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Redshift

Certified

Important Capabilities​

CapabilityStatusNotes
Asset Containers✅Enabled by default. Supported for types - Database, Schema.
Classification✅Optionally enabled via classification.enabled.
Column-level Lineage✅Optionally enabled via configuration (mixed or sql_based lineage needs to be enabled).
Data Profiling✅Optionally enabled via configuration.
Dataset Usage✅Optionally enabled via include_usage_statistics.
Descriptions✅Enabled by default.
Detect Deleted Entities✅Enabled by default via stateful ingestion.
Domains✅Supported via the domain config field.
Platform Instance✅Enabled by default.
Schema Metadata✅Enabled by default.
Table-Level Lineage✅Optionally enabled via configuration.
Test Connection✅Enabled by default.

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
  • Table lineage
  • Usage statistics

Prerequisites​

The DataHub Redshift connector requires specific database privileges to extract metadata, lineage, and usage statistics from your Amazon Redshift cluster.

Permission Overview​

DataHub requires three categories of permissions:

  1. System Table Access - Access to Redshift system tables for lineage and usage statistics
  2. System View Access - Access to system views for metadata discovery
  3. Data Access - Access to user schemas and tables for profiling and classification

System Table and View Permissions​

Execute the following commands as a database superuser or user with sufficient privileges to grant these permissions:

-- Core system access (required for lineage and usage statistics)
ALTER USER datahub WITH SYSLOG ACCESS UNRESTRICTED;

-- Core metadata extraction (always required)
GRANT SELECT ON pg_catalog.svv_redshift_databases TO datahub;    -- Database information and properties
GRANT SELECT ON pg_catalog.svv_redshift_schemas TO datahub;      -- Schema information within databases
GRANT SELECT ON pg_catalog.svv_external_schemas TO datahub;      -- External schemas (Spectrum, federated)
GRANT SELECT ON pg_catalog.svv_table_info TO datahub;           -- Table metadata, statistics, and properties
GRANT SELECT ON pg_catalog.svv_external_tables TO datahub;       -- External table definitions (Spectrum)
GRANT SELECT ON pg_catalog.svv_external_columns TO datahub;      -- External table column information
GRANT SELECT ON pg_catalog.pg_class_info TO datahub;            -- Table creation timestamps and basic info

-- Essential pg_catalog tables for table discovery
GRANT SELECT ON pg_catalog.pg_class TO datahub;                 -- Table and view definitions
GRANT SELECT ON pg_catalog.pg_namespace TO datahub;             -- Schema namespace information
GRANT SELECT ON pg_catalog.pg_description TO datahub;           -- Table and column descriptions/comments
GRANT SELECT ON pg_catalog.pg_database TO datahub;              -- Database catalog information
GRANT SELECT ON pg_catalog.pg_attribute TO datahub;             -- Column definitions and properties
GRANT SELECT ON pg_catalog.pg_attrdef TO datahub;               -- Column default values
GRANT SELECT ON pg_catalog.svl_user_info TO datahub;            -- User information for ownership

-- Datashare lineage (enabled by default)
GRANT SELECT ON pg_catalog.svv_datashares TO datahub;           -- Cross-cluster datashare information

-- Choose ONE based on your Redshift type:
-- For Provisioned Clusters:
GRANT SELECT ON pg_catalog.stv_mv_info TO datahub;              -- Materialized view information (provisioned)

-- For Serverless Workgroups:
-- GRANT SELECT ON pg_catalog.svv_user_info TO datahub;          -- User information (serverless alternative)
-- GRANT SELECT ON pg_catalog.svv_mv_info TO datahub;           -- Materialized view information (serverless)

-- Schema access (required to read tables in each schema)
GRANT USAGE ON SCHEMA <schema_to_ingest> TO datahub;             -- Replace with actual schema names

Important: Make sure to grant USAGE on any schema you want to ingest from:

GRANT USAGE ON SCHEMA <schema_to_ingest> TO datahub;

Detailed Permission Breakdown​

The following sections provide detailed information about which permissions are required for specific features and configurations.

Core System Views (Always Required)​

These system views are accessed in all DataHub configurations:

-- Schema discovery
GRANT SELECT ON pg_catalog.svv_redshift_schemas TO datahub;      -- Schema information within databases
GRANT SELECT ON pg_catalog.svv_external_schemas TO datahub;      -- External schemas (Spectrum, federated)

-- Database information
GRANT SELECT ON pg_catalog.svv_redshift_databases TO datahub;    -- Database information and properties

-- Table metadata and statistics
GRANT SELECT ON pg_catalog.svv_table_info TO datahub;           -- Table metadata, statistics, and properties

-- External table support
GRANT SELECT ON pg_catalog.svv_external_tables TO datahub;       -- External table definitions (Spectrum)
GRANT SELECT ON pg_catalog.svv_external_columns TO datahub;      -- External table column information

-- Table creation timestamps
GRANT SELECT ON pg_catalog.pg_class_info TO datahub;            -- Table creation timestamps and basic info

Conditional System Tables (Feature Dependent)​

Shared Database (Datashare Consumer)​

-- Required when is_shared_database = True
GRANT SELECT ON pg_catalog.svv_redshift_tables TO datahub;       -- Table information in shared databases
GRANT SELECT ON pg_catalog.svv_redshift_columns TO datahub;      -- Column information in shared databases

Redshift Serverless Workgroups​

-- Required for serverless workgroups
GRANT SELECT ON pg_catalog.svv_user_info TO datahub;            -- User information (serverless alternative)
GRANT SELECT ON pg_catalog.svv_mv_info TO datahub;             -- Materialized view information (serverless)

Redshift Provisioned Clusters​

-- Required for provisioned clusters
GRANT SELECT ON pg_catalog.svl_user_info TO datahub;            -- User information for ownership (superuser table)
GRANT SELECT ON pg_catalog.stv_mv_info TO datahub;              -- Materialized view information (provisioned)

Datashares Lineage​

-- Required when include_share_lineage: true (default)
GRANT SELECT ON pg_catalog.svv_datashares TO datahub;           -- Cross-cluster datashare information

For a typical provisioned cluster with default settings:

-- Core system access
ALTER USER datahub WITH SYSLOG ACCESS UNRESTRICTED;

-- Always required
GRANT SELECT ON pg_catalog.svv_redshift_databases TO datahub;    -- Database information and properties
GRANT SELECT ON pg_catalog.svv_redshift_schemas TO datahub;      -- Schema information within databases
GRANT SELECT ON pg_catalog.svv_external_schemas TO datahub;      -- External schemas (Spectrum, federated)
GRANT SELECT ON pg_catalog.svv_table_info TO datahub;           -- Table metadata, statistics, and properties
GRANT SELECT ON pg_catalog.svv_external_tables TO datahub;       -- External table definitions (Spectrum)
GRANT SELECT ON pg_catalog.svv_external_columns TO datahub;      -- External table column information
GRANT SELECT ON pg_catalog.pg_class_info TO datahub;            -- Table creation timestamps and basic info

-- Essential pg_catalog tables for table discovery
GRANT SELECT ON pg_catalog.pg_class TO datahub;                 -- Table and view definitions
GRANT SELECT ON pg_catalog.pg_namespace TO datahub;             -- Schema namespace information
GRANT SELECT ON pg_catalog.pg_description TO datahub;           -- Table and column descriptions/comments
GRANT SELECT ON pg_catalog.pg_database TO datahub;              -- Database catalog information
GRANT SELECT ON pg_catalog.pg_attribute TO datahub;             -- Column definitions and properties
GRANT SELECT ON pg_catalog.pg_attrdef TO datahub;               -- Column default values
GRANT SELECT ON pg_catalog.svl_user_info TO datahub;            -- User information for ownership (superuser table)

-- Datashares (since include_share_lineage defaults to true)
GRANT SELECT ON pg_catalog.svv_datashares TO datahub;           -- Cross-cluster datashare information

-- Provisioned cluster materialized views
GRANT SELECT ON pg_catalog.stv_mv_info TO datahub;              -- Materialized view information (provisioned)

-- Schema access (required to read tables in each schema)
GRANT USAGE ON SCHEMA <schema_to_ingest> TO datahub;             -- Replace with actual schema names

Data Access Privileges (Required for Data Profiling and Classification)​

Important: The system table permissions above only provide access to metadata. To enable data profiling, classification, or any feature that reads actual table data, you must grant additional privileges:

-- Grant USAGE privilege on schemas (required to access schema objects)
GRANT USAGE ON SCHEMA public TO datahub;
GRANT USAGE ON SCHEMA your_schema_name TO datahub;

-- Grant SELECT privilege on existing tables for data access
GRANT SELECT ON ALL TABLES IN SCHEMA public TO datahub;
GRANT SELECT ON ALL TABLES IN SCHEMA your_schema_name TO datahub;

-- Grant privileges on future objects (recommended for production)
-- IMPORTANT: These must be run by each user who will create tables/views
-- OR by a superuser with FOR ROLE clause

-- Option 1: If you (as admin) will create all future tables/views:
ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT SELECT ON TABLES TO datahub;
ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT SELECT ON VIEWS TO datahub;
ALTER DEFAULT PRIVILEGES IN SCHEMA your_schema_name GRANT SELECT ON TABLES TO datahub;
ALTER DEFAULT PRIVILEGES IN SCHEMA your_schema_name GRANT SELECT ON VIEWS TO datahub;

-- Option 2: If other users will create tables/views, run this for each user:
-- ALTER DEFAULT PRIVILEGES FOR ROLE other_user_name IN SCHEMA public GRANT SELECT ON TABLES TO datahub;
-- ALTER DEFAULT PRIVILEGES FOR ROLE other_user_name IN SCHEMA public GRANT SELECT ON VIEWS TO datahub;

-- Option 3: For all future users (requires superuser):
-- ALTER DEFAULT PRIVILEGES FOR ALL ROLES IN SCHEMA public GRANT SELECT ON TABLES TO datahub;
-- ALTER DEFAULT PRIVILEGES FOR ALL ROLES IN SCHEMA public GRANT SELECT ON VIEWS TO datahub;
Data Access vs Metadata Access

The permissions are split into two categories:

  1. System table permissions (above) - Required for metadata extraction, lineage, and usage statistics
  2. Data access permissions (this section) - Required for data profiling, classification, and any feature that reads actual table content

Default privileges only apply to objects created by the user who ran the ALTER DEFAULT PRIVILEGES command. If multiple users create tables in your schemas, you need to:

  1. Run the commands as each user, OR
  2. Use FOR ROLE other_user_name for each user who creates objects, OR
  3. Use FOR ALL ROLES (requires superuser privileges)

Common gotcha: If User A runs ALTER DEFAULT PRIVILEGES and User B creates a table, DataHub won't have access to User B's table unless you used Option 2 or 3 above.

Alternative approach: Instead of default privileges, consider using a scheduled job to periodically grant access to new tables:

-- Run this periodically to catch new tables
GRANT SELECT ON ALL TABLES IN SCHEMA your_schema_name TO datahub;

Optional: Datashare Privileges​

To enable cross-cluster lineage through datashares, grant the following privileges:

-- Grant SHARE privilege on datashares (replace with actual datashare names)
GRANT SHARE ON your_datashare_name TO datahub;

Ingestion of multiple redshift databases, namespaces​

  • If multiple databases are present in the Redshift namespace (or provisioned cluster), you would need to set up a separate ingestion per database.

  • Ingestion recipes of all databases in a particular redshift namespace should use same platform instance.

  • If you've multiple redshift namespaces that you want to ingest within DataHub, it is highly recommended that you specify a platform_instance equivalent to namespace in recipe. It can be same as namespace id or other human readable name however it should be unique across all your redshift namespaces.

Lineage​

There are multiple lineage collector implementations as Redshift does not support table lineage out of the box.

stl_scan_based​

The stl_scan based collector uses Redshift's stl_insert and stl_scan system tables to discover lineage between tables.

Pros:

  • Fast
  • Reliable

Cons:

  • Does not work with Spectrum/external tables because those scans do not show up in stl_scan table.
  • If a table is depending on a view then the view won't be listed as dependency. Instead the table will be connected with the view's dependencies.

sql_based​

The sql_based based collector uses Redshift's stl_insert to discover all the insert queries and uses sql parsing to discover the dependencies.

Pros:

  • Works with Spectrum tables
  • Views are connected properly if a table depends on it

Cons:

  • Slow.
  • Less reliable as the query parser can fail on certain queries

mixed​

Using both collector above and first applying the sql based and then the stl_scan based one.

Pros:

  • Works with Spectrum tables
  • Views are connected properly if a table depends on it
  • A bit more reliable than the sql_based one only

Cons:

  • Slow
  • May be incorrect at times as the query parser can fail on certain queries
note

The redshift stl redshift tables which are used for getting data lineage retain at most seven days of log history, and sometimes closer to 2-5 days. This means you cannot extract lineage from queries issued outside that window.

Datashares Lineage​

This is enabled by default, can be disabled via setting include_share_lineage: False

It is mandatory to run redshift ingestion of datashare producer namespace at least once so that lineage shows up correctly after datashare consumer namespace is ingested.

Profiling​

Profiling runs sql queries on the redshift cluster to get statistics about the tables. To be able to do that, the user needs to have read access to the tables that should be profiled.

If you don't want to grant read access to the tables you can enable table level profiling which will get table statistics without reading the data.

profiling:
  profile_table_level_only: true

Caveats​

note

System table access: The SYSLOG ACCESS UNRESTRICTED privilege gives the user visibility to data generated by other users. For example, STL_QUERY and STL_QUERYTEXT contain the full text of INSERT, UPDATE, and DELETE statements.

note

Datashare lineage: For cross-cluster lineage through datashares, the datahub user requires SHARE privileges on datashares in both producer and consumer namespaces. See the Amazon Redshift datashare documentation for more information.

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: redshift
  config:
    # Coordinates
    host_port: example.something.us-west-2.redshift.amazonaws.com:5439
    database: DemoDatabase

    # Credentials
    username: user
    password: pass

    # Options
    options:
      # driver_option: some-option

    include_table_lineage: true
    include_usage_statistics: true
    # The following options are only used when include_usage_statistics is true
    # it appends the domain after the redshift username which is extracted from the Redshift audit history
    # in the format username@email_domain
    email_domain: mydomain.com

    profiling:
      enabled: true
      # Only collect table level profiling information
      profile_table_level_only: true

  sink:
  # sink configs

#------------------------------------------------------------------------------
# Extra options when running Redshift behind a proxy</summary>
# This requires you to have already installed the Microsoft ODBC Driver for SQL Server.
# See https://docs.microsoft.com/en-us/sql/connect/python/pyodbc/step-1-configure-development-environment-for-pyodbc-python-development?view=sql-server-ver15
#------------------------------------------------------------------------------

source:
  type: redshift
  config:
    host_port: my-proxy-hostname:5439

    options:
      connect_args:
        # check all available options here: https://pypi.org/project/redshift-connector/
        ssl_insecure: "false" # Specifies if IDP hosts server certificate will be verified

sink:
  # sink configs

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"
convert_urns_to_lowercase
boolean
Whether to convert dataset urns to lowercase.
Default: False
database
string
database
Default: dev
default_schema
string
The default schema to use if the sql parser fails to parse the schema with sql_based lineage collector
Default: public
email_domain
One of string, null
Email domain of your organisation so users can be displayed on UI appropriately.
Default: None
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
extra_client_options
object
Default: {}
extract_column_level_lineage
boolean
Whether to extract column level lineage. This config works with rest-sink only.
Default: True
format_sql_queries
boolean
Whether to format sql queries
Default: False
include_copy_lineage
boolean
Whether lineage should be collected from copy commands
Default: True
include_operational_stats
boolean
Whether to display operational stats.
Default: True
include_read_operational_stats
boolean
Whether to report read operational stats. Experimental.
Default: False
include_share_lineage
boolean
Whether lineage should be collected from datashares
Default: True
include_table_lineage
boolean
Whether table lineage should be ingested.
Default: True
include_table_location_lineage
boolean
If the source supports it, include table lineage to the underlying storage location.
Default: True
include_table_rename_lineage
boolean
Whether we should follow alter table ... rename to statements when computing lineage.
Default: True
include_tables
boolean
Whether tables should be ingested.
Default: True
include_top_n_queries
boolean
Whether to ingest the top_n_queries.
Default: True
include_unload_lineage
boolean
Whether lineage should be collected from unload commands
Default: True
include_usage_statistics
boolean
Generate usage statistic. email_domain config parameter needs to be set if enabled
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
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
is_serverless
boolean
Whether target Redshift instance is serverless (alternative is provisioned cluster)
Default: False
lineage_generate_queries
boolean
Whether to generate queries entities for the SQL-based lineage collector.
Default: True
match_fully_qualified_names
boolean
Whether schema_pattern is matched against fully qualified schema name <database>.<schema>.
Default: False
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
patch_custom_properties
boolean
Whether to patch custom properties on existing datasets rather than replace.
Default: True
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
platform_instance_map
One of string, null
A holder for platform -> platform_instance mappings to generate correct dataset urns
Default: None
resolve_temp_table_in_lineage
boolean
Whether to resolve temp table appear in lineage to upstream permanent tables.
Default: True
skip_external_tables
boolean
Whether to skip EXTERNAL tables.
Default: False
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
table_lineage_mode
Enum
One of: "sql_based", "stl_scan_based", "mixed"
top_n_queries
integer
Number of top queries to save to each table.
Default: 10
use_file_backed_cache
boolean
Whether to use a file backed cache for the view definitions.
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
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
s3_lineage_config
S3LineageProviderConfig
Any source that produces s3 lineage from/to Datasets should inherit this class.
s3_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
s3_lineage_config.strip_urls
boolean
Strip filename from s3 url. It only applies if path_specs are not specified.
Default: True
s3_lineage_config.path_specs
array
List of PathSpec. See below the details about PathSpec
Default: []
s3_lineage_config.path_specs.PathSpec
PathSpec
s3_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.
s3_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
s3_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
s3_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
s3_lineage_config.path_specs.PathSpec.enable_compression
boolean
Enable or disable processing compressed files. Currently .gz and .bz files are supported.
Default: True
s3_lineage_config.path_specs.PathSpec.include_hidden_folders
boolean
Include hidden folders in the traversal (folders starting with . or _
Default: False
s3_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
s3_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
s3_lineage_config.path_specs.PathSpec.traversal_method
Enum
One of: "ALL", "MIN_MAX", "MAX"
s3_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: []
s3_lineage_config.path_specs.PathSpec.exclude.string
string
s3_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']
s3_lineage_config.path_specs.PathSpec.file_types.string
string
s3_lineage_config.path_specs.PathSpec.tables_filter_pattern
AllowDenyPattern
A class to store allow deny regexes
s3_lineage_config.path_specs.PathSpec.tables_filter_pattern.ignoreCase
One of boolean, null
Whether to ignore case sensitivity during pattern matching.
Default: True
schema_pattern
AllowDenyPattern
A class to store allow deny regexes
schema_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
user_email_pattern
AllowDenyPattern
A class to store allow deny regexes
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

Troubleshooting​

Schema Discovery Issues​

If you're not seeing all schemas or tables after following the setup steps, check the following:

Missing Schemas​

1. Check schema filtering configuration:

# In your recipe, ensure schema patterns are correct
schema_pattern:
  allow:
    - "your_schema_name"
    - "public"
  # Remove deny patterns that might be blocking schemas

2. Verify permissions on specific schemas:

-- Test if you can see schemas
SELECT schema_name, schema_type
FROM svv_redshift_schemas
WHERE database_name = 'your_database';

-- Test external schemas
SELECT schemaname, eskind, databasename
FROM SVV_EXTERNAL_SCHEMAS;

3. Check for external schemas: External schemas (Redshift Spectrum) require both permissions:

GRANT SELECT ON pg_catalog.svv_external_schemas TO datahub_user;
GRANT SELECT ON pg_catalog.svv_external_tables TO datahub_user;
GRANT SELECT ON pg_catalog.svv_external_columns TO datahub_user;

Missing Tables Within Schemas​

1. Check table filtering:

table_pattern:
  allow:
    - "your_schema.your_table"
  # Ensure no overly restrictive deny patterns

2. Test table visibility:

-- For regular tables
SELECT schemaname, tablename, tabletype
FROM pg_tables
WHERE schemaname = 'your_schema';

-- For views
SELECT schemaname, viewname
FROM pg_views
WHERE schemaname = 'your_schema';

-- For external tables
SELECT schemaname, tablename
FROM SVV_EXTERNAL_TABLES
WHERE schemaname = 'your_schema';

Configuration Issues​

1. Database specification: Ensure you're connecting to the correct database - Redshift ingestion works per database:

database: "your_actual_database_name" # Not the cluster name

2. Schema access permissions: Ensure you have USAGE permissions on the schemas you want to discover:

-- Check if you have USAGE on schemas
SELECT n.nspname as schema_name,
       has_schema_privilege('datahub_user', n.nspname, 'USAGE') as has_usage
FROM pg_catalog.pg_namespace n
WHERE n.nspname NOT LIKE 'pg_%'
  AND n.nspname != 'information_schema';

-- Grant USAGE if missing
GRANT USAGE ON SCHEMA your_schema_name TO datahub_user;

3. Shared database configuration: If using datashare consumers, add:

is_shared_database: true

Permission Test Queries​

Run these to verify your permissions are working:

-- Test core permissions
SELECT COUNT(*) FROM svv_redshift_schemas WHERE database_name = 'your_database';
SELECT COUNT(*) FROM svv_table_info WHERE database = 'your_database';

-- Test external permissions
SELECT COUNT(*) FROM svv_external_schemas;
SELECT COUNT(*) FROM svv_external_tables;

Data Profiling Issues​

Profile Data Not Appearing​

1. Check data access permissions: Ensure you have USAGE on schemas and SELECT on tables:

-- Test schema access
SELECT has_schema_privilege('datahub_user', 'your_schema', 'USAGE');

-- Test table access
SELECT has_table_privilege('datahub_user', 'your_schema.your_table', 'SELECT');

2. Enable table-level profiling only: If you cannot grant SELECT on tables, use table-level profiling:

profiling:
  profile_table_level_only: true

Lineage Issues​

Missing Lineage Information​

1. Check lineage configuration:

table_lineage_mode: stl_scan_based # or sql_based, mixed
include_usage_statistics: true

2. Verify SYSLOG ACCESS:

-- Check if user has SYSLOG ACCESS
SELECT usename, usesyslog
FROM pg_user
WHERE usename = 'datahub_user';
-- usesyslog should be 't' (true)

Cross-Cluster Lineage (Datashares)​

For lineage across datashares, ensure:

  1. DataHub user has SHARE privileges on datashares
  2. Both producer and consumer clusters are ingested
  3. include_share_lineage: true in configuration
-- Check datashare access
SELECT * FROM svv_datashares WHERE share_name = 'your_share';

Code Coordinates​

  • Class Name: datahub.ingestion.source.redshift.redshift.RedshiftSource
  • Browse on GitHub

Questions

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