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name: data-warehouse description: Data warehouse (大数据/数仓) ops skill for designing and operating analytical data platforms. Use for tasks like defining source-of-truth, building ETL/ELT pipelines, dimensional modeling (star schema), data quality checks, partitioning strategies, cost/performance tuning, governance, lineage, and SLA monitoring.

data-warehouse

Use this skill for 大数据/数仓（DW）建设与运维：从数据接入到指标交付与治理。

Defaults / assumptions to confirm

• Warehouse tech: BigQuery/Snowflake/Redshift/Hive/ClickHouse/etc.
• Orchestration: Airflow/Dagster/Argo/dbt
• Ingestion: CDC vs batch, streaming (Kafka) vs file
• Data consumers: BI dashboards, product analytics, ML features

Core outputs

• Warehouse architecture (sources → staging → warehouse → marts)
• Data model (facts/dimensions) + metric definitions
• Pipeline plan (DAGs, schedules, dependencies, SLAs)
• Data quality plan (checks, thresholds, alerts)
• Cost/performance plan (partitioning, clustering, materialization)
• Governance plan (access control, PII handling, retention)

Workflow

1. Understand the business questions

• What decisions will this warehouse support?
• Define critical metrics and their definitions (single source of truth).

2. Source & ingestion design

• Identify systems of record and ownership.
• Choose ingestion: CDC for mutable OLTP, append-only logs for events.
• Define late-arriving data strategy and backfills.

3. Modeling (practical)

• Prefer star schema for BI: Fact tables + Dimension tables.
• Keep grain explicit (one row represents what?).
• Separate raw/staging from curated models; avoid mixing.

4. ETL/ELT pipelines

• Define DAGs with clear inputs/outputs and idempotency.
• Handle retries, partial failures, and reprocessing windows.
• Provide backfill procedures and runbook.

5. Data quality & observability

• Validate freshness, volume, schema drift, null ratios, referential consistency (logical).
• Add anomaly detection for key metrics.
• Track pipeline success rate, duration, and SLA misses.

6. Performance & cost

• Partition by date/time for large facts; cluster by common filters/joins.
• Materialize expensive queries (summary tables, incremental models).
• Control scan cost with column pruning and predicate pushdown.

7. Governance & security

• PII classification, masking, and retention.
• RBAC/ABAC for datasets; audit logging.
• Document lineage and ownership for each table/model.

Templates

Table spec

• Name:
• Grain:
• Partition/cluster keys:
• Key columns:
• Sources:
• Refresh cadence:
• Consumers:
• Quality checks:
• Owner:

Metric spec

• Name:
• Definition:
• Numerator/denominator:
• Filters:
• Time window:
• Known caveats: