All Posts

A $600,000 Salesforce contract, cancelled in two months. Enterprise transformations that run for years, weighed down by governance nobody could shortcut. Having worked inside both, I know why these aren't really the same debate — and that gap is the part worth talking about.

Most organisations still run separate data, integration, and AI teams — three roadmaps, three toolchains, three reporting lines. That separation made sense when each function had genuinely distinct skills. It doesn't hold up as well now. Here's the case for a converged data team, and what it should actually look like.

Three genuinely different mechanisms exist for row and column level security in Unity Catalog. A practitioner's guide to what each one is actually for — and the one mistake that trips up more rollouts than any misconfigured filter

GitLab's 2026 AI Accountability Report surveyed 1,528 developers and technology buyers. The finding worth sitting with: 78% code faster with AI, but overall software delivery hasn't accelerated. The bottleneck didn't disappear — it moved. What this means for Data Integration, Analytics, and AI platform teams specifically

Databricks Genie moved to pay-as-you-go billing on 6 July 2026. This article sets out what the change requires of data team leaders, and what it requires of data engineers, analysts, and data scientists who use Genie directly, within a governed Data Integration, Analytics, and AI capability

A CDF history table captures every Delta Lake change permanently — independent of VACUUM retention. Here's the pattern, the schema evolution handling, and the partitioning strategy that scales

Data and analytics job postings are down twice as much as the broader tech market. At the same time, engineering hiring overall is resilient, and AI agents now build the majority of new databases on major platforms. Here's what the actual data says is happening

SCD Type 2 Databricks implementation compared — manual MERGE logic versus Lakeflow's APPLY CHANGES INTO. Working code, gotchas, and migration lessons from real production pipelines

Vendors are already converging on multi-model routing. We explain the five questions that actually determine whether AI agents create value for your business — and why they matter more than which platform you standardise on.

Change Data Feed in Databricks looks simple in the documentation. In production, it breaks in six distinct ways — and each one is silent until something downstream stops working. This is what I learned building CDF pipelines on Azure Databricks.

Tokenmaxxing — the practice of treating AI token consumption as a measure of productivity — emerged from Silicon Valley in early 2026. It is now showing up in data engineering teams across industries, and the costs are less visible but no less real

Anthropic has published an official integration between Claude and Databricks — giving teams the ability to ask questions of their data in plain English. Here is what your environment needs to look like before that capability actually works.

Most AI initiatives don't fail because of the technology. They fail because the data underneath it was never ready. Here are the six signs your organisation has a data problem — and what to do about it.

67% of businesses use spreadsheet software daily. 94% of those spreadsheets contain errors. The consequences — financial restatements, compliance exposure, decisions made on incorrect data — are well documented. This article examines the shift to self-service analytics and modern data platforms: what the transition involves, what dependencies it creates, and why the effectiveness of any analytics tool ultimately depends on the reliability of the data foundation it sits on.

Boards are asking about AI. Vendors are promising AI-powered everything. But between the enthusiasm and the operational reality sits a question most organisations have not yet answered honestly: is our data good enough to trust with AI? This article examines what AI readiness actually requires across five practical dimensions — and what it looks like when those dimensions are missing.

100 essential terms, explained in plain language for business leaders — not developers. Top 100 Data & AI Terms 2026 The language of data and AI is evolving faster than most organisations can keep up with. New terms appear weekly. Familiar ones get redefined. And the gap between what technology vendors say and what things actually mean keeps widening. This glossary cuts through the noise. A sample of what's inside: Hallucination — When an AI confidently generates information

APIs are the connective tissue of the modern business — the invisible connections that keep your systems talking to each other. Most business leaders have never had the concept explained without jargon. This guide does exactly that, and explains what breaks when APIs are not managed properly.

Most organisations are trying to do analytics before their reporting is reliable, or deploy AI before their analytics is mature. This guide explains what each capability actually is, how they differ, and the sequence that determines whether any of them will work.

Every organisation wants to use AI. Most are not ready for it — not because of the model they choose, but because of the data underneath it. This guide explains why a governed data foundation is the prerequisite for any meaningful AI initiative, and what organisations consistently get wrong by skipping it.

If your business runs on more than five software platforms, your analysts spend more time collecting data than analysing it, or your reports tell different stories depending on which system you look at — this guide was written for you.