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Iceberg

Testing

Important Capabilities

CapabilityStatusNotes
Data ProfilingOptionally enabled via configuration.
DescriptionsEnabled by default.
Detect Deleted EntitiesEnabled via stateful ingestion
DomainsCurrently not supported.
Extract OwnershipOptionally enabled via configuration by specifying which Iceberg table property holds user or group ownership.
Partition SupportCurrently not supported.
Platform InstanceOptionally 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

Install the Plugin

pip install 'acryl-datahub[iceberg]'

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:
name: my_iceberg_catalog
type: rest
# Catalog configuration follows pyiceberg's documentation (https://py.iceberg.apache.org/configuration)
config:
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
platform_instance: my_iceberg_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 
IcebergCatalogConfig
Catalog configuration where to find Iceberg tables. See pyiceberg's catalog configuration details.
catalog.config 
map(str,string)
catalog.type 
string
Type of catalog. See PyIceberg for list of possible values.
catalog.name
string
Name of catalog
Default: default
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
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
table_pattern
AllowDenyPattern
Regex patterns for tables to filter in ingestion.
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
table_pattern.allow
array(string)
table_pattern.deny
array(string)
table_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
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
Default as True if datahub-rest sink is used or if 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
TableDatasetEach Iceberg table maps to a Dataset named using the parent folders. If a table is stored under my/namespace/table, the dataset name will be my.namespace.table. 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

[Common Issue]

[Provide description of common issues with this integration and steps to resolve]

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.