Awesome

Mara Example Project 1

A runnable app that demonstrates how to build a data warehouse with mara. Combines the mara-pipelines and mara-schema libraries with the mara-app framework into a project.

The example ETL integrates publicly available e-commerce and marketing data into a more general modeling and structure for highlighting the capabilities of the Mara framework.

The repository is intended to serve as a template for new projects.

Getting started

Sytem requirements

Python >=3.6 and PostgreSQL >=10 and some smaller packages are required to run the example (and mara in general).

Mac:

$ brew install -v python3
$ brew install -v dialog
$ brew install -v coreutils
$ brew install -v graphviz

Ubuntu 16.04:

$ sudo apt install git dialog coreutils graphviz python3 python3-dev python3-venv

Mara does not run Windows.

On Mac, install Postgresql with brew install -v postgresql. On Ubuntu, follow these instructions. Also, install the cstore_fdw with brew install cstore_fdw and postgresql-hll extensions from source.

To optimize PostgreSQL for ETL workloads, update your postgresql.conf along this example.

Start a database client with sudo -u postgres psql postgres and then create a user with CREATE ROLE root SUPERUSER LOGIN; (you can use any other name).

Installation

Clone the repository somewhere and hit make in the root directory of the project. This will:

create a virtual environment in .venv,
install all packages from requirements.txt.freeze (if you want to create a new requirements.txt.freeze from requirements.txt, then run make update-packages),
copy the file app/local_setup.py.example to app/local_setup.py, which you can adapt to your machine.
create the necessary databases and a number of tables that are needed for running mara.
store the Olist e-commerce and marketing data in the olist_ecommerce PostgreSQL database, locally.

You can now activate the virtual environment with

$ source .venv/bin/activate

To list all available flask cli commands, run flask without parameters.

Running the web UI

$ flask run --with-threads --reload --eager-loading

The app is now accessible at http://localhost:5000.

Running the ETL

For development, it is recommended to run the ETL from the web UI (see above). On production, use flask mara_pipelines.ui.run to run a pipeline or a set of its child nodes.

The command mara_pipelines.ui.run_interactively provides an ncurses-based menu for selecting and running pipelines.

Documentation

Documentation is work in progress. But the code base is quite small and documented.