PyApacheAtlas: A Python SDK for Azure Purview and Apache Atlas
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.
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Release files for pyapacheatlas 0.16.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pyapacheatlas-0.16.0.tar.gz | 65.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pyapacheatlas-0.16.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 142.5 kB
Release files / pyapacheatlas-0.16.0.tar.gz
| Download URL | pyapacheatlas-0.16.0.tar.gz |
|---|---|
| Size | 65.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.8.18
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Release files / pyapacheatlas-0.16.0-py3-none-any.whl
| Download URL | pyapacheatlas-0.16.0-py3-none-any.whl |
|---|---|
| Size | 77.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
7294f4ca46b117b60da326fb188ac18cffb1939aa20094cba79fbd55d8aede4e
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BLAKE2b-256 checksum How to use checksums |
801f4c83a5939c9218aa84e024424bff96120e44a8111a99cf5a90f095fa3dee
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.8.18
|
