Skip to content

Latest commit

 

History

History
97 lines (62 loc) · 2.61 KB

README.md

File metadata and controls

97 lines (62 loc) · 2.61 KB
Raw

From zero to hero: end to end data applications with SQL and Jupyter

Documentation Status

Meet our contributors

Important links

Visit the Jupyter book: https://ploomber-sql.readthedocs.io/en/latest/index.html

You can explore hands on exercises via Google colab on the colabs folder in this repository.

To do so:

  1. Open Google Colab
  2. To open a Jupyter notebook from this repository, change to the GitHub tab, and paste https://github.com/ploomber/sql
  3. Press the search icon 🔍
  4. Select a notebook

Set up for contributors

First set up by following the "Setup" section of CONTRIBUTING.md. Linked here

After cloning this repository and activating your environment, view the course by building the documents.

To do so, run

jupyter-book build docs/

in your command line.

Make sure your working directory is at /sql, which is the top level of the cloned repository.

Course structure

Display the topics

Intro to SQL

  1. Connecting to database engines
  2. Making your first query
  3. Aggregate functions in SQL
  4. Joining data in SQL
  5. Combining data from multiple tables

Interactive queries and parameterization

  1. Introduction to ipywidgets
  2. Parameterize your SQL queries
  3. Make your queries interactive

Advanced querying techniques

  1. Writing subqueries
  2. Advanced joins
  3. Advanced aggregations

Visualizing your SQL queries

  1. Types of data visualizations
  2. What makes a visualization good
  3. Plotting with seaborn
  4. Plotting with plotly
  5. SQL query visualization with ggplot

Packaging your SQL project

  1. Introduction to the dataset and problem
  2. Intro to Python scripting
  3. Intro to ETL pipelines with Python and SQL
  4. Packaging your ETL pipeline with Ploomber and Docker

Introduction to dashboards and apps

  1. Connecting the ETL pipeline to a dashboard (Voila)

Mini project - movie recommender system

  1. Introduction
  2. Setting up your environment with Poetry
  3. Set up an ETL with Python, DuckDB and Ploomber
  4. Perform exploratory data analysis
  5. Set up recommender system
  6. Serving results with FastAPI and Docker
  7. Deploying your application

Deploying your SQL application

  1. Cloud-based options
  2. Automating CI/CD with GitHub actions
  3. Sample AWS deployment workflow