206-dbt-analytics-engineering

dbt-analytics-engineering

Purpose: Establish a robust, scalable, and maintainable analytics engineering workflow using dbt.

1. Project Structure & Naming

• Organize models into folders:
  • staging/ for raw table transformations
  • marts/ subdivided into core/, operational/, analytics/
• Prefix folders and models using stg_, src_, dim_, fct_
• Use snake_case and singular nouns for model names

2. Documentation & Lineage

• Document all sources and models with YAML docs (description, meta, tags)
• Generate and version the docs site via dbt docs generate in CI
• Tag models for lineage grouping (e.g., business_domain, data_source)

3. Testing & Quality

• Define schema tests: unique, not_null, accepted_values, relationships
• Create custom generic tests for domain-specific rules (e.g., same_day_orders)
• Use snapshots for Slowly Changing Dimensions (SCD1/SCD2)
• Integrate pre-commit-dbt and require tests in PR checks

4. Performance & Materializations

• Materialize models strategically:
  • view for lightweight transforms
  • table for heavy or reused results
  • incremental with unique_key and explicit on_schema_change
• Partition and cluster large tables where supported (e.g., by date)
• Avoid expensive operations (cross-joins, cartesian products)
• Leverage warehouse-specific optimizations (e.g., Snowflake clustering)

5. Reusability & Macros

• Centralize reusable logic in macros/ (e.g., watermark, safe_cast)
• Use the dbt_utils package for common operations (surrogate_key, pivot)
• Abstract environment-specific configs in dbt_project.yml via vars

6. Metrics & Semantic Layer

• Define metrics in metrics/ files and build a centralized semantic layer
• Leverage exposures for BI tool integration and lineage tracking
• Validate metric definitions with endpoint tests

7. CI/CD & Versioning

• Pin dbt core and package versions in packages.yml lockfile
• Automate pipelines: dbt deps, dbt run, dbt test, dbt docs generate
• Promote between environments via Git branches and release tags
• Monitor run history and alert on failures or freshness threshold breaches

8. Observability & Monitoring

• Configure sources.yml freshness thresholds and enforce via CI
• Log run metadata (timing, row counts) to the run_results table
• Integrate data observability frameworks (e.g., Great Expectations, Monte Carlo)

References: Consolidated from dbt Labs guides ("Analytics engineering: Six best practices", "Data quality best practices") and community standards.