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Teradata

Overview​

Teradata is a DataHub utility or metadata-focused integration. Learn more in the official Teradata documentation.

The DataHub integration for Teradata covers metadata entities and operational objects relevant to this connector. Depending on module capabilities, it can also capture features such as lineage, usage, profiling, ownership, tags, and stateful deletion detection.

Concept Mapping​

While the specific concept mapping is still pending, this shows the generic concept mapping in DataHub.

Source ConceptDataHub ConceptNotes
Platform/account/project scopePlatform Instance, ContainerOrganizes assets within the platform context.
Core technical asset (for example table/view/topic/file)DatasetPrimary ingested technical asset.
Schema fields / columnsSchemaFieldIncluded when schema extraction is supported.
Ownership and collaboration principalsCorpUser, CorpGroupEmitted by modules that support ownership and identity metadata.
Dependencies and processing relationshipsLineage edgesAvailable when lineage extraction is supported and enabled.

Module teradata​

Testing

Important Capabilities​

CapabilityStatusNotes
Asset Containers✅Enabled by default. Supported for types - Database.
Classification✅Optionally enabled via classification.enabled.
Column-level Lineage✅Optionally enabled via configuration.
Data Profiling✅Optionally enabled via configuration.
Dataset Usage✅Optionally enabled via configuration.
Descriptions✅Enabled by default.
Detect Deleted Entities✅Enabled by default when stateful ingestion is turned on.
Domains✅Enabled by default.
Extract Ownership✅Optionally enabled via configuration (extract_ownership).
Platform Instance✅Enabled by default.
Schema Metadata✅Enabled by default.
Table-Level Lineage✅Optionally enabled via configuration.
Test Connection✅Enabled by default.

Overview​

The teradata module ingests metadata from Teradata into DataHub. It is intended for production ingestion workflows and module-specific capabilities are documented below.

This plugin extracts the following:

  • Metadata for databases, schemas, views, and tables
  • Column types associated with each table
  • Table, row, and column statistics via optional SQL profiling

Prerequisites​

  1. Create a user which has access to the database you want to ingest.

    CREATE USER datahub FROM <database> AS PASSWORD = <password> PERM = 20000000;

  2. Create a user with the following privileges:

    GRANT SELECT ON dbc.columns TO datahub;
    GRANT SELECT ON dbc.databases TO datahub;
    GRANT SELECT ON dbc.tables TO datahub;
    GRANT SELECT ON DBC.All_RI_ChildrenV TO datahub;
    GRANT SELECT ON DBC.ColumnsV TO datahub;
    GRANT SELECT ON DBC.IndicesV TO datahub;
    GRANT SELECT ON dbc.TableTextV TO datahub;
    GRANT SELECT ON dbc.TablesV TO datahub;
    GRANT SELECT ON dbc.dbqlogtbl TO datahub; -- if lineage or usage extraction is enabled

    If you want to run profiling, you need to grant select permission on all the tables you want to profile.

  3. For lineage/usage extraction: Enable query logging and set an appropriate query text size (default is 200 chars, may be insufficient).

    To set for all users:

    REPLACE QUERY LOGGING LIMIT SQLTEXT=2000 ON ALL;

    See more here about query logging: https://docs.teradata.com/r/Lake-Database-Reference/Database-Administration/Tracking-Query-Behavior-with-Database-Query-Logging-Operational-DBAs/SQL-Statements-to-Control-Logging/LIMIT-Logging-Options

Install the Plugin​

pip install 'acryl-datahub[teradata]'

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.

pipeline_name: my-teradata-ingestion-pipeline
source:
  type: teradata
  config:
    host_port: "myteradatainstance.teradata.com:1025"
    username: myuser
    password: mypassword
    #database_pattern:
    #  allow:
    #    - "my_database"
    #  ignoreCase: true
    include_table_lineage: true
    include_usage_statistics: true
    stateful_ingestion:
      enabled: true

    # --- Performance options for large installations ---

    # Skip column extraction for tables not altered in the last N days (recommended for
    # scheduled pipelines — set once and never update the recipe).
    #column_extraction_days_back: 3

    # Alternative: skip column extraction for tables unchanged since an absolute timestamp.
    # Set to the start time of the last successful run. Mutually exclusive with
    # column_extraction_days_back.
    #column_extraction_watermark: "2024-06-01T00:00:00Z"

    # Use dbc.ColumnsV for view columns first (faster); fall back to HELP only
    # when a column has an unknown type (e.g. derived expressions).
    #use_dbc_columns_for_views: true

    # Cap profiling to high-priority tables (uses standard GEProfilingConfig).
    #profiling:
    #  enabled: true
    #  limit: 500
    #profile_pattern:
    #  allow:
    #    - "important_db\\..*"

    # Increase if lineage queries against DBC.QryLogV time out (default: 120000 ms).
    #request_timeout_ms: 300000
sink:

Config Details​

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

FieldDescription
host_port ✅
string
host URL
bucket_duration
Enum
One of: "DAY", "HOUR"
column_extraction_days_back
One of integer, null
Skip column extraction for tables/views not altered within the last N days. Computed at runtime as now() - N days, so the recipe never needs updating. A value of 3 for a daily schedule covers up to two missed runs with no gap risk. Mutually exclusive with column_extraction_watermark.
Default: None
column_extraction_watermark
One of string(date-time), null
Skip column extraction for tables/views whose LastAlterTimeStamp is older than this timestamp. Set to the start time of the last successful ingestion run to enable incremental column extraction. Mutually exclusive with column_extraction_days_back. At 13k tables where ~200 change per day this can reduce ingestion from hours to minutes.
Default: None
connect_timeout_ms
integer
Connection timeout in milliseconds when establishing Teradata connections. Default is 30000 (30 seconds).
Default: 30000
convert_urns_to_lowercase
boolean
Whether to convert dataset urns to lowercase.
Default: False
database
One of string, null
database (catalog)
Default: None
default_db
One of string, null
The default database to use for unqualified table names
Default: None
end_time
string(date-time)
Latest date of lineage/usage to consider. Default: Current time in UTC
extract_ownership
boolean
Whether to extract ownership information for tables and views based on their creator. When enabled, the table/view creator from Teradata's system tables will be added as an owner with DATAOWNER type. Ownership is applied using OVERWRITE mode, meaning any existing ownership information (including manually added or modified owners from the UI) will be replaced. Use with caution.
Default: False
include_historical_lineage
boolean
Whether to include historical lineage data from PDCRINFO.DBQLSqlTbl_Hst in addition to current DBC.QryLogV data. This provides access to historical query logs that may have been archived. The historical table existence is checked automatically and gracefully falls back to current data only if not available.
Default: False
include_queries
boolean
Whether to generate query entities for SQL queries. Query entities provide metadata about individual SQL queries including execution timestamps, user information, and query text.
Default: True
include_table_lineage
boolean
Whether to include table lineage in the ingestion. This requires to have the table lineage feature enabled.
Default: False
include_table_location_lineage
boolean
If the source supports it, include table lineage to the underlying storage location.
Default: True
include_tables
boolean
Whether tables should be ingested.
Default: True
include_usage_statistics
boolean
Generate usage statistic.
Default: False
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
Whether to include view lineage in the ingestion. This requires to have the view lineage feature enabled.
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
max_workers
integer
Maximum number of worker threads to use for parallel processing. Controls the level of concurrency for operations like view processing.
Default: 10
options
object
Any options specified here will be passed to SQLAlchemy.create_engine as kwargs. To set connection arguments in the URL, specify them under connect_args.
password
One of string(password), null
password
Default: None
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
request_timeout_ms
integer
Request timeout in milliseconds for Teradata query execution. Increase this when queries against large system tables (e.g., DBC.QryLogV) time out silently and fall back. Default is 120000 (2 minutes).
Default: 120000
scheme
string
database scheme
Default: teradatasql
sqlalchemy_uri
One of string, null
URI of database to connect to. See https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls. Takes precedence over other connection parameters.
Default: None
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
use_dbc_columns_for_views
boolean
When True, attempt to use dbc.ColumnsV for view column metadata (faster bulk fetch) and fall back to HELP statements only for views where any column has a null/unknown ColumnType (e.g., derived expression columns). Can cut HELP calls by 80-90%% for installations where most view columns have explicit types. Set to False (default) to always use HELP for views, which is the conservative but slower approach.
Default: False
use_file_backed_cache
boolean
Whether to use a file backed cache for the view definitions.
Default: True
use_qvci
boolean
Whether to use QVCI to get column information. This is faster but requires to have QVCI enabled.
Default: False
use_server_side_cursors
boolean
Enable server-side cursors for large result sets using SQLAlchemy's stream_results. This reduces memory usage by streaming results from the database server. Automatically falls back to client-side batching if server-side cursors are not supported.
Default: True
username
One of string, null
username
Default: None
env
string
The environment that all assets produced by this connector belong to
Default: PROD
database_pattern
AllowDenyPattern
A class to store allow deny regexes
database_pattern.ignoreCase
One of boolean, null
Whether to ignore case sensitivity during pattern matching.
Default: True
databases
One of array, null
List of databases to ingest. If not specified, all databases will be ingested. Even if this is specified, databases will still be filtered by database_pattern.
Default: None
databases.string
string
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
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
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
usage
BaseUsageConfig
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.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.method
Enum
One of: "ge", "sqlalchemy"
Default: ge
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

Capabilities​

Use the Important Capabilities table above as the source of truth for supported features and whether additional configuration is required.

Large-scale Deployment Tuning​

For Teradata installations with thousands of tables the following options can significantly reduce ingestion time.

Incremental column extraction

The connector compares each table's LastAlterTimeStamp against a watermark and skips column extraction for tables that have not changed. Only altered tables and tables with no recorded alter timestamp are re-extracted. At 13 000 tables where ~200 change per day this typically reduces a multi-hour run to minutes.

Two mutually exclusive options control the watermark (setting both raises a validation error at startup):

  • column_extraction_days_back — recommended for scheduled pipelines. Set once and never update the recipe. A value of 3 covers up to two missed daily runs with no gap risk.

    column_extraction_days_back: 3

  • column_extraction_watermark — for stateful pipelines that track the exact timestamp of the last successful run programmatically.

    column_extraction_watermark: "2024-06-01T00:00:00Z"

Faster view column fetching

By default the connector uses Teradata HELP statements for every view to ensure derived expression columns (e.g. col1 + col2) have correct types. Set use_dbc_columns_for_views: true to attempt a bulk dbc.ColumnsV fetch first and fall back to HELP only for views where any column has an unknown type. This can reduce HELP calls by 80–90 % on installations where most view columns have explicit types.

Profiling at scale

Profiling all tables in a large installation is impractical. Use profiling.limit (part of the standard GEProfilingConfig) to cap how many tables are profiled per run. You can also combine it with profile_pattern to restrict profiling to specific schemas or tables.

profiling:
  enabled: true
  limit: 500
profile_pattern:
  allow:
    - "high_priority_db\\..*"

Lineage query scope

When databases is not set the connector automatically scopes DBC.QryLogV queries to the databases discovered during metadata extraction, filtered by database_pattern. This avoids scanning the entire audit log. You can further restrict the scope with an explicit databases list.

SQL parse cache size

When usage statistics or lineage are enabled, every query row from DBC.QryLogV is parsed with sqlglot to extract table references. Identical query text in a session (e.g. a BI dashboard query that runs thousands of times per day) hits an LRU cache and avoids re-parsing. The default cache holds 1 000 entries, which is too small for production Teradata installations where hundreds of distinct queries each execute thousands of times.

Set the DATAHUB_SQL_PARSE_CACHE_SIZE environment variable before running the pipeline to increase the cache:

export DATAHUB_SQL_PARSE_CACHE_SIZE=50000
datahub ingest -c teradata_recipe.yml

Each cache entry holds a parsed query result in memory. 50 000 entries typically uses 200–500 MB of additional heap depending on query complexity. Start with 10 000 if memory is constrained and increase until cache hit rates stabilise (visible in the ingestion report under sql_parsing_cache_stats).

Connection timeouts

Use request_timeout_ms and connect_timeout_ms to tune the Teradata driver timeouts. Increase request_timeout_ms (default: 120 000 ms) if lineage queries against large DBC.QryLogV tables time out silently.

Limitations​

  • use_dbc_columns_for_views falls back to HELP for any view that contains derived expression columns. Views with only explicit-type columns benefit most from this option.
  • column_extraction_watermark must be managed manually — set it to the start time of the previous successful run. Use column_extraction_days_back instead if you want a self-maintaining schedule-relative window.
  • column_extraction_watermark and column_extraction_days_back are mutually exclusive. Setting both raises a validation error at startup.
  • Profiling capped by profiling.limit does not prioritise tables — they are profiled in the order they are returned by dbc.TablesV. Use profile_pattern to target specific schemas if order matters.

Troubleshooting​

If ingestion fails, validate credentials, permissions, connectivity, and scope filters first. Then review ingestion logs for source-specific errors and adjust configuration accordingly.

If lineage queries fail silently and return no results, increase request_timeout_ms. The default 2-minute timeout can be insufficient for DBC.QryLogV on busy systems with large audit logs.

Code Coordinates​

  • Class Name: datahub.ingestion.source.sql.teradata.TeradataSource
  • Browse on GitHub
Questions?

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

💡 Contributing to this documentation

This page is auto-generated from the underlying source code. To make changes, please edit the relevant source files in the metadata-ingestion directory.

Tip: For quick typo fixes or documentation updates, you can click the ✏️ Edit icon directly in the GitHub UI to open a Pull Request. For larger changes and PR naming conventions, please refer to our Contributing Guide.