{"categories":[{"label":"Front-Ends","url":"https://skillfed.io/packages/category/database-front-ends"}],"enrichment":{"capability":"PyApacheAtlas provides a Python SDK and Excel integration for programmatic and low-code interaction with Azure Purview and Apache Atlas APIs, supporting entity management, lineage definition, type definitions, and classifications.","skillfed_tags":["metadata-management","data-lineage","azure-purview"],"use_cases":["Bulk-load data asset metadata and lineage definitions into Azure Purview or Apache Atlas from Python scripts or scheduled jobs.","Parse and upload metadata from Excel templates to avoid manual API calls for repetitive metadata registration tasks.","Programmatically define custom entity types, classifications, and relationships to extend the metadata model beyond built-in types.","Automate data lineage tracking by creating Process entities and relationships between source and target assets.","Validate metadata before upload using the package's \"What-If\" analysis to catch missing or invalid attributes early.","Query and extract existing entities by GUID or qualified name for downstream processing or reporting."],"what_it_does":"PyApacheAtlas is a Python SDK that bridges programmatic and low-code workflows for managing metadata in Azure Purview and Apache Atlas. It wraps the REST APIs of both systems to let you create, update, and query entities (data assets), define custom types and classifications, establish lineage relationships, and manage glossary terms\u2014all from Python code or from Excel templates that the package can parse and upload.\n\nThe package is designed for two user profiles: developers who want to automate metadata operations through Python, and business users or data stewards who prefer working with Excel spreadsheets. It handles authentication to both Azure Purview (via service principals) and Apache Atlas (via basic auth), and includes validation helpers to check entity correctness before upload. The core dependencies are minimal (openpyxl for Excel parsing, requests for HTTP calls), making it lightweight to integrate into existing data pipelines.","worth_installing":"Yes, if you are actively working with Azure Purview or Apache Atlas and need to automate metadata operations at scale. The low install friction and permissive license make it a practical choice. However, be aware that maintenance is dormant\u2014no updates since late 2023\u2014so evaluate whether the current API compatibility matches your target system versions and whether you can support the package yourself if needed."},"id":"pyapacheatlas","links":{"html":"https://skillfed.io/packages/pyapacheatlas","md":"https://skillfed.io/packages/pyapacheatlas.md","pypi":"https://pypi.org/project/pyapacheatlas/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2023-12-23","license_spdx":null,"license_treatment":"permissive","name":"pyapacheatlas","python_support":"supports_current","summary":"A package to simplify working with the Apache Atlas REST APIs for Atlas and Azure Purview."},"popularity":{"monthly_downloads":573943,"position":5942,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.16.0"}