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Using the package

Google INFORMATION_SCHEMA tables​

Following models are available to query the INFORMATION_SCHEMA tables. They are materialized as ephemeral in dbt so it acts as a "source" but let you access multiple multiple project based tables using a single ref.

Example​

You can use those models such as:

SELECT query FROM {{ ref('information_schema_jobs') }}

Here's the list (don't forget to prefix the following list by information_schema_ in your ref call).

  • access_control

    • object_privileges

  • bi_engine

    • bi_capacities
    • bi_capacity_changes

  • configuration

    • effective_project_options
    • organization_options
    • organization_options_changes
    • project_options
    • project_options_changes

  • datasets

    • links
    • schemata
    • schemata_options
    • schemata_replicas
    • shared_dataset_usage

  • jobs

    • jobs
    • jobs_by_folder
    • jobs_by_organization
    • jobs_by_project
    • jobs_by_user

  • jobs_timeline

    • jobs_timeline
    • jobs_timeline_by_folder
    • jobs_timeline_by_organization
    • jobs_timeline_by_user

  • recommendations_and_insights

    • insights
    • recommendations_by_organization
    • recommendations

  • reservations

    • assignment_changes
    • assignments
    • capacity_commitment_changes
    • capacity_commitments
    • reservation_changes
    • reservations
    • reservations_timeline

  • routines

    • parameters
    • routine_options
    • routines

  • search_indexes

    • search_index_columns
    • search_indexes

  • sessions

    • sessions
    • sessions_by_project
    • sessions_by_user

  • streaming

    • streaming_timeline
    • streaming_timeline_by_folder
    • streaming_timeline_by_organization

  • tables

    • column_field_paths
    • columns
    • constraint_column_usage
    • key_column_usage
    • partitions
    • table_constraints
    • table_options
    • table_snapshots
    • table_storage
    • table_storage_by_organization
    • table_storage_usage_timeline
    • table_storage_usage_timeline_by_organization
    • tables

  • vector_indexes

    • vector_index_columns
    • vector_index_options
    • vector_indexes

  • views

    • materialized_views
    • views

  • write_api

    • write_api_timeline
    • write_api_timeline_by_folder
    • write_api_timeline_by_organization

  • gcp_billing_export

    • gcp_billing_export_resource_v1

Monitoring models​

The package provides the following datamarts that can be easily used to build monitoring charts and dashboards:

  • global

    • daily_spend
    • dbt_bigquery_monitoring_options

  • compute

    • billing

      • compute_billing_per_hour

    • bi engine

      • bi_engine_usage_per_minute
      • bi_engine_usage_per_hour

    • cost

      • compute_cost_per_hour
      • compute_cost_per_hour_view (adds computed metrics)
      • compute_cost_per_minute
      • compute_cost_per_minute_view (adds computed metrics)

    • jobs

      • most_expensive_jobs
      • most_repeated_jobs
      • slowest_jobs
      • query_with_better_pricing_using_flat_pricing_view
      • query_with_better_pricing_using_on_demand_view

    • models

      • most_expensive_models
      • most_repeated_models

    • reservations

      • reservation_usage_per_minute

    • users

      • most_expensive_users

  • storage

    • dataset_with_better_pricing_on_logical_billing_model
    • dataset_with_better_pricing_on_physical_billing_model
    • dataset_with_cost
    • most_expensive_tables
    • partitions_monitoring
    • read_heavy_tables
    • storage_billing_per_hour
    • table_with_better_pricing_on_logical_billing_model
    • table_with_better_pricing_on_physical_billing_model
    • unused_tables