Product

Taurus Technical Test

From CSV files to a metrics API and an interactive dashboard

A data technical test I took from raw CSV files to a metrics API and an interactive dashboard.

Taurus's real frontend with sample data, showing the AG Grid aggregate tables of quotes, new business, conversion and premiums by tenant and by agent.

Taurus's real Plotly.js charts with sample data, showing the quote conversion rate by tenant, by agent and by region.

What it is about

A data technical test I solved in three repositories, one per layer, for loading and analyzing the CSV files, a metrics API and a dashboard to explore the metrics.

What it does

Loading the CSV files into MySQL and MariaDB, with exploratory analysis in Pandas inside a notebook.
FastAPI metrics API with async SQLAlchemy that groups the data by tenant, agent and state.
React and TypeScript dashboard with an AG Grid table to explore the records.
Plotly.js charts to compare the metrics of each group.

How it works

From files to dashboard

Data flows left to right, from CSV to the database, from the database to the API and from the API to the dashboard.

Data
- The test's CSV files
- MySQL and MariaDB
- Exploratory analysis in Pandas
API
- FastAPI with async SQLAlchemy
- Queries by tenant, agent and state
Dashboard
- React and TypeScript
- AG Grid table
- Plotly.js charts

Decisions and their costs

One layer per repository
The technical test had three parts, loading and analyzing the CSV files, a metrics API and a dashboard.
Each layer lives in its own repository, so each part could be tested on its own.
The cost: coordinating three repositories in a short test.

Repositories

Every public repository of the project, with what it holds.

2024
Taurus-FastAPITaurusSebastian-s
The technical test's metrics API, grouped by tenant, agent and state.
Python, FastAPI, SQLAlchemy
2024
Taurus-FrontendTaurusSebastian-s
A React dashboard with an AG Grid table and Plotly charts.
React, TypeScript, AG Grid, Plotly.js
2024
Taurus-DataAnalysisTaurusSebastian-s
Loading the CSV files into MySQL and exploratory analysis in a notebook.
Python, Pandas, MySQL