What This Is
Python handles the automation, monitoring, and data processing work that sits alongside our primary web application stack. Where PHP and Laravel power the web applications and APIs, Python fills the gaps — standalone scripts, monitoring daemons, data pipelines, and integrations where Python’s ecosystem offers the best tooling for the job.
We use Python in production for our Beacon Bits product — a monitoring system for standalone processes that would otherwise run invisibly. The Python codebase handles scheduled task execution, health reporting, and lifecycle management for unattended processes across client infrastructure. Python’s strength here is its suitability for scripting and automation work that does not need a full web framework.
Python is not our primary language for web applications — that is PHP with Laravel. But for automation scripts, data transformation, monitoring agents, and any work that benefits from Python’s scientific computing or machine learning libraries, Python is the right tool and we write it with the same engineering standards as our PHP code.
When You Need This
Python is the right choice when your project involves automation, data processing, or monitoring where Python’s ecosystem has a clear advantage. Common scenarios:
- You need automation scripts that run on schedules, process data, and report results — not full web applications
- Monitoring or agent software that runs as a background daemon, collecting metrics and reporting to a central system
- Data processing pipelines that transform, clean, or analyse data from multiple sources
- API integrations where Python’s HTTP libraries and ecosystem packages provide the most efficient path
- Your project touches machine learning or data science and needs Python’s NumPy, pandas, or scikit-learn ecosystem
- You need a CLI tool for internal use that automates repetitive operational tasks
This is not the right choice for building web applications with user interfaces. Use PHP with Laravel for web applications and React for frontends.
How We Work
Our Python development follows structured engineering practices. Code is organised into packages with clear module boundaries, typed with Python’s type hints, and formatted with Black or Ruff for consistent style. Dependencies are managed with pip and requirements files, with virtual environments isolating project dependencies.
Testing uses pytest with fixtures, parameterised tests, and mocking where needed. Scripts and monitoring agents are tested against their expected inputs and outputs, with integration tests verifying external API communication. CI pipelines run tests and linting on every commit.
Deployment depends on the use case. Monitoring agents deploy to client infrastructure with systemd service management. Scripts run on schedules via cron or task schedulers. Data pipelines deploy to environments where they can access the data sources they process. Each deployment model has its own configuration and update mechanism.
What You Get
- Automation scripts — scheduled tasks that process data, call APIs, generate reports, and handle operational work
- Monitoring agents — background daemons that watch processes, collect metrics, and report to central dashboards
- Data processing — ETL pipelines, data transformation, and batch processing for large datasets
- CLI tools — command-line utilities for internal operational tasks
- API integration scripts — standalone integrations that bridge systems outside your main web application
- Typed, tested code — Python type hints, pytest suites, and CI enforcement
Technologies We Use
- Python 3.10+ — modern Python with type hints, dataclasses, and async support
- pytest — testing framework with fixtures and parameterised test support
- Requests / httpx — HTTP client libraries for API integration work
- systemd — service management for long-running monitoring agents
- pip / venv — dependency management and environment isolation
Related Systems
Python work often connects back to our Laravel-based systems. Beacon Bits agents report to API endpoints served by the Laravel backend. Data processing scripts feed results into PostgreSQL databases queried by web applications. The two languages complement rather than compete.
Talk to Us About Python Development
If you need automation, monitoring, or data processing work built in Python with proper engineering standards, get in touch and we will scope the project.
