🚌 DataJourney

🪶Short version

Design- first Open Source Data Management Toolkit. Simplifies data workflows with modular, reproducible solutions

🌲Long version

DataJourney demonstrates how organizations can effectively manage and utilize data by harnessing the power of open-source technologies. It's designed to help navigate the complex landscape of data tools, offering a structured approach to building scalable, and reproducible data workflows.

Built on open-source principles, the framework guides users through essential steps—from identifying goals and selecting tools to testing and customising workflows. With its flexible, modular design, DataJourney can be tailored to individual needs, making it an invaluable toolkit for data professionals.

🧱 Design Philosophy (LEGO)

Built with additive, subtractive capabilities glued with open source. Each layer has a certain strength of communication inbuilt

• PO (Base): Static home(s) to keep it together (GitHub)
• P1 (Tooling): Tooling, strings (Powered by open source)
• P2 (Maintenance + Monitoring): Env, automations (Pixi + GHA)
• P3 (Abstraction): Layer(s), CLI/task manager for users to interact with (Pixi)

🛠 Current workflows covered

{✨= Experimental, ✅ = Implemented}

Status	Workflow Description
✅	Python Packaging framework design principles
✅	GitHub actions configured
✅	Vale.sh configured at PR level
✅	Pre-commit hooks configured for code linting/formatting
✨	Hello world LLM design example based on LangChain
✅	Environment management via pixi
✅	Reading data from online sources using intake
✅	Sample pipeline built using Dagster
✅	Building Dashboard using holoviews + panel
✅	Exploratory data analysis (EDA) using mito
✅	Web UI build on Flask
✅	Web UI re-done and expanded with FastHTML
✅	Leverage AI models to analyse data GitHub AI models Beta

☕️ Quickly getting started with DataJourney

• Clone DJ [email protected]:sayantikabanik/DataJourney.git
• Generate & add GITHUB_TOKEN, instructions here
  • Added requirement to run the LLM based workflows
• Switch directory cd DataJourney
• Download pixi : prefix.dev
• Activate env: pixi shell
• Install DJ framework locally pixi run DJ_package
• List all the tasks: pixi task list
• Execute a task from the list: pixi run <TASK>
• Execute a task with verbosity enabled: pixi run -v <TASK>

🏃🏽‍♀️ Active tasks under DJ

Task Name	Description
GIT_TOKEN_CHECK	Verifies the availability and validity of the Git authentication token.
DJ_package	Prepares and builds the Python package for the DataJourney project.
DJ_pre_commit	Runs pre-commit hooks to ensure code quality and adherence to standards.
DJ_dagster	Sets up and runs a Dagster workflow for orchestration in the project.
DJ_fasthtml_app	Executes a FastAPI-based HTML application.
DJ_flask_app	Configures and runs a Flask-based application for data services.
DJ_mito_app	Launches the Mito application for interactive data analysis in notebooks.
DJ_panel_app	Executes a Panel dashboard app for data visualization and analytics.
DJ_llm_analysis	Performs analysis using large language models (LLMs) on project data.
DJ_hello_world_langchain	Sets up a basic LangChain app as a "Hello World" example for LLMs.
DJ_sync_dataset_trees	Downloads and synchronizes the trees.csv dataset into the project structure.

Let me know if you'd like refinements or if there's anything you'd like to add! 🚀

🔌 About pre-commit-hooks and activating

Just like the name suggests, pre-commit-hooks are designed to format the code based on PEP standards before committing. More details

pixi run DJ_pre_commit

🦭 Executing LLM script: Generate stock price recommendations

pixi run DJ_llm_analysis

🪼 Execute pre-configured Dagster pipeline

pixi run DJ_dagster

🐙 Panel app

pixi run DJ_panel_app

NOTE: The dashboard generated is exported into HTML format and saved as stock_price_twilio_dashboard

🐵 Mito

To explore further visit trymito.io

pixi run DJ_mito_app

🦋 Display all data sources present via web UI

# Run FastHTML app
pixi run DJ_fasthtml_app