What Analytics Engineering Is Really For
Analytics engineering is not the tool stack. It is the operating discipline that turns business logic into trusted reporting assets.
Implementation Notes
Technical notes for implementing Reporting Trust
A quiet library for data teams turning Reporting Trust concepts into working models, tests, source-to-report controls, and operational reporting habits.
Implementation Area
Foundations
Core analytics engineering ideas behind Reporting Trust, business logic, ownership, and source-to-report control.
Implementation Area
SQL and Modeling
SQL, model grain, joins, dimensional patterns, source-to-report logic, and trusted reporting marts.
The Join Mistakes That Make Dashboards Lie
How SQL join fanout, duplicate keys, and mismatched grain create plausible but wrong dashboard numbers.
Implementation Area
Testing and QA
Tests, reconciliation checks, freshness controls, reporting contracts, and release confidence.
dbt Tests as Risk Mitigation Controls
Why dbt tests should be treated as reporting trust controls, not just developer checks.
Implementation Area
Orchestration
Job scheduling, dependencies, alerts, freshness, retries, and operational reporting promises.
Orchestration Is Where Reporting Promises Become Operational
Why orchestration turns reporting dependencies, freshness, retries, and ownership into visible operating controls.
Freshness SLAs and the Moment a Dashboard Is Safe to Use
How source freshness, reporting SLAs, and caveat rules help teams decide whether a dashboard is safe for a decision.
Implementation Area
Git and CI/CD
Version control, pull requests, automated checks, deployment habits, and change review for reporting logic.
Git for Analytics Teams
How Git makes reporting logic reviewable, reversible, and safer to change across SQL, dbt, Dataform, and documentation.
CI/CD for dbt and SQL Projects
How CI/CD protects trusted reporting by checking SQL, tests, documentation, and dependencies before changes reach decision workflows.
Implementation Area
Work Management
Ticket framing, ownership, prioritisation, acceptance checks, and delivery habits for analytics work.
Analytics Work Is Product Work
Why Jira, Linear, issue templates, prioritisation, and ownership matter when reporting models become decision products.
Implementation Area
Debugging and Optimization
Performance, dashboard reliability, incident review, cost control, and practical debugging paths.
Why Slow Dashboards Point to a Broken Architecture
Slow dashboards are often a sign that business logic, model grain, and aggregation choices are in the wrong layer.