To go through this lab you will need to install the following tools in your workstation.

If you are running this in your local machine, it's highly recommendable to use a virtual environment to keep the version of your Python libraries under control (and avoid version conflicts).

You can install virtualenv using pip

python3 -m pip install --user virtualenv

Create a virtual environment from your terminal running

virtualenv {name_of_environment}

Then, activate the virtual environment running:

-- Unix (Linux/Mac) source bin/activate -- Windows venv\Scripts\activate

Once you are inside the virtual environment, install dependencies:

pip3 install -r requirements.txt

Sorted! Now let's begin by the Jupyter notebook. Run from the terminal:

jupyter notebook

We need the following libraries:

• Jupyter: Needed to work with Jupyter notebooks.
• Luigi: This library allows us to build and schedule data pipelines.

⚠️ To have access to all Luigi's features you need a UNIX machine (Linux, macOS). In this lab we will just run the pipelines using the local-scheduler mode.

• Pandas: One of the best-known Python libraries for data manipulation.
• SQL Alchemy: Library used as an interface to interact from Python with different databases.
• Psycopg: This module serves as a connector to PostgreSQL.

If you have pip installed, you can download these libraries using the requirements file in this repository as follows:

pip install -r requirements.txt

In this lab we use some basic SQL (Structured Query Language). This is the main tool to interact with the vast majority of databases. Each SQL has its own "dialect", but there is a common core to all of them.

If you are not familiar with SQL already have no worries, it's by far the easiest programming language. You can get a grasp of the basics in any of the resources below:

• Mode
• W3Schools
• SQL Zoo
• CodeCademy

Files you will find in this repository:

• Data Engineering Lab.ipynb: A Jupyter notebook with some examples of data manipulation that serves as an initial setup.
• lab_params.py: Several parameter we will use during the lab (database settings, file paths)
• lab_utils.py: A collection of functions to be reused in the lab, mostly to interact with the database (create tables, load data, run queries,retrieve data...).
• pipelines (folder): You can find here some examples of data pipelines using Luigi.
  • rpl_covid_survey.py : Downloads daily reports from API.
  • covid_survey_covid_mask.py : Joins 2 reports from different indicators (covid & mask) into a single table.
  • covid_survey_covid_mask_2.py: Similar to the previous pipeline, but using a table schema that is more escalable.
  • covid_survey_json.py: Yet another iteration on covid_survey_covid_mask, but now making it fully escalable to handle a dinamic list of rpl_covid_XXX reports as input.

• Facebook Data For Good
• The Global COVID-19 Trends and Impact Survey Open Data API
• Movement Range Maps
• High Resolution Population Density Maps