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BigQuery

Ingesting metadata from BigQuery requires using the bigquery module. Certified

Important Capabilities​

CapabilityStatusNotes
Asset Containers✅Enabled by default. Supported for types - Project, Dataset.
Classification✅Optionally enabled via classification.enabled.
Column-level Lineage✅Optionally enabled via configuration.
Data Profiling✅Optionally enabled via configuration.
Dataset Usage✅Enabled by default, can be disabled via configuration include_usage_statistics.
Descriptions✅Enabled by default.
Detect Deleted Entities✅Enabled by default via stateful ingestion.
Domains✅Supported via the domain config field.
Partition Support✅Enabled by default, partition keys and clustering keys are supported.
Platform Instance❌Platform instance is pre-set to the BigQuery project id.
Schema Metadata✅Enabled by default.
Table-Level Lineage✅Optionally enabled via configuration.
Test Connection✅Enabled by default.

Prerequisites​

To understand how BigQuery ingestion needs to be set up, first familiarize yourself with the concepts in the diagram below:

There are two important concepts to understand and identify:

  • Extractor Project: This is the project associated with a service-account, whose credentials you will be configuring in the connector. The connector uses this service-account to run jobs (including queries) within the project.
  • Bigquery Projects are the projects from which table metadata, lineage, usage, and profiling data need to be collected. By default, the extractor project is included in the list of projects that DataHub collects metadata from, but you can control that by passing in a specific list of project ids that you want to collect metadata from. Read the configuration section below to understand how to limit the list of projects that DataHub extracts metadata from.

Create a datahub profile in GCP​

  1. Create a custom role for datahub as per BigQuery docs.
  2. Follow the sections below to grant permissions to this role on this project and other projects.
Basic Requirements (needed for metadata ingestion)​
  1. Identify your Extractor Project where the service account will run queries to extract metadata.
permission                      Description                                                                                                                        Capability                                                              
bigquery.jobs.create          Run jobs (e.g. queries) within the project. This only needs for the extractor project where the service account belongs                                                                                                                       
bigquery.jobs.list            Manage the queries that the service account has sent. This only needs for the extractor project where the service account belongs                                                                                                             
bigquery.readsessions.create  Create a session for streaming large results. This only needs for the extractor project where the service account belongs                                                                                                                     
bigquery.readsessions.getDataGet data from the read session. This only needs for the extractor project where the service account belongs                      
  1. Grant the following permissions to the Service Account on every project where you would like to extract metadata from
info

If you have multiple projects in your BigQuery setup, the role should be granted these permissions in each of the projects.

PermissionDescriptionCapabilityDefault GCP Role Which Contains This Permission
bigquery.datasets.getRetrieve metadata about a dataset.Table Metadata Extractionroles/bigquery.metadataViewer
bigquery.datasets.getIamPolicyRead a dataset's IAM permissions.Table Metadata Extractionroles/bigquery.metadataViewer
bigquery.tables.listList BigQuery tables.Table Metadata Extractionroles/bigquery.metadataViewer
bigquery.tables.getRetrieve metadata for a table.Table Metadata Extractionroles/bigquery.metadataViewer
bigquery.routines.getGet Routines. Needs to retrieve metadata for a table from system table.Table Metadata Extractionroles/bigquery.metadataViewer
bigquery.routines.listList Routines. Needs to retrieve metadata for a table from system table.Table Metadata Extractionroles/bigquery.metadataViewer
resourcemanager.projects.getGet project metadata.Table Metadata Extractionroles/bigquery.metadataViewer
resourcemanager.projects.listSearch projects. Needed if not setting project_ids.Table Metadata Extractionroles/bigquery.metadataViewer
bigquery.jobs.listAllList all jobs (queries) submitted by any user. Needs for Lineage extraction.Lineage Extraction/Usage Extractionroles/bigquery.resourceViewer
logging.logEntries.listFetch log entries for lineage/usage data. Not required if use_exported_bigquery_audit_metadata is enabled.Lineage Extraction/Usage Extractionroles/logging.privateLogViewer
logging.privateLogEntries.listFetch log entries for lineage/usage data. Not required if use_exported_bigquery_audit_metadata is enabled.Lineage Extraction/Usage Extractionroles/logging.privateLogViewer
bigquery.tables.getDataAccess table data to extract storage size, last updated at, data profiles etc.Profiling
datacatalog.policyTags.getOptional Get policy tags for columns with associated policy tags. This permission is required only if extract_policy_tags_from_catalog is enabled.Policy Tag Extractionroles/datacatalog.viewer

Create a service account in the Extractor Project​

  1. Setup a ServiceAccount as per BigQuery docs and assign the previously created role to this service account.
  2. Download a service account JSON keyfile. Example credential file:
{
  "type": "service_account",
  "project_id": "project-id-1234567",
  "private_key_id": "d0121d0000882411234e11166c6aaa23ed5d74e0",
  "private_key": "-----BEGIN PRIVATE KEY-----\nMIIyourkey\n-----END PRIVATE KEY-----",
  "client_email": "test@suppproject-id-1234567.iam.gserviceaccount.com",
  "client_id": "113545814931671546333",
  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
  "token_uri": "https://oauth2.googleapis.com/token",
  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/test%suppproject-id-1234567.iam.gserviceaccount.com"
}

  1. To provide credentials to the source, you can either:

    Set an environment variable:

    $ export GOOGLE_APPLICATION_CREDENTIALS="/path/to/keyfile.json"

    or

    Set credential config in your source based on the credential json file. For example:

    credential:
      project_id: project-id-1234567
      private_key_id: "d0121d0000882411234e11166c6aaa23ed5d74e0"
      private_key: "-----BEGIN PRIVATE KEY-----\nMIIyourkey\n-----END PRIVATE KEY-----\n"
      client_email: "test@suppproject-id-1234567.iam.gserviceaccount.com"
      client_id: "123456678890"
Profiling Requirements​

To profile BigQuery external tables backed by Google Drive document, you need to grant document's "Viewer" access to service account's email address (client_email in credentials json file). To find the Google Drive document linked to BigQuery table, open the BigQuery console, locate the needed table, select "Details" from the drop-down menu in the top-right corner and refer "Source" field . To share access of Google Drive document, open the document, click "Share" in the top-right corner, add the service account's email address that needs "Viewer" access. Google Drive Sharing Dialog

Lineage and Usage Computation Details​

DataHub's BigQuery connector supports two approaches for extracting lineage and usage statistics:

Modern Approach (Default): use_queries_v2: true​

Recommended for most users - Uses BigQuery's Information Schema for efficient metadata extraction.

  • Data Source: BigQuery Information Schema (INFORMATION_SCHEMA.JOBS* tables)
  • Features:
    • Advanced lineage extraction using SQL parsing
    • Query entities with full query text
    • Query popularity statistics and rankings
    • Multi-region support via region_qualifiers
    • Table and column-level usage statistics
  • Requirements:
    • bigquery.jobs.listAll permission on target projects
    • No additional Cloud Logging permissions needed

Configuration:

source:
  type: bigquery
  config:
    use_queries_v2: true # Default
    include_queries: true # Enable query entities
    include_query_usage_statistics: true # Query popularity stats
    region_qualifiers: ["region-us", "region-eu"] # Multi-region support

Legacy Approach: use_queries_v2: false​

Use when you need specific legacy features - Processes BigQuery audit logs for metadata extraction.

  • Data Source: BigQuery audit logs (two options below)
  • Features:
    • Basic table-level lineage and usage statistics
    • upstream_lineage_in_report debugging feature
    • Works with existing audit log exports

Two data source options:

Option 1: Google Cloud Logging API (Default)​
source:
  type: bigquery
  config:
    use_queries_v2: false
    use_exported_bigquery_audit_metadata: false # Default
  • Requirements: logging.logEntries.list and logging.privateLogEntries.list permissions
  • Limitations: API rate limits, potential costs for large volumes
Option 2: Pre-exported Audit Logs in BigQuery Tables​
source:
  type: bigquery
  config:
    use_queries_v2: false
    use_exported_bigquery_audit_metadata: true
    bigquery_audit_metadata_datasets:
      - "my-project.audit_dataset"
      - "another-project.audit_logs"
  • Requirements:
    • Pre-exported audit logs in BigQuery tables
    • Tables must be named cloudaudit_googleapis_com_data_access
    • Only protoPayloads with type.googleapis.com/google.cloud.audit.BigQueryAuditMetadata are supported
  • Benefits: No Cloud Logging API limits, better for large-scale ingestion
  • Setup: Follow BigQuery audit logs export guide
  • Note: The bigquery_audit_metadata_datasets parameter accepts datasets in $PROJECT.$DATASET format, allowing lineage computation from multiple projects.

Profiling Details​

For performance reasons, we only profile the latest partition for partitioned tables and the latest shard for sharded tables. You can set partition explicitly with partition.partition_datetime property if you want, though note that partition config will be applied to all partitioned tables.

Caveats​

  • For materialized views, lineage is dependent on logs being retained. If your GCP logging is retained for 30 days (default) and 30 days have passed since the creation of the materialized view we won't be able to get lineage for them.

CLI based Ingestion​

Starter Recipe​

Check out the following recipe to get started with ingestion! See below for full configuration options.

For general pointers on writing and running a recipe, see our main recipe guide.

source:
  type: bigquery
  config:
    dataset_pattern:
      allow:
        - finance_bq_dataset
    table_pattern:
      deny:
        # The exact name of the table is revenue_table_name
        # The reason we have this `.*` at the beginning is because the current implmenetation of table_pattern is testing
        # project_id.dataset_name.table_name
        # We will improve this in the future
        - .*revenue_table_name
    include_table_lineage: true
    include_usage_statistics: true
    profiling:
      enabled: true
      profile_table_level_only: true

sink:
  # sink configs

Config Details​

Note that a . is used to denote nested fields in the YAML recipe.

FieldDescription
bucket_duration
Enum
One of: "DAY", "HOUR"
column_limit
integer
Maximum number of columns to process in a table. This is a low level config property which should be touched with care. This restriction is needed because excessively wide tables can result in failure to ingest the schema.
Default: 300
convert_urns_to_lowercase
boolean
Whether to convert dataset urns to lowercase.
Default: False
debug_include_full_payloads
boolean
Include full payload into events. It is only for debugging and internal use.
Default: False
enable_legacy_sharded_table_support
boolean
Use the legacy sharded table urn suffix added.
Default: True
enable_stateful_lineage_ingestion
boolean
Enable stateful lineage ingestion. This will store lineage window timestamps after successful lineage ingestion. and will not run lineage ingestion for same timestamps in subsequent run.
Default: True
enable_stateful_profiling
boolean
Enable stateful profiling. This will store profiling timestamps per dataset after successful profiling. and will not run profiling again in subsequent run if table has not been updated.
Default: True
enable_stateful_usage_ingestion
boolean
Enable stateful lineage ingestion. This will store usage window timestamps after successful usage ingestion. and will not run usage ingestion for same timestamps in subsequent run.
Default: True
end_time
string(date-time)
Latest date of lineage/usage to consider. Default: Current time in UTC
exclude_empty_projects
boolean
Option to exclude empty projects from being ingested.
Default: False
extra_client_options
object
Additional options to pass to google.cloud.logging_v2.client.Client.
Default: {}
extract_column_lineage
boolean
If enabled, generate column level lineage. Requires lineage_use_sql_parser to be enabled.
Default: False
extract_lineage_from_catalog
boolean
This flag enables the data lineage extraction from Data Lineage API exposed by Google Data Catalog. NOTE: This extractor can't build views lineage. It's recommended to enable the view's DDL parsing. Read the docs to have more information about: https://cloud.google.com/data-catalog/docs/concepts/about-data-lineage
Default: False
extract_policy_tags_from_catalog
boolean
This flag enables the extraction of policy tags from the Google Data Catalog API. When enabled, the extractor will fetch policy tags associated with BigQuery table columns. For more information about policy tags and column-level security, refer to the documentation: https://cloud.google.com/bigquery/docs/column-level-security-intro
Default: False
include_column_lineage_with_gcs
boolean
When enabled, column-level lineage will be extracted from the gcs.
Default: True
include_data_platform_instance
boolean
Whether to create a DataPlatformInstance aspect, equal to the BigQuery project id. If enabled, will cause redundancy in the browse path for BigQuery entities in the UI, because the project id is represented as the top-level container.
Default: False
include_external_url
boolean
Whether to populate BigQuery Console url to Datasets/Tables
Default: True
include_queries
boolean
If enabled, generate query entities associated with lineage edges. Only applicable if use_queries_v2 is enabled.
Default: True
include_query_usage_statistics
boolean
If enabled, generate query popularity statistics. Only applicable if use_queries_v2 is enabled.
Default: True
include_schema_metadata
boolean
Whether to ingest the BigQuery schema, i.e. projects, schemas, tables, and views.
Default: True
include_table_constraints
boolean
Whether to ingest table constraints. If you know you don't use table constraints, you can disable it to save one extra query per dataset. In general it should be enabled
Default: True
include_table_lineage
One of boolean, null
Option to enable/disable lineage generation. Is enabled by default.
Default: True
include_table_location_lineage
boolean
If the source supports it, include table lineage to the underlying storage location.
Default: True
include_table_snapshots
One of boolean, null
Whether table snapshots should be ingested.
Default: True
include_tables
boolean
Whether tables should be ingested.
Default: True
include_usage_statistics
boolean
Generate usage statistic
Default: True
include_view_column_lineage
boolean
Populates column-level lineage for view->view and table->view lineage using DataHub's sql parser. Requires include_view_lineage to be enabled.
Default: True
include_view_lineage
boolean
Populates view->view and table->view lineage using DataHub's sql parser.
Default: True
include_views
boolean
Whether views should be ingested.
Default: True
incremental_lineage
boolean
When enabled, emits lineage as incremental to existing lineage already in DataHub. When disabled, re-states lineage on each run.
Default: False
lineage_sql_parser_use_raw_names
boolean
This parameter ignores the lowercase pattern stipulated in the SQLParser. NOTE: Ignored if lineage_use_sql_parser is False.
Default: False
lineage_use_sql_parser
boolean
Use sql parser to resolve view/table lineage.
Default: True
log_page_size
integer
The number of log item will be queried per page for lineage collection
Default: 1000
match_fully_qualified_names
boolean
[deprecated] Whether dataset_pattern is matched against fully qualified dataset name <project_id>.<dataset_name>.
Default: True
max_query_duration
string(duration)
Correction to pad start_time and end_time with. For handling the case where the read happens within our time range but the query completion event is delayed and happens after the configured end time.
Default: PT15M
max_threads_dataset_parallelism
integer
Number of worker threads to use to parallelize BigQuery Dataset Metadata Extraction. Set to 1 to disable.
Default: 20
number_of_datasets_process_in_batch_if_profiling_enabled
integer
Number of partitioned table queried in batch when getting metadata. This is a low level config property which should be touched with care. This restriction is needed because we query partitions system view which throws error if we try to touch too many tables.
Default: 1000
options
object
Any options specified here will be passed to SQLAlchemy.create_engine as kwargs.
platform_instance
One of string, null
The instance of the platform that all assets produced by this recipe belong to. This should be unique within the platform. See https://docs.datahub.com/docs/platform-instances/ for more details.
Default: None
project_on_behalf
One of string, null
[Advanced] The BigQuery project in which queries are executed. Will be passed when creating a job. If not passed, falls back to the project associated with the service account.
Default: None
rate_limit
boolean
Should we rate limit requests made to API.
Default: False
requests_per_min
integer
Used to control number of API calls made per min. Only used when rate_limit is set to True.
Default: 60
scheme
string
Default: bigquery
sharded_table_pattern
string
The regex pattern to match sharded tables and group as one table. This is a very low level config parameter, only change if you know what you are doing,
Default: ((.+\D)[_$]?)?(\d\d\d\d(?:0[1-9]|1[0-2])(?:0[1-9]|...
start_time
string(date-time)
Earliest date of lineage/usage to consider. Default: Last full day in UTC (or hour, depending on bucket_duration). You can also specify relative time with respect to end_time such as '-7 days' Or '-7d'.
Default: None
temp_table_dataset_prefix
string
If you are creating temp tables in a dataset with a particular prefix you can use this config to set the prefix for the dataset. This is to support workflows from before bigquery's introduction of temp tables. By default we use _ because of datasets that begin with an underscore are hidden by default https://cloud.google.com/bigquery/docs/datasets#dataset-naming.
Default: _
upstream_lineage_in_report
boolean
Useful for debugging lineage information. Set to True to see the raw lineage created internally. Only works with legacy approach (use_queries_v2: False).
Default: False
use_date_sharded_audit_log_tables
boolean
Whether to read date sharded tables or time partitioned tables when extracting usage from exported audit logs.
Default: False
use_exported_bigquery_audit_metadata
boolean
When configured, use BigQueryAuditMetadata in bigquery_audit_metadata_datasets to compute lineage information.
Default: False
use_file_backed_cache
boolean
Whether to use a file backed cache for the view definitions.
Default: True
use_queries_v2
boolean
If enabled, uses the new queries extractor to extract queries from bigquery.
Default: True
use_tables_list_query_v2
boolean
List tables using an improved query that extracts partitions and last modified timestamps more accurately. Requires the ability to read table data. Automatically enabled when profiling is enabled.
Default: False
env
string
The environment that all assets produced by this connector belong to
Default: PROD
bigquery_audit_metadata_datasets
One of array, null
A list of datasets that contain a table named cloudaudit_googleapis_com_data_access which contain BigQuery audit logs, specifically, those containing BigQueryAuditMetadata. It is recommended that the project of the dataset is also specified, for example, projectA.datasetB.
Default: None
bigquery_audit_metadata_datasets.string
string
capture_dataset_label_as_tag
One of boolean, AllowDenyPattern
Capture BigQuery dataset labels as DataHub tag
Default: False
capture_dataset_label_as_tag.ignoreCase
One of boolean, null
Whether to ignore case sensitivity during pattern matching.
Default: True
capture_dataset_label_as_tag.allow
array
List of regex patterns to include in ingestion
Default: ['.*']
capture_dataset_label_as_tag.allow.string
string
capture_dataset_label_as_tag.deny
array
List of regex patterns to exclude from ingestion.
Default: []
capture_dataset_label_as_tag.deny.string
string
capture_table_label_as_tag
One of boolean, AllowDenyPattern
Capture BigQuery table labels as DataHub tag
Default: False
capture_table_label_as_tag.ignoreCase
One of boolean, null
Whether to ignore case sensitivity during pattern matching.
Default: True
capture_table_label_as_tag.allow
array
List of regex patterns to include in ingestion
Default: ['.*']
capture_table_label_as_tag.allow.string
string
capture_table_label_as_tag.deny
array
List of regex patterns to exclude from ingestion.
Default: []
capture_table_label_as_tag.deny.string
string
capture_view_label_as_tag
One of boolean, AllowDenyPattern
Capture BigQuery view labels as DataHub tag
Default: False
capture_view_label_as_tag.ignoreCase
One of boolean, null
Whether to ignore case sensitivity during pattern matching.
Default: True
capture_view_label_as_tag.allow
array
List of regex patterns to include in ingestion
Default: ['.*']
capture_view_label_as_tag.allow.string
string
capture_view_label_as_tag.deny
array
List of regex patterns to exclude from ingestion.
Default: []
capture_view_label_as_tag.deny.string
string
credential
One of GCPCredential, null
BigQuery credential informations
Default: None
credential.client_email ❓
string
Client email
credential.client_id ❓
string
Client Id
credential.private_key ❓
string
Private key in a form of '-----BEGIN PRIVATE KEY-----\nprivate-key\n-----END PRIVATE KEY-----\n'
credential.private_key_id ❓
string
Private key id
credential.auth_provider_x509_cert_url
string
Auth provider x509 certificate url
Default: https://www.googleapis.com/oauth2/v1/certs
credential.auth_uri
string
Authentication uri
Default: https://accounts.google.com/o/oauth2/auth
credential.client_x509_cert_url
One of string, null
If not set it will be default to https://www.googleapis.com/robot/v1/metadata/x509/client_email
Default: None
credential.project_id
One of string, null
Project id to set the credentials
Default: None
credential.token_uri
string
Token uri
Default: https://oauth2.googleapis.com/token
credential.type
string
Authentication type
Default: service_account
dataset_pattern
AllowDenyPattern
A class to store allow deny regexes
dataset_pattern.ignoreCase
One of boolean, null
Whether to ignore case sensitivity during pattern matching.
Default: True
domain
map(str,AllowDenyPattern)
A class to store allow deny regexes
domain.key.allow
array
List of regex patterns to include in ingestion
Default: ['.*']
domain.key.allow.string
string
domain.key.ignoreCase
One of boolean, null
Whether to ignore case sensitivity during pattern matching.
Default: True
domain.key.deny
array
List of regex patterns to exclude from ingestion.
Default: []
domain.key.deny.string
string
gcs_lineage_config
GcsLineageProviderConfig
Any source that produces gcs lineage from/to Datasets should inherit this class.
gcs_lineage_config.ignore_non_path_spec_path
boolean
Ignore paths that are not match in path_specs. It only applies if path_specs are specified.
Default: False
gcs_lineage_config.strip_urls
boolean
Strip filename from gcs url. It only applies if path_specs are not specified.
Default: True
gcs_lineage_config.path_specs
array
List of PathSpec. See below the details about PathSpec
Default: []
gcs_lineage_config.path_specs.PathSpec
PathSpec
gcs_lineage_config.path_specs.PathSpec.include ❓
string
Path to table. Name variable {table} is used to mark the folder with dataset. In absence of {table}, file level dataset will be created. Check below examples for more details.
gcs_lineage_config.path_specs.PathSpec.allow_double_stars
boolean
Allow double stars in the include path. This can affect performance significantly if enabled
Default: False
gcs_lineage_config.path_specs.PathSpec.autodetect_partitions
boolean
Autodetect partition(s) from the path. If set to true, it will autodetect partition key/value if the folder format is {partition_key}={partition_value} for example year=2024
Default: True
gcs_lineage_config.path_specs.PathSpec.default_extension
One of string, null
For files without extension it will assume the specified file type. If it is not set the files without extensions will be skipped.
Default: None
gcs_lineage_config.path_specs.PathSpec.enable_compression
boolean
Enable or disable processing compressed files. Currently .gz and .bz files are supported.
Default: True
gcs_lineage_config.path_specs.PathSpec.include_hidden_folders
boolean
Include hidden folders in the traversal (folders starting with . or _
Default: False
gcs_lineage_config.path_specs.PathSpec.sample_files
boolean
Not listing all the files but only taking a handful amount of sample file to infer the schema. File count and file size calculation will be disabled. This can affect performance significantly if enabled
Default: True
gcs_lineage_config.path_specs.PathSpec.table_name
One of string, null
Display name of the dataset.Combination of named variables from include path and strings
Default: None
gcs_lineage_config.path_specs.PathSpec.traversal_method
Enum
One of: "ALL", "MIN_MAX", "MAX"
gcs_lineage_config.path_specs.PathSpec.exclude
One of array, null
list of paths in glob pattern which will be excluded while scanning for the datasets
Default: []
gcs_lineage_config.path_specs.PathSpec.exclude.string
string
gcs_lineage_config.path_specs.PathSpec.file_types
array
Files with extenstions specified here (subset of default value) only will be scanned to create dataset. Other files will be omitted.
Default: ['csv', 'tsv', 'json', 'parquet', 'avro']
gcs_lineage_config.path_specs.PathSpec.file_types.string
string
gcs_lineage_config.path_specs.PathSpec.tables_filter_pattern
AllowDenyPattern
A class to store allow deny regexes
gcs_lineage_config.path_specs.PathSpec.tables_filter_pattern.ignoreCase
One of boolean, null
Whether to ignore case sensitivity during pattern matching.
Default: True
profile_pattern
AllowDenyPattern
A class to store allow deny regexes
profile_pattern.ignoreCase
One of boolean, null
Whether to ignore case sensitivity during pattern matching.
Default: True
project_id_pattern
AllowDenyPattern
A class to store allow deny regexes
project_id_pattern.ignoreCase
One of boolean, null
Whether to ignore case sensitivity during pattern matching.
Default: True
project_ids
array
Ingests specified project_ids. Use this property if you want to specify what projects to ingest or don't want to give project resourcemanager.projects.list to your service account. Overrides project_id_pattern.
project_ids.string
string
project_labels
array
Ingests projects with the specified labels. Set value in the format of key:value. Use this property to define which projects to ingest basedon project-level labels. If project_ids or project_id is set, this configuration has no effect. The ingestion process filters projects by label first, and then applies the project_id_pattern.
project_labels.string
string
region_qualifiers
array
BigQuery regions to be scanned for bigquery jobs when using use_queries_v2. See this for details.
Default: ['region-us', 'region-eu']
region_qualifiers.string
string
table_pattern
AllowDenyPattern
A class to store allow deny regexes
table_pattern.ignoreCase
One of boolean, null
Whether to ignore case sensitivity during pattern matching.
Default: True
table_snapshot_pattern
AllowDenyPattern
A class to store allow deny regexes
table_snapshot_pattern.ignoreCase
One of boolean, null
Whether to ignore case sensitivity during pattern matching.
Default: True
usage
BigQueryUsageConfig
usage.apply_view_usage_to_tables
boolean
Whether to apply view's usage to its base tables. If set to False, uses sql parser and applies usage to views / tables mentioned in the query. If set to True, usage is applied to base tables only.
Default: False
usage.bucket_duration
Enum
One of: "DAY", "HOUR"
usage.end_time
string(date-time)
Latest date of lineage/usage to consider. Default: Current time in UTC
usage.format_sql_queries
boolean
Whether to format sql queries
Default: False
usage.include_operational_stats
boolean
Whether to display operational stats.
Default: True
usage.include_read_operational_stats
boolean
Whether to report read operational stats. Experimental.
Default: False
usage.include_top_n_queries
boolean
Whether to ingest the top_n_queries.
Default: True
usage.max_query_duration
string(duration)
Correction to pad start_time and end_time with. For handling the case where the read happens within our time range but the query completion event is delayed and happens after the configured end time.
Default: PT15M
usage.start_time
string(date-time)
Earliest date of lineage/usage to consider. Default: Last full day in UTC (or hour, depending on bucket_duration). You can also specify relative time with respect to end_time such as '-7 days' Or '-7d'.
Default: None
usage.top_n_queries
integer
Number of top queries to save to each table.
Default: 10
usage.user_email_pattern
AllowDenyPattern
A class to store allow deny regexes
usage.user_email_pattern.ignoreCase
One of boolean, null
Whether to ignore case sensitivity during pattern matching.
Default: True
view_pattern
AllowDenyPattern
A class to store allow deny regexes
view_pattern.ignoreCase
One of boolean, null
Whether to ignore case sensitivity during pattern matching.
Default: True
classification
ClassificationConfig
classification.enabled
boolean
Whether classification should be used to auto-detect glossary terms
Default: False
classification.info_type_to_term
map(str,string)
classification.max_workers
integer
Number of worker processes to use for classification. Set to 1 to disable.
Default: 4
classification.sample_size
integer
Number of sample values used for classification.
Default: 100
classification.classifiers
array
Classifiers to use to auto-detect glossary terms. If more than one classifier, infotype predictions from the classifier defined later in sequence take precedance.
Default: [{'type': 'datahub', 'config': None}]
classification.classifiers.DynamicTypedClassifierConfig
DynamicTypedClassifierConfig
classification.classifiers.DynamicTypedClassifierConfig.type ❓
string
The type of the classifier to use. For DataHub, use datahub
classification.classifiers.DynamicTypedClassifierConfig.config
One of object, null
The configuration required for initializing the classifier. If not specified, uses defaults for classifer type.
Default: None
classification.column_pattern
AllowDenyPattern
A class to store allow deny regexes
classification.column_pattern.ignoreCase
One of boolean, null
Whether to ignore case sensitivity during pattern matching.
Default: True
classification.table_pattern
AllowDenyPattern
A class to store allow deny regexes
classification.table_pattern.ignoreCase
One of boolean, null
Whether to ignore case sensitivity during pattern matching.
Default: True
profiling
GEProfilingConfig
profiling.catch_exceptions
boolean
Default: True
profiling.enabled
boolean
Whether profiling should be done.
Default: False
profiling.field_sample_values_limit
integer
Upper limit for number of sample values to collect for all columns.
Default: 20
profiling.include_field_distinct_count
boolean
Whether to profile for the number of distinct values for each column.
Default: True
profiling.include_field_distinct_value_frequencies
boolean
Whether to profile for distinct value frequencies.
Default: False
profiling.include_field_histogram
boolean
Whether to profile for the histogram for numeric fields.
Default: False
profiling.include_field_max_value
boolean
Whether to profile for the max value of numeric columns.
Default: True
profiling.include_field_mean_value
boolean
Whether to profile for the mean value of numeric columns.
Default: True
profiling.include_field_median_value
boolean
Whether to profile for the median value of numeric columns.
Default: True
profiling.include_field_min_value
boolean
Whether to profile for the min value of numeric columns.
Default: True
profiling.include_field_null_count
boolean
Whether to profile for the number of nulls for each column.
Default: True
profiling.include_field_quantiles
boolean
Whether to profile for the quantiles of numeric columns.
Default: False
profiling.include_field_sample_values
boolean
Whether to profile for the sample values for all columns.
Default: True
profiling.include_field_stddev_value
boolean
Whether to profile for the standard deviation of numeric columns.
Default: True
profiling.limit
One of integer, null
Max number of documents to profile. By default, profiles all documents.
Default: None
profiling.max_number_of_fields_to_profile
One of integer, null
A positive integer that specifies the maximum number of columns to profile for any table. None implies all columns. The cost of profiling goes up significantly as the number of columns to profile goes up.
Default: None
profiling.max_workers
integer
Number of worker threads to use for profiling. Set to 1 to disable.
Default: 20
profiling.offset
One of integer, null
Offset in documents to profile. By default, uses no offset.
Default: None
profiling.partition_datetime
One of string(date-time), null
If specified, profile only the partition which matches this datetime. If not specified, profile the latest partition. Only Bigquery supports this.
Default: None
profiling.partition_profiling_enabled
boolean
Whether to profile partitioned tables. Only BigQuery and Aws Athena supports this. If enabled, latest partition data is used for profiling.
Default: True
profiling.profile_external_tables
boolean
Whether to profile external tables. Only Snowflake and Redshift supports this.
Default: False
profiling.profile_if_updated_since_days
One of number, null
Profile table only if it has been updated since these many number of days. If set to null, no constraint of last modified time for tables to profile. Supported only in snowflake and BigQuery.
Default: None
profiling.profile_nested_fields
boolean
Whether to profile complex types like structs, arrays and maps.
Default: False
profiling.profile_table_level_only
boolean
Whether to perform profiling at table-level only, or include column-level profiling as well.
Default: False
profiling.profile_table_row_count_estimate_only
boolean
Use an approximate query for row count. This will be much faster but slightly less accurate. Only supported for Postgres and MySQL.
Default: False
profiling.profile_table_row_limit
One of integer, null
Profile tables only if their row count is less than specified count. If set to null, no limit on the row count of tables to profile. Supported only in Snowflake, BigQuery. Supported for Oracle based on gathered stats.
Default: 5000000
profiling.profile_table_size_limit
One of integer, null
Profile tables only if their size is less than specified GBs. If set to null, no limit on the size of tables to profile. Supported only in Snowflake, BigQuery and Databricks. Supported for Oracle based on calculated size from gathered stats.
Default: 5
profiling.query_combiner_enabled
boolean
This feature is still experimental and can be disabled if it causes issues. Reduces the total number of queries issued and speeds up profiling by dynamically combining SQL queries where possible.
Default: True
profiling.report_dropped_profiles
boolean
Whether to report datasets or dataset columns which were not profiled. Set to True for debugging purposes.
Default: False
profiling.sample_size
integer
Number of rows to be sampled from table for column level profiling.Applicable only if use_sampling is set to True.
Default: 10000
profiling.turn_off_expensive_profiling_metrics
boolean
Whether to turn off expensive profiling or not. This turns off profiling for quantiles, distinct_value_frequencies, histogram & sample_values. This also limits maximum number of fields being profiled to 10.
Default: False
profiling.use_sampling
boolean
Whether to profile column level stats on sample of table. Only BigQuery and Snowflake support this. If enabled, profiling is done on rows sampled from table. Sampling is not done for smaller tables.
Default: True
profiling.operation_config
OperationConfig
profiling.operation_config.lower_freq_profile_enabled
boolean
Whether to do profiling at lower freq or not. This does not do any scheduling just adds additional checks to when not to run profiling.
Default: False
profiling.operation_config.profile_date_of_month
One of integer, null
Number between 1 to 31 for date of month (both inclusive). If not specified, defaults to Nothing and this field does not take affect.
Default: None
profiling.operation_config.profile_day_of_week
One of integer, null
Number between 0 to 6 for day of week (both inclusive). 0 is Monday and 6 is Sunday. If not specified, defaults to Nothing and this field does not take affect.
Default: None
profiling.tags_to_ignore_sampling
One of array, null
Fixed list of tags to ignore sampling. If not specified, tables will be sampled based on use_sampling.
Default: None
profiling.tags_to_ignore_sampling.string
string
stateful_ingestion
One of StatefulStaleMetadataRemovalConfig, null
Default: None
stateful_ingestion.enabled
boolean
Whether or not to enable stateful ingest. Default: True if a pipeline_name is set and either a datahub-rest sink or datahub_api is specified, otherwise False
Default: False
stateful_ingestion.fail_safe_threshold
number
Prevents large amount of soft deletes & the state from committing from accidental changes to the source configuration if the relative change percent in entities compared to the previous state is above the 'fail_safe_threshold'.
Default: 75.0
stateful_ingestion.remove_stale_metadata
boolean
Soft-deletes the entities present in the last successful run but missing in the current run with stateful_ingestion enabled.
Default: True

Code Coordinates​

  • Class Name: datahub.ingestion.source.bigquery_v2.bigquery.BigqueryV2Source
  • Browse on GitHub

Questions

If you've got any questions on configuring ingestion for BigQuery, feel free to ping us on our Slack.