The Data Modernisation Playbook offers actionable strategies and insights to help data practitioners stay ahead of the curve with expert guidance on seamless migrations, cloud adoption, and future-ready data solutions. #DataAnalytics

Everyone says, "Build a data lakehouse for AI". But here's why that's wrong for most organisations. Your existing data warehouse isn't the enemy of AI; bad pipeline design is. Companies spend millions migrating from Snowflake to Databricks, thinking that's what AI readiness

Your Airflow DAGs are choking on AI workloads. And it's not your fault, you built them for traditional analytics. Here's what I see happening: Data engineers design beautiful pipelines for dashboard reporting: clean transformations, perfect scheduling, reliable batch

Check out the latest article in my newsletter: Beyond Data Warehouses: How Data Lakehouses Are Making Enterprise-Grade Analytics Accessible in 2025 via @LinkedIn.

Beyond Data Warehouses: How Data Lakehouses Are Making Enterprise-Grade Analytics Accessible in 2025

There are 3 secrets every AI-ready data pipeline handles…. That traditional ETL pipelines completely miss. Want them? Here they are. 👇. AI-ready pipeline: Handles schema drift automatically with Delta Lake format. Traditional pipeline: Breaks when new columns appear in source

Think of data storage like organising your garage:. Traditional Data Warehouse = Organised Tool Cabinet.- Every tool has a specific place.- You organise before storing.- Easy to find what you need quickly.- Perfect for regular maintenance tasks. Data Lakehouse = Large Storage

Check out the latest article in my newsletter: Why your data lake is bleeding money (and 3 ways to stop it) via @LinkedIn.

Why your data lake is bleeding money (and 3 ways to stop it)

There are 3 differences between data warehouses and data lake-houses. That every data team should understand. I've worked with both for 15 years. Here they are. 👇. Data Warehouses: Store structured data in predefined tables and schemas. Data Lakehouses: Store any file type,

5 things I wish someone told me before my first Snowflake migration:. 𝟭- Your biggest enemy is not technical debt; it's tribal knowledge walking out the door. 𝟮- "Lift and shift" costs three times more than 'redesign and migrate'. 𝟯- Your legacy system's quirkiest features are

Check out the latest article in my newsletter: Key Insights from the dbt CEO on AI and Data Engineering via @LinkedIn.

Key Insights from dbt CEO on AI and Data Engineering

You are never JUST writing ETL scripts. When I started as an ETL Developer at a mid-sized financial services company, I thought my job was moving data from A to B. I was wrong. My actual daily tasks included:. - Debugging why yesterday's "perfect" data looked wrong today

You are never JUST a Data Engineer. When I worked at a major NZ bank, my job title was Business Analyst for Data. But my actual responsibilities included:. - Designing data models for real-time insights (Data Architect).- Managing stakeholder expectations during system

Check out the latest article in my newsletter: Avoid These Data Traps: 5 Lessons From Real-World Data Architecture Journey via @LinkedIn.

5 Lessons From Real-World Data Architecture Decisions (Case Study)

5 indicators that your ETL process is hindering your data modernisation initiatives…. (And why modular architecture changes everything). Here's what I am seeing in the field. 👇. 🔴 Cron jobs everywhere: If your data processing depends on random scheduled scripts, you're

There are 3 reasons your AI models keep failing in production…. That has nothing to do with your algorithms. Want to know what's really killing your AI initiatives? Here they are. 👇. - Legacy ETL scripts: You're feeding AI models with data processed by scripts written 10

Check out the latest article in my newsletter: Is Your ETL Sabotaging Your AI? (The Real Reason AI Projects Fail) via @LinkedIn.

Is Your ETL Sabotaging Your AI? (The Real Reason AI Projects Fail) .#AI #ETL #DataPipeline