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PyApacheAtlas: A Python SDK for Azure Purview and Apache Atlas

PyApacheAtlas Logo

PyApacheAtlas lets you work with the Azure Purview and Apache Atlas APIs in a Pythonic way. Supporting bulk loading, custom lineage, custom type definition and more from an SDK and Excel templates / integration.

The package supports programmatic interaction and an Excel template for low-code uploads.

Using Excel to Accelerate Metadata Uploads

  • Bulk upload entities.
    • Upload entities / assets for built-in or custom types.
    • Supports adding glossary terms to entities.
    • Supports adding classifications to entities.
    • Supports creating relationships between entities (e.g. columns of a table).
  • Creating custom lineage between existing entities.
  • Defining Purview Column Mappings / Column Lineage.
  • Bulk upload custom type definitions.
  • Bulk upload of classification definitions (Purview Classification Rules not supported).

Using the Pythonic SDK for Purview and Atlas

The PyApacheAtlas package itself supports those operations and more for the advanced user:

  • Programmatically create Entities, Types (Entity, Relationship, etc.).
  • Perform partial updates of an entity (for non-complex attributes like strings or integers).
  • Extracting entities by guid or qualified name.
  • Creating custom lineage with Process and Entity types.
  • Working with the glossary.
    • Uploading terms.
    • Downloading individual or all terms.
  • Working with classifications.
    • Classify one entity with multiple classifications.
    • Classify multiple entities with a single classification.
    • Remove classification ("declassify") from an entity.
  • Working with relationships.
    • Able to create arbitrary relationships between entities.
    • e.g. associating a given column with a table.
  • Deleting types (by name) or entities (by guid).
  • Performing "What-If" analysis to check if...
    • Your entities are valid types.
    • Your entities are missing required attributes.
    • Your entities are using undefined attributes.
  • Azure Purview's Search: query, autocomplete, suggest, browse.
  • Authentication to Azure Purview using azure-identity and Service Principal
  • Authentication to Apache Atlas using basic authentication of username and password.

Quickstart

Install from PyPi

python -m pip install pyapacheatlas

Using Azure-Identity and the Azure CLI to Connect to Purview

For connecting to Azure Purview, it's even more convenient to install the azure-identity package and its support for Managed Identity, Environment Credential, and Azure CLI credential.

If you want to use your Azure CLI credential rather than a service principal, install azure-identity by running pip install azure-identity and then run the code below.

from azure.identity import AzureCliCredential

from pyapacheatlas.core import PurviewClient

cred = AzureCliCredential()

# Create a client to connect to your service.
client = PurviewClient(
    account_name = "Your-Purview-Account-Name",
    authentication = cred
)

Create a Purview Client Connection Using Service Principal

If you don't want to install any additional packages, you should use the built-in ServicePrincipalAuthentication class.

from pyapacheatlas.auth import ServicePrincipalAuthentication
from pyapacheatlas.core import PurviewClient

auth = ServicePrincipalAuthentication(
    tenant_id = "", 
    client_id = "", 
    client_secret = ""
)

# Create a client to connect to your service.
client = PurviewClient(
    account_name = "Your-Purview-Account-Name",
    authentication = auth
)

Create Entities "By Hand"

You can also create your own entities by hand with the helper AtlasEntity class.

from pyapacheatlas.core import AtlasEntity

# Get All Type Defs
all_type_defs = client.get_all_typedefs()

# Get Specific Entities
list_of_entities = client.get_entity(guid=["abc-123-def","ghi-456-jkl"])

# Create a new entity
ae = AtlasEntity(
    name = "my table", 
    typeName = "demo_table", 
    qualified_name = "somedb.schema.mytable",
    guid = -1000
)

# Upload that entity with the client
upload_results = client.upload_entities( [ae] )

Create Entities from Excel

Read from a standardized excel template that supports...

  • Bulk uploading entities into your data catalog.
  • Creating custom table and column level lineage.
  • Creating custom type definitions for datasets.
  • Creating custom lineage between existing assets / entities in your data catalog.
  • Creating custom classification (Purview Classification rules are not supported yet).

See end to end samples for each scenario in the excel samples.

Learn more about the Excel features and configuration in the wiki.

Additional Resources