In this project, called BeGees, we are monitoring BeOptimized's energy consumption and production in real time.

This project utilizes several open-source packages and covers various aspects of the data lifecycle: data capture, data quality, data transfer, data storage, and data presentation. Energy data is captured through a Raspberry Pi connected to a smart meter, and the data is sent to a database via the network. To make this work, all the packages and the database have been converted into containers, orchestrated via Docker Compose. The containers and Docker are run on a NAS server and mounted to my desktop folders.

In this project, we converted the BeGees Docker Compose infrastructure into a Kubernetes cluster.

The entire application was moved from a Synology NAS/Docker Compose environment to Docker Desktop/Kubernetes on Windows 11. The application containers were placed in Pods, and the frontend and backend were set to 3 replicas to increase availability. Prometheus and Grafana are used to monitor the Kubernetes cluster, and an Ingress controller is used for routing rules. The hostname is now begees.com.

In this project, we are monitoring stock evolution and displaying the data on a dashboard using Plotly Dash.

The data source is extracted from the Yahoo Finance API and stored in an SQLite database. The data is transformed using Pandas and displayed on a dashboard with Dash. The stock list, numbers, and values are encoded in an Excel file, which is then read in a Python loop. In addition to the portfolio, a watchlist page was also created to monitor stocks that may enter the portfolio.

In this project, we are analyzing the cash flow evolution of BeOptimized and displaying the data on a dashboard using Plotly Dash.

Most of the data comes from MS Excel, with some data sourced from the BeOptimized website. We import all the data and store it in SQLite. Three pages were created:

• The Planning & Billed/Unbilled page displays the planning (customers & working days) for the current month, as well as the invoices that still need to be paid for the current month.
• The Invoice Details page shows a comparison between the current month and previous months.
• Lastly, the Yearly Evolution page is used to compare the current year with previous years.

The website monitoring project was created to track BeOptimized's website traffic accessible from a central, multi-page dashboard.

To access the data, I used the Google Analytics API with Python. On a daily basis, the data are extracted and stored into a SQLite database and surfaced into the dashboard. The dashboard is simple, showing active users over time, statistics about the users' country, operating system, search engine, and the most visited web pages. For me, this information is sufficient and allows me to save couple of clicks because I don't have to go to the Google Analytics web site anymore.

In this project, we gather job opportunities, store them, and display them on a dashboard using Plotly Dash.

There are plenty of job search engines on the market. I registered with some of them using keywords like SAS+Bruxelles or Python+Bruxelles, and then requested that they send me job opportunities via email on a daily basis. The content of the emails is always structured the same way, so depending on the source, I know how to parse their content and store it in an SQLite database. Finally, all the data is displayed on a large dashboard, allowing me to monitor and analyze all the job listings from a central location.