<\/p>\n
A design checklist is valuable when it exposes missing decisions before production. It should not replace architecture reasoning; it should force teams to state assumptions, ownership, limits, and evidence across the data platform.<\/p>\n
Confirm the grain, schema, update pattern, owner, expected volume, and arrival behavior of every important source. Define how schema changes are detected and how reprocessing works.<\/p>\n
Start by classifying sources and consumers; DP-900 data concepts<\/a> helps match data types to suitable storage and processing patterns before services are selected.<\/p>\n Identify the purpose of raw, curated, and serving layers. Define retention, archival, deletion, partitioning, and file-size strategy. Know which datasets are authoritative and which are temporary.<\/p>\n For warehouse-style architecture, SnowPro foundations overview<\/a> makes the separation between storage, compute, and consumption explicit enough to review independently.<\/p>\n For each pipeline, define dependencies, retries, idempotency, backfill behavior, failure notification, and restart point. Record run metadata so an operator can reconstruct what happened.<\/p>\n Recovery has to be designed alongside transformation. DP-700 engineering<\/a> treats retries, reruns, and operational behavior as part of the engineering system.<\/p>\n State which checks prove that data is usable: schema, freshness, completeness, uniqueness, domain validity, reconciliation, or business-specific rules. Decide which failures block publication.<\/p>\n Quality rules need owners and thresholds. A check no one responds to becomes noise.<\/p>\n Assign dataset owners. Maintain catalog descriptions for important data products. Capture lineage from source through major transformations to consumption where practical.<\/p>\n Governance should help users answer what a field means, where it came from, who owns it, and whether they may use it.<\/p>\n Classify sensitive data, restrict access by role and purpose, protect secrets, encrypt appropriate paths, and define masking or fine-grained access.<\/p>\n Security review should separate identity, encryption, and data-protection controls; AWS data protection overview<\/a> provides a cloud example of those distinct responsibilities.<\/p>\n Define privileged roles, environment separation, change control, patching or managed-service responsibilities, and break-glass access.<\/p>\n Managed databases still require monitoring, access control, backup, and performance discipline; DP-300 administration guide<\/a> shows the operational responsibilities that remain after infrastructure is abstracted.<\/p>\n Record latency and throughput targets. Check partitioning, clustering or ordering, file layout, query shape, concurrency, and workload isolation.<\/p>\n Do not optimize based only on development-scale data. Test representative patterns.<\/p>\n Tag or allocate spend by workload, team, environment, or product. Set scaling limits where appropriate. Monitor storage growth, data movement, idle compute, and expensive recurring jobs.<\/p>\n Performance and cost should be reviewed together because a faster design is not automatically a more economical one.<\/p>\n Define reusable business measures, relationships, calendars, refresh expectations, and security at the consumption layer.<\/p>\n Modeling and reporting choices depend on the platform beneath them; Azure and Power BI architecture<\/a> shows how storage, transformation, semantic modeling, and BI fit together.<\/p>\n Monitor source arrivals, pipeline state, quality checks, query performance, capacity, model refresh, and data freshness. Correlate alerts with the logical data period they affect.<\/p>\n Operators should be able to distinguish infrastructure failure from late data, bad data, and consumer-layer failure.<\/p>\n Document backup and restore behavior, historical replay, regional failure assumptions, and the order in which critical datasets should return.<\/p>\n Recovery tests should validate data correctness and access controls, not only that storage can be restored.<\/p>\n Keep pipeline, model, and infrastructure definitions under version control where practical. Use review and automated checks before production change.<\/p>\n Safe change becomes easier to understand when a lab is deliberately broken and recovered; Google data engineering labs<\/a> provides scenarios where those failure and recovery behaviors can be practiced.<\/p>\n Identify the real consumers: dashboards, analysts, machine learning, operational applications, data sharing, or APIs. Define freshness and service expectations from their needs.<\/p>\n The platform exists to deliver usable data, not merely to run pipelines.<\/p>\n Before approval, ask whether the architecture can explain ownership, failure behavior, security boundaries, data quality, performance limits, and cost drivers.<\/p>\nStorage and lifecycle<\/h2>\n
Pipeline reliability<\/h2>\n
Data quality<\/h2>\n
Governance and lineage<\/h2>\n
Security and privacy<\/h2>\n
Platform administration<\/h2>\n
Performance<\/h2>\n
Cost<\/h2>\n
BI and semantic consistency<\/h2>\n
Observability<\/h2>\n
Recovery<\/h2>\n
Development workflow<\/h2>\n
Consumption and value<\/h2>\n
Final design review<\/h2>\n