2025

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

Data Modeling Showdown: Kimball vs One Big Table vs Relational

Introduction When architecting a data warehouse, one of the most crucial decisions is choosing the right data modeling approach. Like selecting the right tool for a job, each modeling methodology has its strengths and ideal use cases. Today, we’ll explore three popular approaches: Kimball’s dimensional modeling (star schema), the one big table approach, and traditional relational modeling. The Dataset: Understanding Our Example To illustrate these approaches, let’s consider a retail sales system with these core components:

Saturday, January 25, 2025 Read

Data Industry Trends: What to Expect in 2025

Introduction The data industry has kicked off 2025 with transformative developments that are fundamentally reshaping our approach to data management and analytics. The landscape is witnessing seismic shifts - from Databricks’ historic funding round to Boomi’s strategic acquisition of Rivery, and the industry-shaking Iceberg buyout. Yet amid this technological evolution, a critical question emerges: how will these advancements translate into tangible value for organizations? As we navigate through this dynamic environment, the focus extends beyond identifying dominant technologies to understanding their practical impact on business outcomes. Let’s explore the key trends that are defining the data world in 2025, and more importantly, how they’re reshaping the way organizations leverage their data assets.

Saturday, January 18, 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