Power BI
Five DAX Patterns Every Power BI Analyst Should Know
Five practical DAX patterns for Power BI: measures vs. calculated columns, year-to-date and year-over-year time intelligence, running totals, and dynamic ranking with RANKX.
3 min read
Knowledge hub
The blog
Tutorials, case studies, and notes on analytics engineering and BI.
Analytics Engineering
A Practical Intro to Analytics Engineering with dbt
What dbt actually does and why teams adopt it: the staging, intermediate, and marts layering pattern, a worked model, and how tests and docs turn raw SQL into a trustworthy product.
3 min read
SQL
Mastering SQL Window Functions
SQL window functions explained with runnable examples: running totals, ranking with row_number, rank, and dense_rank, and period-over-period comparisons with lag and lead.
4 min read
Career
Career
Turning a Vague Request Into an Analysis Brief
Most wasted analyst effort comes from the gap between the request someone makes and the decision they are trying to make. Four questions that close it, the short brief that converts your assumptions into theirs, and how to handle the answers you will actually get.
6 min read
Power BI
Power BI
Measures vs Calculated Columns in Power BI
The same formula written two ways behaves completely differently. Row context versus filter context, why averaging a margin column is wrong, the memory cost of stored columns, and a one-sentence rule for choosing correctly.
6 min read
Analytics Engineering
Analytics Engineering
Slowly Changing Dimensions, Explained Without the Jargon
When a sales rep changes team, should last quarter's numbers move with them? That question decides your dimension design. Type 1 versus Type 2 in plain terms, the join mistake that silently loses rows, and how dbt snapshots handle it.
6 min read
Python
Python
From Notebook to Pipeline: Analysis You Can Re-Run
Why the notebook that produced your headline number only works on your laptop, and the hour of work that fixes it: restart-and-run-all, extracting functions, config from the environment, idempotent writes, and assertions that catch data changes.
6 min read
SQL
SQL
The Grain Problem: Why Your Joins Inflate Your Totals
Fan-out is the most dangerous bug in analytics because it makes numbers look bigger, not smaller. What grain means, how a join silently changes it, the four ways to fix double counting, and the two checks that catch it in seconds.
6 min read
Analytics Engineering
Analytics Engineering
Data Quality Tests That Catch Real Bugs
Most dbt test suites are green and catch nothing. The four generic tests that earn their place, how to assert your model's grain, writing business-specific tests for reconciliation and freshness, and using severity so people keep paying attention.
6 min read