Blog table

Before moving an ADF metadata framework into Fabric Data Factory, I separate durable operating decisions from platform-specific connections, targets, schedules, and validation.

Before planners use a supply-chain feed, I gate it with freshness, row-count, and business-rule checks for duplicates, UOMs, locations, inventory, lifecycle, demand grain, and RM/FG classification.

For late ASN and shipment confirmations, I treat weekly OTIF as preliminary inside a restatement window so routine corrections settle without manual report patching.

Before supply-chain KPIs reach a business review, I define timing, exclusions, grain, owner, and decision use so one label does not hide planning, warehouse, and finance conflicts.

For analytics repos in Azure DevOps, I want one pre-production record that shows PR validation, dbt compile, changed-scope checks, published evidence, and environment approval before promotion.

I call a dashboard self-service only when a review card makes its question, audience, grain, filters, timing, and “not for” cases visible before broader reuse.

Where I use Microsoft Fabric in a finance and operations reporting path, and where I still keep semantic ownership, transform checks, and the first failure surface explicit before I trust the output.

Snowflake models stay easier to trust when raw landing absorbs source drift, staged tables normalize names and types, and curated models keep row meaning, joins, and recovery boundaries explicit.

I investigate Snowflake compute spikes in a fixed order: warehouse metering, idle gap, load, query history, later attribution, and pruning evidence before I start tuning.

For a business-critical dbt model, I order the first tests by failure cost: grain uniqueness, decision-critical nulls, relationships, domain rules, and business assertions.

I trust a business-critical dashboard more when one short operating spec names the owner, source models, metric note, refresh cutoff, trusted validation slice, known boundaries, and failure path.

I keep dbt Core deployments boring with one short deployment record: state reference, selector, target, changed nodes, and promotion result before production moves.

I use a short BI release checklist to compare outputs, review filter and definition changes, confirm sign-off, and keep a rollback path before business-critical dashboard changes go live.

I keep a one-page incident note during analytics incidents so impact, checks, decisions, cause, and prevention stay clear while the KPI is still broken.

I use five signals—source freshness, latest publish, runtime, output shape, and owner—to make a missed analytics pipeline cutoff explainable before I add more orchestration.

I validate a rebuilt metric with a short backfill note, slice-by-slice comparison, and a published restatement boundary before I republish the dashboard.

How I keep models trustworthy with a simple grain note: row meaning, expected key, safe joins, and the first duplication test I write.

When one metric starts arguing with itself across dashboards, I use a definition card to decide whether the team needs one owned definition or separate named metrics.

When a dashboard number moves without warning, I isolate the failing layer—freshness, row counts, joins, or metric logic—before debating the chart.

The five checks I use before I trust a pipeline: freshness, row counts, uniqueness, null rates, and one business-shape check that tells me whether the published data is safe to use.

My six-part source-table contract covers row meaning, key rule, landing cadence, valid-record handling, correction window, and owner so downstream analytics work does not start with guesses.

Before quarter-close reporting locks, I use a metric ownership review card to make changed definitions, owners, open questions, affected surfaces, and safe-to-lock decisions visible.

I choose an analytics stack I can debug: trace a late run or wrong number back to the landed extract, the logic diff, the metric definition, and the owner.

I write about pipeline checks, metric definitions, dashboard trust, and delivery habits that keep analytics systems explainable when numbers move.

Before I promote a source table into production dependencies, I score observed cadence, key stability, corrections, null risk, late arrivals, and owner response so the promotion decision is explicit and defensible.

When a validated backfill moves reported numbers, I publish a change log with affected ranges, expected movement, evidence, sign-off, and interpretation boundaries.

I use a simple trigger table to decide when an analytics incident needs a note, a lightweight review, or a full postmortem based on impact, recurrence, exposure, and unresolved prevention work.

I keep a short runbook behind analytics SLA alerts so the first responder can see impact, last success, failed step, source freshness, owner, escalation path, and the stakeholder message before the cutoff is missed.

I use a schema-change checklist to classify additive fields, renames, type changes, dropped fields, and semantic changes before an upstream source breaks downstream models or dashboards.

I use a small analytics release evidence packet to keep changed assets, validation output, owner approval, rollback notes, and stakeholder context together before a decision-critical release goes live.