucket/data"
         s3_tmp_table_dir: "s3://<environment>-<region>-staging-bucket/tmp"

3. Run dbt commands:

   export DBT_PROFILE=local
   export AWS_PROFILE=<your_profile>
   export AWS_REGION=<your_region>
   
   # Run a specific model
   uv run dbt run --select model_name
   
   # Run with Makefile shortcut
   make run-local cmd="run --select model_name"

Option 2: Execute in AWS ECS Fargate

Once your template is deployed to AWS you can run dbt in the cloud environment:

export AWS_PROFILE=<your_profile>
export ENVIRONMENT=<your_environment>
make run cmd="run"

This will trigger an ECS Fargate task to execute the specified dbt command and store results in Iceberg.

Deployment

For manual deployment:

# Set required environment variables
export AWS_PROFILE=<your_profile>
export ENVIRONMENT=<your_environment>
cd pipelines/transform

# Build and deploy
make deploy

This process:

1. Builds the Docker image locally

2. Pushes it to ECR

The next time you trigger an ECS task, it will use the latest image.

Common Commands

# Development
make run-local cmd="run"              # Run dbt locally with specified command
make run-local cmd="test"             # Run dbt tests locally
make run-local cmd="docs generate"    # Generate dbt documentation

# Cloud Execution
make run cmd="run"                    # Run dbt in ECS Fargate
make run cmd="test"                   # Run tests in ECS Fargate

# Deployment
make build                            # Build Docker image
make deploy                           # Build and deploy to ECR

Resources

• dbt Documentation
• AWS Athena User Guide
• Apache Iceberg Documentation
• BoringData CLI Guide