Add bump2version

.bumpversion.cfg

@@ -0,0 +1,8 @@
+[bumpversion]
+current_version = 7.2.2
+commit = True
+tag = True
+
+[bumpversion:file:pyproject.toml]
+[bumpversion:file:prefect.yaml]
+[bumpversion:file:uv.lock]

README.md

@@ -3,14 +3,14 @@
 
 This repository contains my personal data pipeline, built with [Prefect](https://www.prefect.io/), to automate data extraction, transformation, and loading (ETL). The pipeline runs on my NAS and integrates multiple data sources, processing them and exporting the results to [MotherDuck](https://motherduck.com/) for analysis in [Metabase](https://www.metabase.com/).
 
-## Pipeline Overview
+## 📌 Pipeline Overview
 
 The pipeline serves two main purposes:
 
 1. **Automation:** Automates repetitive data tasks by extracting data from APIs, cloud services, and other sources.
 2. **Analysis:** Loads transformed data into MotherDuck, where Metabase connects for visualization and insights.
 
-## Prefect Workflow
+## ⚙️ Prefect Workflow
 
 The pipeline is orchestrated with Prefect, using flows and tasks to manage dependencies and execution. The scheduling logic is defined in `schedules/hourly.py`, ensuring that jobs run on an hourly basis. The execution happens on my NAS.
 
@@ -20,7 +20,7 @@ The pipeline is orchestrated with Prefect, using flows and tasks to manage depen
 3. **Transform:** Data processing is primarily handled using `dbt`, with additional transformations as needed.
 4. **Visualization:** Metabase connects to MotherDuck to generate insights and reports.
 
-## Repository Structure
+## 📂 Repository Structure
 
 ```plaintext
 ─ vtasks
@@ -37,7 +37,7 @@ The pipeline is orchestrated with Prefect, using flows and tasks to manage depen
  └── vdbt            # dbt project for data modeling
 ```
 
-## 🚀 How to Run Locally
+## 🚀 How to Run the Pipeline Locally
 
 > **Note**  
 > This project uses **UV** for dependency management and execution.
@@ -50,7 +50,7 @@ The pipeline is orchestrated with Prefect, using flows and tasks to manage depen
    ```
 2. **Set up the virtual environment:**
    ```bash
-   uv venv --python=3.11 .venv
+   uv venv .venv
    ```
 3. **Install dependencies (in editable mode for local development):**
    ```bash
@@ -76,7 +76,7 @@ uv run python -m vtasks.jobs.dropbox.export_tables
 ```
 This ensures that **relative imports** work correctly.
 
-## Deployment
+## 📦 Deployment
 
 > **Note**
 > This pipeline is deployed manually to my NAS since I prefer not to grant GitHub Actions access to it. This ensures better control and security over the deployment process.
@@ -92,10 +92,19 @@ set PREFECT_API_URL=http://tnas:6006/api
 prefect --no-prompt deploy --all
 ```
 
-This approach provides full control over the deployment process while maintaining security and isolation.
+### 🔢 Versioning
 
-## Author
+To ensure all files reflect the correct version, use `bump2version`:
+
+```bash
+bump2version patch  # Or major/minor
+```
+
+This automates version updates across `prefect.yaml`, `pyproject.toml`, and `uv.lock`.
+
+## 👤 Author
 [Arnau Villoro](https://villoro.com)
 
-## License
+## 📜 License
 This repository is licensed under [MIT](https://opensource.org/licenses/MIT).
+