BigQuery Integration Discovered Resources
Ingested Resources & Properties
BigQuery Project (gcp_project)
name
The GCP project ID
type
Always gcp_project
native_id
The GCP project ID
Database (database)
In BigQuery, Euno represents each discovered GCP project as a database resource.
name
The GCP project ID
type
Always database
native_id
The GCP project ID
subtype
Always database
parent_container
The gcp_project containing the BigQuery project
Dataset (database_schema)
name
The BigQuery dataset name
type
Always database_schema
subtype
Always database_schema
created_at
Date and time when the dataset was created
updated_at
Date and time when the dataset was last modified
parent_container
The database resource containing the dataset
database_technology
Always bigquery
location
The BigQuery dataset location
meta
Dataset labels when Discover dataset labels as meta is enabled
Table (table)
name
The BigQuery table, view, materialized view, external table, snapshot, or clone name
type
Always table
native_id
Fully qualified BigQuery object ID in project.dataset.table format
subtype
One of base_table, view, materialized_view, external_table, snapshot, or clone
created_at
Date and time when the table was created
updated_at
Date and time when table storage was last modified
native_last_data_update
Last refresh time for materialized views, snapshots, and clones when available
parent_container
The database_schema resource containing the table
database_technology
Always bigquery
database_schema
Parent schema (BigQuery dataset name)
meta
Reserved for labels and metadata when available
Column (column)
name
The column name. Nested STRUCT fields use their full field path, for example profile.city
type
Always column
subtype
struct for root STRUCT columns
description
Column description when available
parent_name
The table name
parent_container
The table resource containing the column
database_technology
Always bigquery
database_schema
Parent schema (BigQuery dataset name)
Google Sheet (google_sheet)
Google Sheets are discovered when they are referenced as external tables in BigQuery with format = 'GOOGLE_SHEETS'.
name
Always Google Sheet
type
Always google_sheet
external_links
Link to the referenced Google Sheet
Hex Project (hex_project)
Hex projects are detected from BigQuery query history when Hex query metadata is present and the corresponding Hex project already exists in Euno.
name
The Hex project name when present in BigQuery query metadata
type
Always hex_project
Hex projects also receive internal BigQuery query metadata used for lineage. Which BigQuery tables and columns a Hex project depends on appears under Lineage and in the Relationships section below, not as named fields in resource details.
BigQuery Usage & Optimization Properties
Usage and cost optimization properties are derived from BigQuery query history.
For base tables and materialized views, read and write usage properties are derived from INFORMATION_SCHEMA.JOBS and INFORMATION_SCHEMA.JOBS_BY_PROJECT. For subtype = 'view', total_read_queries_{14d,30d,60d} reflects queries that referenced the view. Other read optimization properties, such as bytes, slots, and runtime, reflect underlying table processing and may differ.
Read Properties
total_read_queries_14d, total_read_queries_30d, total_read_queries_60d
Total number of read queries referencing the resource over the last 14, 30, or 60 days. Applies to tables and columns.
total_read_slots_14d, total_read_slots_30d, total_read_slots_60d
Total slots consumed by read queries referencing the table over the last 14, 30, or 60 days.
average_read_slots_14d, average_read_slots_30d, average_read_slots_60d
Average slots consumed per read query referencing the table over the last 14, 30, or 60 days.
total_read_bytes_processed_14d, total_read_bytes_processed_30d, total_read_bytes_processed_60d
Total bytes processed by read queries referencing the table over the last 14, 30, or 60 days.
average_read_bytes_processed_14d, average_read_bytes_processed_30d, average_read_bytes_processed_60d
Average bytes processed per read query referencing the table over the last 14, 30, or 60 days.
total_read_runtime_14d, total_read_runtime_30d, total_read_runtime_60d
Total runtime of read queries referencing the table over the last 14, 30, or 60 days.
total_read_cost_14d, total_read_cost_30d, total_read_cost_60d
Estimated read cost over the last 14, 30, or 60 days, based on the configured cost per slot hour.
average_read_cost_14d, average_read_cost_30d, average_read_cost_60d
Estimated average read cost per query over the last 14, 30, or 60 days.
distinct_users_14d, distinct_users_30d, distinct_users_60d
Number of distinct users who queried the table or column over the last 14, 30, or 60 days.
distinct_impressions_users_14d, distinct_impressions_users_30d, distinct_impressions_users_60d
Number of distinct users from query-history based column impressions over the last 14, 30, or 60 days.
Write Properties
total_write_queries_14d, total_write_queries_30d, total_write_queries_60d
Total number of write operations involving the table over the last 14, 30, or 60 days.
total_write_slots_14d, total_write_slots_30d, total_write_slots_60d
Total slots consumed by write operations involving the table over the last 14, 30, or 60 days.
average_write_slots_14d, average_write_slots_30d, average_write_slots_60d
Average slots consumed per write operation involving the table over the last 14, 30, or 60 days.
total_write_bytes_processed_14d, total_write_bytes_processed_30d, total_write_bytes_processed_60d
Total bytes processed by write operations involving the table over the last 14, 30, or 60 days.
average_write_bytes_processed_14d, average_write_bytes_processed_30d, average_write_bytes_processed_60d
Average bytes processed per write operation involving the table over the last 14, 30, or 60 days.
total_write_runtime_14d, total_write_runtime_30d, total_write_runtime_60d
Total runtime of write operations involving the table over the last 14, 30, or 60 days.
total_write_cost_14d, total_write_cost_30d, total_write_cost_60d
Estimated write cost over the last 14, 30, or 60 days, based on the configured cost per slot hour.
Storage Properties
Storage properties reflect the current size of the table as stored in BigQuery.
row_count
Number of rows in the table.
volume
Current logical storage volume in bytes.
Relationships
gcp_project
has child
database
The BigQuery project is represented as a database under its GCP project.
database
has child
database_schema
BigQuery datasets are represented as database_schema resources under the BigQuery project.
database_schema
has child
table
BigQuery tables, views, materialized views, external tables, snapshots, and clones are contained in datasets.
table
has child
column
BigQuery columns and nested STRUCT fields are contained in tables.
table
has upstream
table
For views and materialized views, lineage is based on SQL analysis. For base tables, lineage can be inferred from BigQuery query history.
column
has upstream_fields
column
Column-level lineage is based on SQL analysis and BigQuery query history.
table
has upstream_fields
column
Statement-level dependencies include fields used by clauses such as JOIN, WHERE, GROUP BY, HAVING, QUALIFY, and ORDER BY, even when those fields are not selected in the final projection.
table
has upstream
google_sheet
External tables with format = 'GOOGLE_SHEETS' depend on the referenced Google Sheet.
hex_project
has upstream
table
Hex project table lineage is inferred from BigQuery query history.
hex_project
has upstream_fields
column
Hex project field lineage is inferred from BigQuery query history.
What to expect from table-level field lineage
For BigQuery views, table-level lineage in Euno may include columns that are used only by SQL logic and are not selected as output columns.
Example:
In this case, table-level lineage for the view can include the order_status column from orders_raw, even though order_status is not part of the projection.
Known Limitations
ARRAY of STRUCT paths through UNNEST are supported, but element-level instance or index lineage is not tracked.
Fields accessed via dynamic SQL or UDFs may not be captured.
Some complex UNNEST operations with multiple levels may not fully resolve.
Cross-project nested field lineage is most accurate when BigQuery metadata and query history are available for all referenced projects.
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