Dbt

Healing Tables: When Day-by-Day Backfills Become a Slow-Motion Disaster

It was 2 AM on a Saturday when I realised we’d been loading data wrong for six months. The situation: a customer dimension with three years of history needed to be backfilled after a source system migration. The previous team’s approach was straightforward—run the daily incremental process 1,095 times, once for each day of history. They estimated three weeks to complete. What they hadn’t accounted for was how errors compound. By the time I looked at the data, we had 47,000 records with overlapping date ranges, 12,000 timeline gaps where customers seemed to vanish and reappear, and an unknowable number of missed changes from when source systems updated the same record multiple times in a single day.

Saturday, February 7, 2026 Read

Context Engineering: The New Must-Have Skill for Data Engineers

Last year I watched a colleague ask AI to help write a dbt model. The AI spit out perfectly functional SQL—clean syntax, proper CTEs, the works. Looked great. Then I noticed the table would eventually hold 800 million rows. No partitioning. No clustering. Just a raw, unoptimised heap waiting to turn into a query performance nightmare (that would likely become my nightmare to fix). The engineer wasn’t at fault. The AI wasn’t at fault either, really. The AI simply didn’t know that our environment clusters large tables by date. It didn’t know our team’s conventions around incremental models. It couldn’t know, because nobody had told it.

Saturday, January 31, 2026 Read

The Guerrilla Guide to Data Engineering Interviews

The Scenario That Changes Everything Picture this: You’re sitting in an interview room—or more likely these days, staring at a Zoom window with your carefully curated bookshelf background—and the interviewer asks you about data quality. “Tell me about your experience with data quality,” they say. You have two choices. Choice A: “Data quality is really important in data engineering. It involves ensuring data is accurate, complete, consistent, and timely. I believe strongly in implementing data quality checks throughout the pipeline.”

Sunday, January 11, 2026 Read

Building AI Agents with Claude Code

Introduction Imagine you’re reviewing a pull request with dozens of SQL files, each containing complex queries for your data pipeline. You spot inconsistent formatting, or syntax which doesn’t work with your infrastructure. Sound familiar? It’s common for data professionals to struggle with maintaining consistent SQL standards across their projects, especially when working with specialised platforms and it can be time consuming to review these elements within a peer review. It would be better use of time to focus on the hard thinking elements, like logic etc. However these small syntax or style issues, can be distracting. Well at least they are for me.

Saturday, September 13, 2025 Read

Continuous Integration for Data Teams: Beyond the Buzzwords

The Day Everything Broke (And How CI Could Have Saved Us) Picture this: It’s 9 AM on a Monday, and your Slack is exploding. The executive dashboard is showing impossible numbers. Customer support is fielding complaints about incorrect billing amounts. The marketing team is questioning why their conversion metrics suddenly dropped to zero. You trace it back to a seemingly innocent change you merged Friday afternoon—a simple column rename that seemed harmless enough. But that “harmless” change cascaded through your entire data pipeline, breaking downstream models, dashboards, and automated reports.

Saturday, June 28, 2025 Read

dbt Fusion: The Engine Upgrade That's Got Everyone Talking

When Your Favourite Tool Gets a Makeover You know that feeling when your favourite app suddenly changes its interface? That mix of excitement and anxiety about whether the changes will actually improve your workflow or just mess with muscle memory you’ve spent years building. That’s exactly what happened when dbt Labs dropped dbt Fusion on the analytics engineering community. The reactions were… let’s call them passionate. Some folks were celebrating like they’d just discovered fire, while others were questioning whether this marked the beginning of the end for open-source dbt.

Saturday, June 21, 2025 Read

Leveraging LLMs for Business Impact: Part 2 - Building an AI Data Engineer Agent

Introduction In Part 1 of this series, we explored the theoretical foundations of Large Language Models (LLMs), Retrieval Augmented Generation (RAG), and vector databases. Now, it’s time to put theory into practice. This is going to be a long read, so grab some coffee, and one (couple) of your favourite biscuits. One use case for leveraging LLM’s, is creating of a Agent - a Senior Data Engineer AI that automatically reviews Pull Requests in your data engineering projects. This agent will be that nit picky Data Engineer that enforces SQL formatting standards, ensure naming and data type consistency, validate data quality checks, and suggest improvements based on best practices. By integrating this into your GitHub workflow, you can maintain higher code quality, accelerate onboarding for new team members, and reduce the burden of manual code reviews.

Saturday, March 8, 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

Data Vault Data Modelling with Python and dbt

Introduction Data Vault is a data modelling 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 modelling 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