Python

Streamlining Data Pipeline Reliability: The Write-Audit-Publish Pattern

Introduction: Why Safe Data Pipelines Matter In the world of data engineering, there’s a constant challenge we all face: how do we ensure our production data remains reliable and error-free when deploying updates? Anyone who’s experienced the cold sweat of a bad deployment affecting critical business data knows this pain all too well. Enter the Write-Audit-Publish pattern—a robust approach that can significantly reduce the risk of data pipeline failures. This pattern, which shares DNA with the well-known Blue-Green deployment strategy from software engineering, creates a safety net that can save your team countless hours of troubleshooting and emergency fixes.

Sunday, May 18, 2025 Read

Setting Up Your Data Engineering Environment on Windows

Introduction Setting up a development environment for data engineering on Windows requires some specific considerations that differ from Unix-based systems. This guide will walk you through creating a robust Python development environment on Windows, with detailed explanations of each component and why it’s important. Clean Slate: Removing Existing Python Installations Before starting, it’s important to remove any existing Python installations to avoid conflicts: Open Windows Settings > Apps > Apps & Features Search for “Python” Uninstall any Python versions listed Also check and remove Python from these locations:

Monday, February 3, 2025 Read

Setting Up Your Data Engineering Environment on MacOS

Introduction Setting up a development environment for data engineering on MacOS requires careful consideration of package management, Python version control, and tool configuration. This guide will walk you through the process, explaining not just how to set up these tools, but why each component is important. Clean Slate: Removing Existing Python Installations Before we begin, it’s important to ensure we’re starting with a clean slate. Multiple Python installations can cause confusion and conflicts. Let’s remove any existing Python installations:

Sunday, February 2, 2025 Read

Automating Python Virtual Environments with Zsh on macOS

Automating Python Virtual Environments with Zsh on macOS Managing Python virtual environments can be tedious - having to manually activate and deactivate them as you move between projects. I decided to create a script that will automatically activate virtual environment. When you cd into a directory containing a .venv folder. When you leave, it will deactivate it. Installing Zsh While macOS comes with zsh as the default shell since Catalina (10.15), you may want to ensure you have the latest version. The easiest way to install or update zsh is using Homebrew:

Tuesday, January 14, 2025 Read

UV: A Game-Changer for Data Engineering Scripts

Introduction While pip install has been the go-to package installer for Python developers, UV brings game-changing performance improvements to dependency management. UV achieves significantly faster installation speeds through several clever optimizations: Parallel Downloads: Unlike pip’s sequential approach, UV downloads multiple packages simultaneously, dramatically reducing wait times for large dependency sets. Wheel-First Strategy: UV prioritizes pre-built wheels over source distributions, avoiding time-consuming compilation steps when possible. Rust-Based Implementation: Built with Rust’s memory safety and concurrent processing capabilities, UV handles package resolution more efficiently than pip’s Python-based implementation. In real-world testing, UV often installs packages 5-10x faster than pip, particularly in environments with many dependencies. For data professionals working with complex libraries like pandas, numpy, scikit-learn, or pyspark, this speed difference isn’t just convenient – it’s transformative for workflow efficiency.

Saturday, January 11, 2025 Read

Data Vault Data Modeling with Python and dbt

Introduction Data Vault is a data modeling technique that is specifically designed for use in Data Warehouses. It is a hybrid approach that combines the best elements of 3rd Normal Form (3NF) and Star Schema to provide a flexible and scalable data modeling solution. Hubs, Links, Satellites A Data Vault consists of three main components: Hubs, Links, and Satellites. Hubs are the backbone of the Data Vault architecture and represent the entities within the data model. They are the core data elements and contain the primary key information.

Sunday, February 26, 2023 Read