Practical guides on data, cloud, and building data-driven organizations.
47 articles
You need data engineering help. But do you need a full-time hire, or would fractional support make more sense? Here's how to decide.
AI coding assistants are incredible for prototyping. But mission-critical data systems need the judgment that comes from years of watching things fail.
After completing a hands-on platform comparison between Databricks and Snowflake, here's what I learned - beyond the marketing materials.
Should you invest in a managed ETL tool or build pipelines yourself? After helping multiple organizations make this decision, here's how to think about it.
85% of data science projects fail. 55% of big data projects are never finished. Why? And how do you avoid becoming another statistic?
Data engineering is one of the fastest-growing roles in tech, but it's also one of the most misunderstood. Here's what data engineers actually do.
ETL might be the most important acronym in data that nobody outside of data teams understands. It's the plumbing that makes everything else possible.
The data storage landscape has gotten confusing. Data lakes. Data warehouses. Now data lakehouses. What's the difference, and which one do you actually need?
If your organization makes decisions based on data - or wants to - you'll eventually need a data warehouse. But what exactly is it, and why does it matter?
Every organization uses data. But not every organization uses data well. Data maturity is the framework for understanding where you are on that spectrum.
Not every organization should migrate to the cloud immediately. But most should start planning. Here's how to know if you're ready.
I spent time interviewing technology leaders at Canadian financial institutions about their cloud journeys. The patterns were remarkably consistent.
If your cloud business case focuses only on cost savings, you're setting yourself up to fail.
Hybrid cloud sounds like the best of both worlds. Sometimes it is. Sometimes it's the worst of both. And sometimes it's not a strategy at all - it's just what happened.
Banks use AWS. Healthcare organizations run on Azure. Government agencies deploy to GCP. If regulated industries can make cloud work, so can you.
I've seen technically flawless cloud architectures fail because nobody wanted to use them.
Most failed cloud migrations don't fail for technical reasons. They fail because organizations underestimate the human element.
Medallion architecture organizes your data lake into bronze, silver, and gold layers. Each layer adds more refinement, making raw data progressively more useful for analysis.
Code moves from development to QA to production in a careful progression. Understanding this lifecycle prevents costly mistakes and keeps your production systems stable.
Data cleaning is the process of fixing errors, inconsistencies, and gaps in your data. It's the difference between data you can trust and data that leads you astray.
KPI stands for Key Performance Indicator. It's a metric that tells you whether you're succeeding at something important.
A dashboard is a visual display of your most important metrics, updated automatically, designed to answer specific questions at a glance.
You spent $50,000 on marketing last quarter. Revenue went up. But which campaigns actually drove that growth? Marketing attribution answers these questions.
A data pipeline moves data from one place to another, transforming it along the way. It brings all your scattered data together so you can actually use it.
A data requirements document defines what you're trying to achieve before building anything. It prevents spending months building something that doesn't solve the problem.
You've decided you need someone focused on data. But who do you actually hire? The titles are confusing, the job market is competitive, and making the wrong choice is expensive.
Every data decision involves a choice: build a custom solution or buy an existing tool? The right answer depends on your specific situation.
When should you analyze data? As it happens (real-time) or periodically in chunks (batch)? The answer affects your architecture, costs, and what questions you can answer.
Technical debt is the future cost of taking shortcuts today. Like financial debt, it accumulates interest - the longer you wait to address it, the more it costs.
A data stack is the collection of tools and technologies you use to collect, store, transform, and analyze data. It's the infrastructure that makes analytics possible.
Agile is an approach to building things that emphasizes iteration, feedback, and flexibility. Instead of planning everything upfront, you work in short cycles and adapt.
Data governance is the system of policies, processes, and responsibilities that ensure your data is accurate, secure, and used appropriately.
An API integration connects two software systems so they can share data automatically. Instead of manually exporting and importing, integrations keep them in sync.
Data residency requirements are real, but they're often overstated as a cloud blocker.
Spreadsheets are where most businesses start. They're flexible, familiar, and free. But eventually, they break. Knowing when to move on saves pain.
Serverless computing doesn't mean "no servers" - there are always servers somewhere. It means you don't think about them. The cloud provider handles infrastructure.
A data lake is a storage repository that holds vast amounts of raw data in its native format until needed. Unlike databases, you don't have to structure data before storing it.
A data model is a blueprint for how your data is organized. It defines what data you store, how pieces of data relate to each other, and the rules that govern them.
Business Intelligence (BI) turns raw data into information people can actually use to make decisions. It's the dashboards, reports, and analytics that help you understand what's happening.
SQL (Structured Query Language) is how you talk to databases. Learning basic SQL is one of the highest-leverage skills for anyone who works with data.
All data falls into two buckets: structured and unstructured. Understanding the difference matters because they require different tools, storage, and approaches.
A database is an organized collection of data stored electronically. In practice, it's where your business information lives - customers, orders, products, transactions.
API stands for Application Programming Interface. In plain English: it's how software talks to other software.
This might be controversial: cloud security is usually better than what most organizations can achieve on their own.
The three major cloud providers each bring different strengths. Understanding them helps, but here's the truth: for most organizations, any of them will work.
Cloud computing comes in three flavors, and understanding the difference matters more than most people realize.
Cloud computing is about shifting from capital expenditure to operational expenditure. Instead of buying servers and hoping you sized everything correctly, you rent computing resources on demand.