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2026-03-05 19:13:15 +00:00
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README.md
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# data-platform
A [Dagster](https://dagster.io/) + [dbt](https://www.getdbt.com/) data platform, managed with
[uv](https://github.com/astral-sh/uv) and deployed via Docker Compose.
A personal data platform for ingesting, transforming and analysing external data sources. Built with
[Dagster](https://dagster.io/), [dbt](https://www.getdbt.com/) and
[PostgreSQL](https://www.postgresql.org/), managed with [uv](https://github.com/astral-sh/uv) and
deployed via Docker Compose.
## Stack
| Layer | Tool |
| -------------- | ---------------------------- |
| Orchestration | Dagster (webserver + daemon) |
| Transformation | dbt-core + dbt-postgres |
| Storage | PostgreSQL 16 |
| Package/venv | uv |
| Secrets | `.env` file |
| Layer | Tool |
| -------------- | ------------------------------------------------- |
| Orchestration | Dagster (webserver + daemon) |
| Transformation | dbt-core + dbt-postgres |
| Storage | PostgreSQL 16 |
| Observability | Elementary (report served via nginx) |
| CI | GitHub Actions (Ruff, SQLFluff, Prettier, pytest) |
| Package / venv | uv |
| Infrastructure | Docker Compose |
## Data pipeline
### Ingestion
Python-based Dagster assets fetch data from external APIs and load it into a raw PostgreSQL schema
using upsert semantics. Assets are prefixed with `raw_` to clearly separate unmodelled data from
transformed outputs. They are chained via explicit dependencies and run on a recurring cron
schedule.
### Transformation
dbt models follow the **staging → intermediate → marts** layering pattern:
| Layer | Materialisation | Purpose |
| ---------------- | --------------- | ---------------------------------------------------------------- |
| **Staging** | view | 1:1 cleaning of each raw table with enforced contracts |
| **Intermediate** | view | Business-logic joins and enrichment across staging models |
| **Marts** | incremental | Analysis-ready tables with derived metrics, loaded incrementally |
Mart models use dbt's **incremental materialisation** — on each run only rows with a newer ingestion
timestamp are merged into the target table. On a full-refresh the entire table is rebuilt.
All staging and intermediate models have **dbt contracts enforced**, locking column names and data
types to prevent silent schema drift.
### Data quality
- **dbt tests**: uniqueness, not-null, accepted values, referential integrity and expression-based
tests run as part of every `dbt build`.
- **Source freshness**: a scheduled job verifies raw tables haven't gone stale.
- **Elementary**: collects test results and generates an HTML observability report served via nginx.
## Scheduling & automation
Ingestion assets run on cron schedules managed by the Dagster daemon. Downstream dbt models use
**eager auto-materialisation**: whenever an upstream raw asset completes, Dagster automatically
triggers the dbt build for all dependent staging, intermediate and mart models.
Assets tagged `"manual"` are excluded from auto-materialisation and only run when triggered
explicitly. A guard condition (`~any_deps_in_progress`) prevents duplicate runs while upstream
assets are still materialising.
## Project layout
```
data_platform/ # Dagster Python package (assets, definitions)
dbt/ # dbt project (models, seeds, tests)
profiles.yml # reads credentials from env vars
dagster_home/ # dagster.yaml + workspace.yaml
Dockerfile # single image used by both dagster services
docker-compose.yaml # postgres + dagster-webserver + dagster-daemon
.env.example # copy to .env and fill in credentials
pyproject.toml # uv-managed dependencies
data_platform/ # Dagster Python package
assets/
dbt.py # @dbt_assets definition
ingestion/ # Raw ingestion assets + SQL templates
helpers/ # Shared utilities (SQL rendering, formatting, automation)
jobs/ # Job definitions
schedules/ # Schedule definitions
resources/ # Dagster resources (API clients, Postgres)
definitions.py # Main Definitions entry point
dbt/ # dbt project
models/
staging/ # 1:1 views on raw tables + source definitions
intermediate/ # Enrichment joins
marts/ # Incremental analysis-ready tables
macros/ # Custom schema generation, Elementary compat
profiles.yml # Reads credentials from env vars
dagster_home/ # dagster.yaml + workspace.yaml
tests/ # pytest test suite
nginx/ # Elementary report nginx config
docker-compose.yaml # All services
Dockerfile # Multi-stage: usercode + dagster-infra
Makefile # Developer shortcuts
```
## Getting started
@@ -36,14 +96,14 @@ curl -Lsf https://astral.sh/uv/install.sh | sh
cd ~/git/data-platform
# 3. Create your credentials file
cp .env.example .env
# Edit .env with your passwords
cp .env.example .env # edit .env with your passwords
# 4. Install dependencies into a local venv
uv sync
# 5. Generate the dbt manifest (needed before first run)
uv run dbt parse --profiles-dir dbt --project-dir dbt
uv run dbt deps --project-dir dbt --profiles-dir dbt
uv run dbt parse --project-dir dbt --profiles-dir dbt
# 6. Start all services
docker compose up -d --build
@@ -52,7 +112,7 @@ docker compose up -d --build
# http://localhost:3000
```
## Local development (without Docker)
## Local development
```bash
uv sync
@@ -60,4 +120,19 @@ source .venv/bin/activate
# Run the Dagster UI locally
DAGSTER_HOME=$PWD/dagster_home dagster dev
# Useful Make targets
make validate # Check Dagster definitions load
make lint # Ruff + SQLFluff + Prettier
make lint-fix # Auto-fix all linters
make test # pytest
make reload-code # Rebuild + restart user-code container
```
## Services
| Service | URL |
| ----------------- | --------------------- |
| Dagster UI | http://localhost:3000 |
| pgAdmin | http://localhost:5050 |
| Elementary report | http://localhost:8080 |