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Version: 1.0.0

Iceberg

Testing

Important Capabilities​

CapabilityStatusNotes
Data Profiling✅Optionally enabled via configuration.
Descriptions✅Enabled by default.
Detect Deleted Entities✅Enabled via stateful ingestion
Domains❌Currently not supported.
Extract Ownership✅Automatically ingests ownership information from table properties based on user_ownership_property and group_ownership_property
Partition Support❌Currently not supported.
Platform Instance✅Optionally enabled via configuration, an Iceberg instance represents the catalog name where the table is stored.

Integration Details​

The DataHub Iceberg source plugin extracts metadata from Iceberg tables stored in a distributed or local file system. Typically, Iceberg tables are stored in a distributed file system like S3 or Azure Data Lake Storage (ADLS) and registered in a catalog. There are various catalog implementations like Filesystem-based, RDBMS-based or even REST-based catalogs. This Iceberg source plugin relies on the pyiceberg library.

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: "iceberg"
  config:
    env: PROD
    catalog:
      # REST catalog configuration example using S3 storage
      my_rest_catalog:
        type: rest
        # Catalog configuration follows pyiceberg's documentation (https://py.iceberg.apache.org/configuration)
        uri: http://localhost:8181
        s3.access-key-id: admin
        s3.secret-access-key: password
        s3.region: us-east-1
        warehouse: s3a://warehouse/wh/
        s3.endpoint: http://localhost:9000
      # SQL catalog configuration example using Azure datalake storage and a PostgreSQL database
      # my_sql_catalog:
      #   type: sql
      #   uri: postgresql+psycopg2://user:password@sqldatabase.postgres.database.azure.com:5432/icebergcatalog
      #   adlfs.tenant-id: <Azure tenant ID>
      #   adlfs.account-name: <Azure storage account name>
      #   adlfs.client-id: <Azure Client/Application ID>
      #   adlfs.client-secret: <Azure Client Secret>
    platform_instance: my_rest_catalog
    table_pattern:
      allow:
        - marketing.*
    profiling:
      enabled: true

sink:
  # sink configs

Config Details​

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

FieldDescription
catalog ✅
map(str,object)
group_ownership_property
string
Iceberg table property to look for a CorpGroup owner. Can only hold a single group value. If property has no value, no owner information will be emitted.
platform_instance
string
The instance of the platform that all assets produced by this recipe belong to. This should be unique within the platform. See https://datahubproject.io/docs/platform-instances/ for more details.
processing_threads
integer
How many threads will be processing tables
Default: 1
user_ownership_property
string
Iceberg table property to look for a CorpUser owner. Can only hold a single user value. If property has no value, no owner information will be emitted.
Default: owner
env
string
The environment that all assets produced by this connector belong to
Default: PROD
namespace_pattern
AllowDenyPattern
Regex patterns for namespaces to filter in ingestion.
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
namespace_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
namespace_pattern.allow
array
List of regex patterns to include in ingestion
Default: ['.*']
namespace_pattern.allow.string
string
namespace_pattern.deny
array
List of regex patterns to exclude from ingestion.
Default: []
namespace_pattern.deny.string
string
table_pattern
AllowDenyPattern
Regex patterns for tables to filter in ingestion.
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
table_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
table_pattern.allow
array
List of regex patterns to include in ingestion
Default: ['.*']
table_pattern.allow.string
string
table_pattern.deny
array
List of regex patterns to exclude from ingestion.
Default: []
table_pattern.deny.string
string
profiling
IcebergProfilingConfig
Default: {'enabled': False, 'include_field_null_count': Tru...
profiling.enabled
boolean
Whether profiling should be done.
Default: False
profiling.include_field_max_value
boolean
Whether to profile for the max 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.operation_config
OperationConfig
Experimental feature. To specify operation configs.
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
integer
Number between 1 to 31 for date of month (both inclusive). If not specified, defaults to Nothing and this field does not take affect.
profiling.operation_config.profile_day_of_week
integer
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.
stateful_ingestion
StatefulStaleMetadataRemovalConfig
Iceberg Stateful Ingestion Config.
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.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

Concept Mapping​

This ingestion source maps the following Source System Concepts to DataHub Concepts:

Source ConceptDataHub ConceptNotes
icebergData Platform
TableDatasetAn Iceberg table is registered inside a catalog using a name, where the catalog is responsible for creating, dropping and renaming tables. Catalogs manage a collection of tables that are usually grouped into namespaces. The name of a table is mapped to a Dataset name. If a Platform Instance is configured, it will be used as a prefix: <platform_instance>.my.namespace.table.
Table propertyUser (a.k.a CorpUser)The value of a table property can be used as the name of a CorpUser owner. This table property name can be configured with the source option user_ownership_property.
Table propertyCorpGroupThe value of a table property can be used as the name of a CorpGroup owner. This table property name can be configured with the source option group_ownership_property.
Table parent folders (excluding warehouse catalog location)ContainerAvailable in a future release
Table schemaSchemaFieldMaps to the fields defined within the Iceberg table schema definition.

Troubleshooting​

Exceptions while increasing processing_threads​

Each processing thread will open several files/sockets to download manifest files from blob storage. If you experience exceptions appearing when increasing processing_threads configuration parameter, try to increase limit of open files (i.e. using ulimit in Linux).

DataHub Iceberg REST Catalog​

DataHub also implements the Iceberg REST Catalog. See here for more details.

Code Coordinates​

  • Class Name: datahub.ingestion.source.iceberg.iceberg.IcebergSource
  • Browse on GitHub

Questions

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