Dashboard number moves
Trace what changed, which checks ran, and how to keep dashboard releases explainable.
Field notes
Start with the problem
If the reverse-chronological list is not where you want to begin, these pathways group the field notes by the production pressure you are trying to handle.
Source becomes production-critical
Turn a fragile upstream dependency into a source your downstream models can trust.
Metric changes and backfills
Keep definition changes, schema shifts, and backfilled numbers visible to reviewers.
Analytics release goes live
Use release evidence, deployment checks, and runbooks before shared analytics changes ship.
Supply-chain reliability
Make late-arriving data, operational reviews, and platform choices easier to inspect.
analytics-engineering backfills dashboard-reliability data-contracts data-engineering data-modeling data-pipelines data-platforms data-quality data-reliability metrics observability operations semantic-layer snowflake supply-chain
· 8 min read
The metadata-driven pipeline decisions I would revisit before moving ADF patterns into Fabric Data Factory
· 6 min read
The checks I add to supply chain data before planners trust it
· 4 min read
Handling late-arriving supply chain data without rewriting history by hand
· 9 min read
The supply chain numbers I define before they reach a business review
· 7 min read
Azure DevOps checks before analytics code reaches production
· 7 min read
What I check before I label a dashboard self-service
· 7 min read
Where Microsoft Fabric fits and where I would keep it out of the critical path
· 6 min read
The Snowflake design choices that make downstream models easier to trust
· 6 min read
What I watch in Snowflake before compute cost becomes a surprise
· 5 min read
The dbt tests I write first for business-critical models
· 6 min read
The operating spec I want before I trust a business-critical dashboard
· 4 min read
How I keep dbt Core deployments boring in production
· 5 min read
A dashboard release checklist before a BI change goes live
· 5 min read
The incident note template I wish every analytics team used
· 4 min read
Five pipeline observability signals before more orchestration
· 6 min read
How I validate a metric after a backfill
· 4 min read
Every important model needs an explicit grain
· 5 min read
The definition card I use to stop metric drift across dashboards
· 5 min read
When a dashboard number changes, I check these four things first
· 5 min read
Row counts are not enough: the checks I add before I trust a pipeline
· 5 min read
The 6-part data contract I want before I trust a source table
· 6 min read
The metric ownership review I run before quarter close
· 4 min read
How I choose an analytics stack I can debug
· 3 min read
What I write about: reliable analytics systems that stay explainable
· 6 min read
The source-trust scorecard I use before promoting a table to production
· 5 min read
The change log I publish when a backfill moves reported numbers
· 7 min read
When an analytics incident needs a postmortem, not just a note
· 6 min read
The first-response runbook I want behind every analytics SLA alert
· 7 min read
The schema-change checklist I use before a source breaks downstream models
· 6 min read