Cloud and data
Let's discuss your project →BigQuery metadata indexing: when freshness is good enough to trust the data
A practical BigQuery guide to metadata freshness: what it proves, how to check it, and when to consider dedicated background capacity.
Learn more →BigQuery default data policy project: when to configure it for data governance tags
A practical guide to when BigQuery's default data policy project is worth configuring, with a cross-project read-path example and rollout checks.
Learn more →From dashboard KPI to source tables: tracing lineage in Google Cloud
A practical guide to tracing a dashboard KPI back to its source tables in Google Cloud using BigQuery and Knowledge Catalog lineage, with a simulated column rename, ownership checks, and explicit limits on coverage and configuration.
Learn more →Reconciling an AWS Bill Line Item to a Cost Center and Owner
How to map an already billed AWS line item to a cost center and owner, while handling exceptions, unallocated costs, and closed periods safely.
Learn more →BigQuery policy tags vs data governance tags: how to choose for sensitive columns
A practical guide to choosing between BigQuery policy tags and data governance tags for column-level security. It explains prerequisites, a decision path, a simulated pilot, and acceptance checks for teams that need masking and access control without overcomplicating governance.
Learn more →Dashboard updated or just opened today? How to verify data freshness
GOV.UK treats data quality as fitness for purpose and links it to lifecycle thinking and clear user communication. This article turns that idea into a practical dashboard freshness check.
Learn more →PostgreSQL slow? Read the query plan before adding an index
Read PostgreSQL execution plans before adding an index: capture a reproducible case, compare estimates with measured runs, and test trade-offs in a controlled environment.
Learn more →Cloud resources without an owner: use tags before deciding what to shut down
AWS tags help turn cloud resources into a readable inventory. This article shows a practical triage workflow: confirm ownership, verify purpose, and decide whether to keep, investigate, or decommission a resource.
Learn more →GitLab’s 2017 Database Incident: A Retrospective on Backups and Error Signals
GitLab’s 2017 post-mortem shows that backups, notifications, and restores are separate checks. The retrospective suggests a simple exercise: inventory, notification test, and restore of a test dataset, without touching production.
Learn more →AWS Backup: How to Design a Restore Test That Also Verifies the Application
A restore test is only useful if it also confirms that the application can use the recovered data. This note outlines success criteria, environment setup, checks, and closure for a simulated scenario, keeping restore and application validation separate.
Learn more →BigQuery data governance tags: a practical way to protect sensitive columns without redesigning the warehouse
BigQuery’s data governance tags give teams a more structured way to protect sensitive columns with IAM-linked policies and masking. This article explains what changes, when the feature is useful for SMEs, and how to test it safely in a small pilot.
Learn more →AWS Budgets and cost anomalies: how to read them without mixing them up
An operational guide for SMEs using AWS: how to distinguish budgets, anomalies, and forecasts; what AWS documents about update delays and alert timing; and how to build a practical review flow before taking action on a cost spike.
Learn more →Cloudflare’s internal outage: why it can look like an attack, and how SMEs should prepare
A practical retrospective on Cloudflare’s 18 November 2025 outage: what happened, why it resembled an attack, and how SMEs can prepare with dependency mapping, an independent status channel, fallback rules, and recovery checks.
Learn more →BigQuery: What to check before automating KPI analysis
Google Cloud’s BigQuery augmented analytics announcement introduces functions for drivers, trends, change points, seasonality, and causal estimates. This article explains the checks teams should put in place before automating KPI analysis: metric definition, data quality, permissions, cost discipline, and human review.
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