### Install pyatlan with Dependency Groups

Source: https://github.com/atlanhq/atlan-python/blob/main/README.md

Install specific dependency groups like 'dev' or 'docs' using uv sync. Multiple groups can be installed simultaneously.

```bash
# Install both dev and docs dependencies
uv sync --group dev --group docs

# Install all dependencies
uv sync --all-groups
```

--------------------------------

### Install pyatlan using uv

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/index.md

Install the pyatlan package using uv.

```bash
uv add pyatlan
```

--------------------------------

### Set Up Development Environment and Pre-commit Hooks

Source: https://github.com/atlanhq/atlan-python/blob/main/README.md

Clone the repository, install uv, synchronize development dependencies, and install pre-commit hooks for contributing.

```bash
# Fork and clone the repository
git clone https://github.com/your-username/atlan-python.git
cd atlan-python

# Install uv (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install development dependencies
uv sync --group dev

# Install pre-commit hooks
uv run pre-commit install
```

--------------------------------

### Clone Repository and Install Development Dependencies

Source: https://github.com/atlanhq/atlan-python/blob/main/README.md

Set up the development environment by cloning the repository, installing uv, and synchronizing development dependencies.

```bash
git clone https://github.com/atlanhq/atlan-python.git
cd atlan-python

# Install uv (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install with development dependencies
uv sync --group dev
```

--------------------------------

### Install pyatlan from source using uv (dev)

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/index.md

Clone the repository and install pyatlan with development dependencies using uv.

```bash
git clone https://github.com/atlanhq/atlan-python.git
cd atlan-python
uv sync --group dev
```

--------------------------------

### Run Integration Tests

Source: https://github.com/atlanhq/atlan-python/blob/main/README.md

Set up the environment by copying the example .env file and then run integration tests.

```bash
# Set up environment
cp .env.example .env
# Edit .env with your Atlan credentials

# Run integration tests
uv run pytest tests/integration
```

--------------------------------

### Install pyatlan using pip

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/index.md

Install the pyatlan package using pip.

```bash
pip install pyatlan
```

--------------------------------

### Install Specific pyatlan Version

Source: https://github.com/atlanhq/atlan-python/blob/main/README.md

Install a particular version of the pyatlan package by specifying the version number.

```bash
pip install pyatlan==7.1.3
```

--------------------------------

### Example Field Values for Level and Connection

Source: https://github.com/atlanhq/atlan-python/blob/main/HISTORY.md

Demonstrates the expected string formats for 'level' and 'connection' fields, which can now accept string types.

```json
"level": "user",
"connection": "default/bigquery/1234567890"
```

--------------------------------

### Run QA checks for Atlan Python SDK

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/index.md

After forking and cloning the repository, install development dependencies using uv and then run all quality assurance checks, including linting, type-checking, and formatting.

```bash
# Fork & clone
git clone https://github.com/atlanhq/atlan-python.git
cd atlan-python

# Install dev dependencies
uv sync --group dev

# Run tests
uv run pytest tests/unit

# Run all QA checks (lint, type-check, format)
uv run ./qa-checks
```

--------------------------------

### Run Interactive Python Session in Docker

Source: https://github.com/atlanhq/atlan-python/blob/main/README.md

Start an interactive Python session within the Atlan Docker container.

```bash
docker run -it --rm registry.atlan.com/public/pyatlan:main-latest
```

--------------------------------

### Retrieve Asset by GUID with Specific Attributes

Source: https://github.com/atlanhq/atlan-python/blob/main/HISTORY.md

Retrieve an asset by its GUID, specifying which attributes and related attributes to include. This method defaults to ignoring relationships for efficiency.

```python
glossary = client.asset.get_by_guid(
        guid="b4113341-251b-4adc-81fb-2420501c30e6",
        asset_type=AtlasGlossary,
        min_ext_info=False,
        ignore_relationships=True,
        attributes=[AtlasGlossary.USER_DESCRIPTION, AtlasGlossary.TERMS],
        related_attributes=[AtlasGlossaryTerm.USER_DESCRIPTION]
    )
```

--------------------------------

### Initialize Atlan Client and Search Assets

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/index.md

Initialize the AtlanClient with your tenant URL and API key, then perform a fluent search for active Table assets, limiting the results to 10 per page. This snippet demonstrates how to connect to Atlan and query for specific data assets.

```python
from pyatlan.client import AtlanClient
from pyatlan.model.fluent_search import FluentSearch
from pyatlan.model.assets import Table

# Initialize the client
client = AtlanClient(
    base_url="https://<your-tenant>.atlan.com",
    api_key="<your-api-key>",
)

# Search for assets
response = client.asset.search(
    FluentSearch()
    .where(FluentSearch.asset_type(Table))
    .where(FluentSearch.active_assets())
    .page_size(10)
    .to_request()
)

for asset in response:
    print(asset.name, asset.qualified_name)
```

--------------------------------

### Run Unit Tests with uv

Source: https://github.com/atlanhq/atlan-python/blob/main/README.md

Execute all unit tests for the pyatlan package using uv.

```bash
uv run pytest tests/unit
```

--------------------------------

### After: Using Client-Bound Thread-Local Storage Cache

Source: https://github.com/atlanhq/atlan-python/blob/main/HISTORY.md

Demonstrates the current approach where caches are bound to specific `AtlanClient` instances using thread-local storage. This ensures that caches are managed per client and avoids cross-thread interference. Note that re-initializing a client may require a new API call if the cache was previously cleared or not yet established for that instance.

```python
c1 = AtlanClient()

tag_id = c1.atlan_tag_cache.get_id_for_name(atlan_tag_name)  # <-- Uses default client (c1) and populates the caches (API call)

# OR
tag_id = AtlanClient.get_current_client().atlan_tag_cache.get_id_for_name(atlan_tag_name) # <-- Uses default client (c1) and populates the caches (API call)
tag_id = AtlanTagCache.get_id_for_name(atlan_tag_name)  # Returns the ID from the cache (no API call)

c2 = AtlanClient()
tag_id = c2.atlan_tag_cache.get_id_for_name(atlan_tag_name)  # <-- Uses default client (c2) and populates the cache (API call)
tag_id = c2.atlan_tag_cache.get_id_for_name(atlan_tag_name)  # Returns the ID from the cache (no API call)

c1 = AtlanClient()
tag_id = c1.atlan_tag_cache.get_id_for_name(atlan_tag_name)  # <-- c1 initialized again. Since no cache_key is used in the latest approach, the previously populated cache instance is gone, and we need to make an API call to populate the cache for c1.
tag_id = c1.atlan_tag_cache.get_id_for_name(atlan_tag_name)  # Returns the ID from the cache (no API call)
```

--------------------------------

### AtlanClient

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/client.md

The main AtlanClient class serves as the entry point for interacting with the Atlan API.

```APIDOC
## AtlanClient

### Description
The `AtlanClient` class is the primary interface for all client-side operations within the Atlan SDK. It aggregates various client modules for different Atlan functionalities.

### Usage
```python
from pyatlan.client import AtlanClient

# Initialize the client (replace with your actual credentials/config)
client = AtlanClient(
    host="YOUR_HOST",
    token="YOUR_TOKEN"
)

# Access different client modules through the main client instance
asset_client = client.asset
user_client = client.user
```
```

--------------------------------

### Make and Commit Changes for Contribution

Source: https://github.com/atlanhq/atlan-python/blob/main/README.md

Create a feature branch, make changes, run tests and quality checks, and commit using conventional commits before pushing.

```bash
# Create a feature branch
git checkout -b feature/amazing-feature

# Make your changes and test
uv run ./formatter
uv run ./qa-checks
uv run pytest tests/unit

# Commit with conventional commits
git commit -m "feat: add amazing feature"

# Push and create a pull request
git push origin feature/amazing-feature
```

--------------------------------

### Query Client

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/client.md

Enables running queries against Atlan data sources.

```APIDOC
## Query Client

### Description
The Query Client allows users to execute queries against data sources managed by Atlan.

### Usage
```python
from pyatlan.client.query import QueryClient

# Assuming 'client' is an initialized AtlanClient instance
query_client: QueryClient = client.query

# Example: Execute a SQL query
# results = query_client.execute_sql("SELECT * FROM your_table")
```
```

--------------------------------

### Configure AtlanClient with Proxy Settings

Source: https://github.com/atlanhq/atlan-python/blob/main/HISTORY.md

Use this snippet to configure the AtlanClient with explicit proxy settings for enhanced security and network handling. This is useful when the SDK needs to connect through a proxy or verify SSL certificates.

```python
from pyatlan.client.atlan import AtlanClient

proxy_settings = {
    "verify": "mitmproxy-ca-cert.pem",  # Path to certificate file
    "proxy": "http://127.0.0.1:8081",    # Proxy URL to use
}

client = AtlanClient(**proxy_settings)
```

--------------------------------

### Open Lineage Client

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/client.md

Manages OpenLineage events and metadata.

```APIDOC
## Open Lineage Client

### Description
The Open Lineage Client is used for sending and managing lineage information according to the OpenLineage standard.

### Usage
```python
from pyatlan.client.open_lineage import OpenLineageClient

# Assuming 'client' is an initialized AtlanClient instance
open_lineage_client: OpenLineageClient = client.open_lineage

# Example: Send lineage event
# open_lineage_client.send_event(lineage_event_data)
```
```

--------------------------------

### Run Quality Checks with uv

Source: https://github.com/atlanhq/atlan-python/blob/main/README.md

Execute all quality assurance checks, including formatting, linting, and type checking, using the uv run command.

```bash
uv run ./qa-checks
```

--------------------------------

### Generate Asset Models with SDK Generator

Source: https://github.com/atlanhq/atlan-python/blob/main/README.md

Use the SDK generator to retrieve typedefs from your Atlan instance and generate asset models. Supports caching and custom typedefs.

```bash
uv run ./generator
```

```bash
uv run ./generator --override
```

```bash
uv run ./generator ./my-typedefs.json
```

```bash
uv run ./generator --override ./my-typedefs.json
```

--------------------------------

### Run Unit Tests with Coverage

Source: https://github.com/atlanhq/atlan-python/blob/main/README.md

Execute unit tests and generate an HTML coverage report using pytest.

```bash
uv run pytest tests/unit --cov=pyatlan --cov-report=html
```

--------------------------------

### Regenerate pyatlan_v9 Models with Claude Code

Source: https://github.com/atlanhq/atlan-python/blob/main/README.md

Recommended method for regenerating v9 models using Claude Code skill. Supports generating from specific branches and running tests.

```bash
/generate-v9-models
```

```bash
/generate-v9-models <branch>
```

```bash
/generate-v9-models test
```

```bash
/generate-v9-models <branch> test
```

--------------------------------

### Run Python Script in Docker with Environment Variables

Source: https://github.com/atlanhq/atlan-python/blob/main/README.md

Execute a Python script inside the Atlan Docker container, mounting the current directory and setting necessary environment variables for Atlan connection.

```bash
docker run -it --rm \
  -v $(pwd):/app \
  -e ATLAN_API_KEY=your_key \
  -e ATLAN_BASE_URL=https://your-tenant.atlan.com \
  registry.atlan.com/public/pyatlan:main-latest \
  python your_script.py
```

--------------------------------

### Before: Using Shared Default Client Cache

Source: https://github.com/atlanhq/atlan-python/blob/main/HISTORY.md

Illustrates the previous approach where `AtlanTagCache` used a shared default client, leading to potential cache inconsistencies in concurrent environments. Each call to `get_id_for_name` might interact with the default client and its associated cache.

```python
from pyatlan.cache.atlan_tag_cache import AtlanTagCache

c1 = AtlanClient()
tag_id = AtlanTagCache.get_id_for_name(atlan_tag_name)  # <-- Uses default client (c1), populates the caches (API call), and uses cache_key to store the cache instance
tag_id = AtlanTagCache.get_id_for_name(atlan_tag_name)  # Returns the ID from the cache (no API call)

c2 = AtlanClient()
tag_id = AtlanTagCache.get_id_for_name(atlan_tag_name)  # <-- Uses default client (c2), populates the caches, and uses cache_key to store the cache instance
tag_id = AtlanTagCache.get_id_for_name(atlan_tag_name)  # Returns the ID from the cache (no API call)

c1 = AtlanClient()
tag_id = AtlanTagCache.get_id_for_name(atlan_tag_name)  # <-- c1 initialized again. Since cache_key was used for c1 previously, the populated cache instance in memory is reused, avoiding an API call.
tag_id = AtlanTagCache.get_id_for_name(atlan_tag_name)  # Returns the ID from the cache (no API call)
```

--------------------------------

### Access Custom Metadata Property

Source: https://github.com/atlanhq/atlan-python/blob/main/HISTORY.md

Demonstrates the updated syntax for accessing custom metadata properties, using bracket notation with the exact property name.

```python
cm['First Name']
```

--------------------------------

### Configure Upload Blocklist for Sensitive Files

Source: https://github.com/atlanhq/atlan-python/blob/main/HISTORY.md

Users can extend the blocklist for sensitive files in `FileClient.upload_file()` using the `PYATLAN_UPLOAD_FILE_BLOCKED_PATHS` environment variable. This variable accepts a comma-separated list of substrings to match against the resolved file path.

```bash
PYATLAN_UPLOAD_FILE_BLOCKED_PATHS="/custom/secrets/,.vault,.credentials"
```

--------------------------------

### User Client

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/client.md

Provides functionalities for managing users within Atlan.

```APIDOC
## User Client

### Description
The User Client allows for the management of users, including creating, updating, deleting, and retrieving user information.

### Usage
```python
from pyatlan.client.user import UserClient

# Assuming 'client' is an initialized AtlanClient instance
user_client: UserClient = client.user

# Example: Get a user by their email
# user = user_client.get_by_email("user@example.com")
```
```

--------------------------------

### Asyncio Test Configuration

Source: https://github.com/atlanhq/atlan-python/blob/main/HISTORY.md

Configure asyncio test loop scope in `pyproject.toml` for module-level fixtures, similar to sync integration tests.

```toml
asyncio_mode = "auto"
asyncio_default_test_loop_scope = "module"
asyncio_default_fixture_loop_scope = "module"
```

--------------------------------

### API

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/api.md

Represents an API asset within Atlan.

```APIDOC
## API

::: pyatlan.model.assets.a_p_i.API
```

--------------------------------

### Task Client

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/client.md

Manages tasks and their execution status.

```APIDOC
## Task Client

### Description
The Task Client provides functionalities for managing and monitoring tasks within the Atlan platform.

### Usage
```python
from pyatlan.client.task import TaskClient

# Assuming 'client' is an initialized AtlanClient instance
task_client: TaskClient = client.task

# Example: Get task details
# task = task_client.get("your_task_id")
```
```

--------------------------------

### Async Find Purposes by Name

Source: https://github.com/atlanhq/atlan-python/blob/main/HISTORY.md

Use this async method to find purposes by name, leveraging shared business logic. It requires the `FindPurposesByName` class for request preparation and response processing.

```python
from pyatlan.client.common import FindPurposesByName

@validate_arguments
async def find_purposes_by_name(
    self,
    name: str,
    attributes: Optional[List[str]] = None,
) -> List[Purpose]:
    """
    Async find purposes by name using shared business logic.
    :param name: of the purpose
    :param attributes: (optional) collection of attributes to retrieve for the purpose
    :returns: all purposes with that name, if found
    :raises NotFoundError: if no purpose with the provided name exists
    """
    search_request = FindPurposesByName.prepare_request(name, attributes)
    search_results = await self.search(search_request)
    return FindPurposesByName.process_response(
        search_results,
        name,
        allow_multiple=True,
    )
```

--------------------------------

### SSO Client

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/client.md

Handles Single Sign-On (SSO) configurations and operations.

```APIDOC
## SSO Client

### Description
The SSO Client module facilitates the management of Single Sign-On configurations for user authentication.

### Usage
```python
from pyatlan.client.sso import SSOClient

# Assuming 'client' is an initialized AtlanClient instance
sso_client: SSOClient = client.sso

# Example: Configure SSO settings
# sso_client.configure_settings(settings_data)
```
```

--------------------------------

### AIModel Asset

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/ai.md

Represents an AI model within Atlan.

```APIDOC
## AIModel Asset

### Description
Represents an AI model within Atlan.

### Class
`pyatlan.model.assets.core.a_i_model.AIModel`
```

--------------------------------

### Asset Client

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/client.md

Provides functionalities for managing assets within Atlan.

```APIDOC
## Asset Client

### Description
The Asset Client module allows users to interact with and manage assets in Atlan. This includes operations related to creating, retrieving, updating, and deleting assets.

### Usage
```python
from pyatlan.client.asset import AssetClient

# Assuming 'client' is an initialized AtlanClient instance
asset_client: AssetClient = client.asset

# Example: Get an asset by its qualified name
# asset = asset_client.get_by_qualified_name("your_asset_qualified_name")
```
```

--------------------------------

### Run Snyk Security Scan Locally

Source: https://github.com/atlanhq/atlan-python/blob/main/README.md

Perform a local Snyk security scan on project dependencies. This involves exporting requirements and then running the scan.

```bash
uv export --all-extras --no-hashes > requirements.txt
snyk test --file=requirements.txt --severity-threshold=high --skip-unresolved
rm -f requirements.txt
```

--------------------------------

### Run Individual Quality Assurance Checks

Source: https://github.com/atlanhq/atlan-python/blob/main/README.md

Execute specific QA checks such as code formatting, linting, and type checking individually using uv run.

```bash
# Code formatting
uv run ruff format .

# Linting
uv run ruff check .

# Type checking
uv run mypy .
```

--------------------------------

### Role Client

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/client.md

Handles the management of user roles and permissions.

```APIDOC
## Role Client

### Description
The Role Client provides functionalities for managing roles within Atlan, including assigning permissions and retrieving role details.

### Usage
```python
from pyatlan.client.role import RoleClient

# Assuming 'client' is an initialized AtlanClient instance
role_client: RoleClient = client.role

# Example: List all available roles
# roles = role_client.list()
```
```

--------------------------------

### Admin Client

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/client.md

Provides administrative functionalities for Atlan.

```APIDOC
## Admin Client

### Description
The Admin Client offers a suite of tools for managing the Atlan environment, including system settings and configurations.

### Usage
```python
from pyatlan.client.admin import AdminClient

# Assuming 'client' is an initialized AtlanClient instance
admin_client: AdminClient = client.admin

# Example: Get system status
# status = admin_client.get_status()
```
```

--------------------------------

### Pull Latest Atlan Docker Image

Source: https://github.com/atlanhq/atlan-python/blob/main/README.md

Pull the latest Docker image for the Atlan Python SDK from the Harbor registry.

```bash
docker pull registry.atlan.com/public/pyatlan:main-latest
```

--------------------------------

### Token Client

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/client.md

Manages API tokens for authentication and authorization.

```APIDOC
## Token Client

### Description
The Token Client is used for managing API tokens, which are essential for authenticating requests to the Atlan API.

### Usage
```python
from pyatlan.client.token import TokenClient

# Assuming 'client' is an initialized AtlanClient instance
token_client: TokenClient = client.token

# Example: Generate a new token
# new_token = token_client.generate("token_name")
```
```

--------------------------------

### CogniteFile

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/cognite.md

Represents a file within Cognite.

```APIDOC
## CogniteFile

::: pyatlan.model.assets.cognite_file.CogniteFile
```

--------------------------------

### Typedef Client

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/client.md

Manages type definitions (typedefs) in Atlan.

```APIDOC
## Typedef Client

### Description
The Typedef Client is used for managing type definitions, which define the structure and metadata of assets within Atlan.

### Usage
```python
from pyatlan.client.typedef import TypedefClient

# Assuming 'client' is an initialized AtlanClient instance
typedef_client: TypedefClient = client.typedef

# Example: Get a typedef by its name
# typedef = typedef_client.get_by_name("your_typedef_name")
```
```

--------------------------------

### Namespace Class

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/core.md

Represents a Namespace in Atlan. Used for logical grouping and access control of assets.

```APIDOC
## Namespace

::: pyatlan.model.assets.core.namespace.Namespace
```

--------------------------------

### APIPath

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/api.md

Represents an API path asset within Atlan.

```APIDOC
## APIPath

::: pyatlan.model.assets.a_p_i_path.APIPath
```

--------------------------------

### Add HARD delete option to AssetClient.purge_by_guid()

Source: https://github.com/atlanhq/atlan-python/blob/main/HISTORY.md

The `AssetClient.purge_by_guid()` method now accepts an optional `delete_type: HARD` parameter for permanent deletion.

```python
AssetClient.purge_by_guid(guid: str, delete_type: Literal['SOFT', 'HARD'] = 'SOFT')
```

--------------------------------

### AIApplication Asset

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/ai.md

Represents an AI application within Atlan.

```APIDOC
## AIApplication Asset

### Description
Represents an AI application within Atlan.

### Class
`pyatlan.model.assets.core.a_i_application.AIApplication`
```

--------------------------------

### SageMakerUnifiedStudio

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/sagemaker.md

Represents a SageMaker Unified Studio asset.

```APIDOC
## SageMakerUnifiedStudio

::: pyatlan.model.assets.sage_maker_unified_studio.SageMakerUnifiedStudio
```

--------------------------------

### Databricks

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/databricks.md

Represents a Databricks asset in Atlan.

```APIDOC
## Databricks

::: pyatlan.model.assets.core.databricks.Databricks
```

--------------------------------

### Automatic 401 Token Refresh with ContextVar

Source: https://github.com/atlanhq/atlan-python/blob/main/HISTORY.md

Fixes automatic 401 token refresh by using `ContextVar` for `AtlanClient._401_has_retried` to prevent race conditions in multithreading environments.

```python
AtlanClient._401_has_retried = ContextVar('401_has_retried', default=False)
```

--------------------------------

### Folder Class

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/core.md

Represents a Folder in Atlan. Used for organizing assets within the data catalog.

```APIDOC
## Folder

::: pyatlan.model.assets.core.folder.Folder
```

--------------------------------

### APIQuery

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/api.md

Represents an API query asset within Atlan.

```APIDOC
## APIQuery

::: pyatlan.model.assets.a_p_i_query.APIQuery
```

--------------------------------

### Impersonation Client

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/client.md

Enables impersonation for administrative tasks.

```APIDOC
## Impersonation Client

### Description
The Impersonation Client allows administrators to perform actions as another user for support or testing purposes.

### Usage
```python
from pyatlan.client.impersonate import ImpersonationClient

# Assuming 'client' is an initialized AtlanClient instance
impersonation_client: ImpersonationClient = client.impersonate

# Example: Start an impersonation session
# impersonation_client.start("user_to_impersonate_id")
```
```

--------------------------------

### Abstract Asset Cache

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/cache.md

Provides caching for abstract asset representations.

```APIDOC
## Abstract Asset Cache

### Description
Caches abstract representations of assets, facilitating efficient operations on various asset types.

### Module
`pyatlan.cache.abstract_asset_cache`
```

--------------------------------

### SageMakerUnifiedStudioAsset

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/sagemaker.md

Represents a SageMaker Unified Studio Asset.

```APIDOC
## SageMakerUnifiedStudioAsset

::: pyatlan.model.assets.sage_maker_unified_studio_asset.SageMakerUnifiedStudioAsset
```

--------------------------------

### LookerTile

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/looker.md

Represents a Looker Tile asset.

```APIDOC
::: pyatlan.model.assets.looker_tile.LookerTile
```

--------------------------------

### ObjectStore Asset Representation

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/cloud-storage.md

Represents a generic Object Store asset in Atlan.

```APIDOC
## ObjectStore

::: pyatlan.model.assets.object_store.ObjectStore
```

--------------------------------

### OAuth Client

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/client.md

Manages OAuth 2.0 authentication flows.

```APIDOC
## OAuth Client

### Description
The OAuth Client module handles OAuth 2.0 authentication, enabling secure access to resources.

### Usage
```python
from pyatlan.client.oauth import OAuthClient

# Assuming 'client' is an initialized AtlanClient instance
oauth_client: OAuthClient = client.oauth

# Example: Obtain an access token
# token_info = oauth_client.get_token(code="authorization_code")
```
```

--------------------------------

### SageMakerUnifiedStudioAssetSchema

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/sagemaker.md

Represents a SageMaker Unified Studio Asset Schema.

```APIDOC
## SageMakerUnifiedStudioAssetSchema

::: pyatlan.model.assets.sage_maker_unified_studio_asset_schema.SageMakerUnifiedStudioAssetSchema
```

--------------------------------

### Configure AtlanClient for Token Refresh

Source: https://github.com/atlanhq/atlan-python/blob/main/HISTORY.md

Set the `_user_id` field on the `AtlanClient` to enable automatic token refresh and retry of API requests upon receiving a 401 Unauthorized response. Ensure `CLIENT_ID` and `CLIENT_SECRET` environment variables are configured.

```python
client = AtlanClient()
client._user_id = "962c8f78-98a7-908f-9ec2-9e5b7ee7a09f"
```

--------------------------------

### SageMakerUnifiedStudioPublishedAsset

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/sagemaker.md

Represents a SageMaker Unified Studio Published Asset.

```APIDOC
## SageMakerUnifiedStudioPublishedAsset

::: pyatlan.model.assets.sage_maker_unified_studio_published_asset.SageMakerUnifiedStudioPublishedAsset
```

--------------------------------

### File Client

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/client.md

Handles file-related operations, such as uploads and downloads.

```APIDOC
## File Client

### Description
The File Client module is responsible for managing files within Atlan, including uploading, downloading, and retrieving file metadata.

### Usage
```python
from pyatlan.client.file import FileClient

# Assuming 'client' is an initialized AtlanClient instance
file_client: FileClient = client.file

# Example: Upload a file
# with open("path/to/your/file.txt", "rb") as f:
#     file_info = file_client.upload(file=f, file_name="file.txt")
```
```

--------------------------------

### BIProcess Class

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/core.md

Represents a Business Intelligence Process in Atlan. Used for tracking data lineage related to BI tools.

```APIDOC
## BIProcess

::: pyatlan.model.assets.core.b_i_process.BIProcess
```

--------------------------------

### OAuth Client (Client Credentials)

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/client.md

Manages OAuth client credentials for service-to-service authentication.

```APIDOC
## OAuth Client (Client Credentials)

### Description
This specific client handles OAuth client credentials flow for machine-to-machine authentication.

### Usage
```python
from pyatlan.client.oauth_client import OAuthClient as OAuthClientCredentials

# Assuming 'client' is an initialized AtlanClient instance
oauth_client_creds: OAuthClientCredentials = client.oauth_client

# Example: Request an access token using client credentials
# token_info = oauth_client_creds.get_token()
```
```

--------------------------------

### Credential Client

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/client.md

Manages credentials used for various integrations.

```APIDOC
## Credential Client

### Description
The Credential Client is responsible for managing credentials that Atlan uses to connect to external systems and services.

### Usage
```python
from pyatlan.client.credential import CredentialClient

# Assuming 'client' is an initialized AtlanClient instance
credential_client: CredentialClient = client.credential

# Example: List available credentials
# credentials = credential_client.list()
```
```

--------------------------------

### APIObject

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/api.md

Represents an API object asset within Atlan.

```APIDOC
## APIObject

::: pyatlan.model.assets.a_p_i_object.APIObject
```

--------------------------------

### Contract Client

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/client.md

Handles data contract management within Atlan.

```APIDOC
## Contract Client

### Description
The Contract Client provides functionalities for managing data contracts, ensuring data quality and consistency.

### Usage
```python
from pyatlan.client.contract import ContractClient

# Assuming 'client' is an initialized AtlanClient instance
contract_client: ContractClient = client.contract

# Example: Create a new data contract
# contract = contract_client.create(contract_data)
```
```

--------------------------------

### SageMakerUnifiedStudioProject

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/sagemaker.md

Represents a SageMaker Unified Studio Project asset.

```APIDOC
## SageMakerUnifiedStudioProject

::: pyatlan.model.assets.sage_maker_unified_studio_project.SageMakerUnifiedStudioProject
```

--------------------------------

### DataQuality

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/data-quality.md

Represents DataQuality assets.

```APIDOC
## DataQuality

::: pyatlan.model.assets.core.data_quality.DataQuality
```

--------------------------------

### Metric

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/data-quality.md

Represents Metric assets.

```APIDOC
## Metric

::: pyatlan.model.assets.core.metric.Metric
```

--------------------------------

### Airflow Asset

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/orchestration.md

Represents an Airflow asset.

```APIDOC
## Airflow Asset

### Description
Represents an Airflow asset.

### Model
::: pyatlan.model.assets.core.airflow.Airflow
```

--------------------------------

### DataQualityRule

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/data-quality.md

Represents DataQualityRule assets.

```APIDOC
## DataQualityRule

::: pyatlan.model.assets.core.data_quality_rule.DataQualityRule
```

--------------------------------

### Group Client

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/client.md

Manages user groups and their memberships.

```APIDOC
## Group Client

### Description
The Group Client allows for the management of user groups, including creating, updating, deleting, and retrieving group information and memberships.

### Usage
```python
from pyatlan.client.group import GroupClient

# Assuming 'client' is an initialized AtlanClient instance
group_client: GroupClient = client.group

# Example: Get a group by its name
# group = group_client.get_by_name("your_group_name")
```
```

--------------------------------

### DQ Template Config Cache

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/cache.md

Handles caching for Data Quality template configurations.

```APIDOC
## DQ Template Config Cache

### Description
Manages cached configurations for Data Quality templates, improving the performance of DQ-related operations.

### Module
`pyatlan.cache.dq_template_config_cache`
```

--------------------------------

### MCIncident

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/data-quality.md

Represents MCIncident assets.

```APIDOC
## MCIncident

::: pyatlan.model.assets.core.m_c_incident.MCIncident
```

--------------------------------

### S3Prefix Asset Representation

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/cloud-storage.md

Represents an S3 Prefix (folder) asset in Atlan.

```APIDOC
## S3Prefix

::: pyatlan.model.assets.s3_prefix.S3Prefix
```

--------------------------------

### ColumnProcess Class

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/core.md

Represents a Column Process in Atlan. This class is relevant for tracking data transformations at the column level.

```APIDOC
## ColumnProcess

::: pyatlan.model.assets.core.column_process.ColumnProcess
```

--------------------------------

### DataQualityRuleTemplate

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/data-quality.md

Represents DataQualityRuleTemplate assets.

```APIDOC
## DataQualityRuleTemplate

::: pyatlan.model.assets.core.data_quality_rule_template.DataQualityRuleTemplate
```

--------------------------------

### MatillionComponent Asset

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/orchestration.md

Represents a Matillion Component asset.

```APIDOC
## MatillionComponent Asset

### Description
Represents a Matillion Component asset.

### Model
::: pyatlan.model.assets.core.matillion_component.MatillionComponent
```

--------------------------------

### Salesforce Asset

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/salesforce.md

Represents a Salesforce asset in Atlan.

```APIDOC
## Salesforce

::: pyatlan.model.assets.salesforce.Salesforce
```

--------------------------------

### Referenceable Class

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/core.md

Represents a Referenceable entity in Atlan. This is a base class for objects that can be referenced.

```APIDOC
## Referenceable

::: pyatlan.model.assets.core.referenceable.Referenceable
```

--------------------------------

### Retrieve Asset by Qualified Name with Specific Attributes

Source: https://github.com/atlanhq/atlan-python/blob/main/HISTORY.md

Retrieve an asset by its qualified name, specifying which attributes and related attributes to include. This method defaults to ignoring relationships for efficiency.

```python
glossary = client.asset.get_by_qualified_name(
        asset_type=AtlasGlossary,
        qualified_name="pXkf3RUvsIOIG8xnn0W3O",
        min_ext_info=False,
        ignore_relationships=True,
        attributes=[AtlasGlossary.USER_DESCRIPTION, AtlasGlossary.TERMS],
        related_attributes=[AtlasGlossaryTerm.USER_DESCRIPTION]
    )
```

--------------------------------

### Process Class

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/core.md

Represents a generic Process in Atlan. This is a base class for various types of data processing entities.

```APIDOC
## Process

::: pyatlan.model.assets.core.process.Process
```

--------------------------------

### Connection Cache

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/cache.md

Provides caching for connection details.

```APIDOC
## Connection Cache

### Description
Caches connection details to optimize the process of establishing and managing connections.

### Module
`pyatlan.cache.connection_cache`
```

--------------------------------

### MatillionProject Asset

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/orchestration.md

Represents a Matillion Project asset.

```APIDOC
## MatillionProject Asset

### Description
Represents a Matillion Project asset.

### Model
::: pyatlan.model.assets.core.matillion_project.MatillionProject
```

--------------------------------

### Workflow Client

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/client.md

Provides functionalities for managing and interacting with workflows.

```APIDOC
## Workflow Client

### Description
The Workflow Client allows users to manage and execute workflows within the Atlan platform.

### Usage
```python
from pyatlan.client.workflow import WorkflowClient

# Assuming 'client' is an initialized AtlanClient instance
workflow_client: WorkflowClient = client.workflow

# Example: Trigger a workflow
# result = workflow_client.trigger("your_workflow_id")
```
```

--------------------------------

### LookerProject

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/looker.md

Represents a Looker Project asset.

```APIDOC
::: pyatlan.model.assets.looker_project.LookerProject
```

--------------------------------

### S3Bucket Asset Representation

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/cloud-storage.md

Represents an S3 Bucket asset in Atlan.

```APIDOC
## S3Bucket

::: pyatlan.model.assets.s3_bucket.S3Bucket
```

--------------------------------

### DatabricksNotebook

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/databricks.md

Represents a Databricks Notebook asset.

```APIDOC
## DatabricksNotebook

::: pyatlan.model.assets.databricks_notebook.DatabricksNotebook
```

--------------------------------

### DatabricksAIModelContext

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/databricks.md

Represents the AI Model Context for Databricks assets.

```APIDOC
## DatabricksAIModelContext

::: pyatlan.model.assets.core.databricks_a_i_model_context.DatabricksAIModelContext
```

--------------------------------

### Pull Specific Atlan Docker Image Version

Source: https://github.com/atlanhq/atlan-python/blob/main/README.md

Pull a Docker image for a specific version of pyatlan and Python tag.

```bash
docker pull registry.atlan.com/public/pyatlan:8.5.1-3.11
```

--------------------------------

### Search Log Client

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/client.md

Provides access to search logs for auditing and analysis.

```APIDOC
## Search Log Client

### Description
The Search Log Client allows retrieval and analysis of search queries performed within Atlan.

### Usage
```python
from pyatlan.client.search_log import SearchLogClient

# Assuming 'client' is an initialized AtlanClient instance
search_log_client: SearchLogClient = client.search_log

# Example: Get recent search logs
# logs = search_log_client.get_logs()
```
```

--------------------------------

### DatabricksUnityCatalogTag

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/databricks.md

Represents a Unity Catalog Tag for Databricks assets.

```APIDOC
## DatabricksUnityCatalogTag

::: pyatlan.model.assets.core.databricks_unity_catalog_tag.DatabricksUnityCatalogTag
```

--------------------------------

### TableauDashboard Asset Model

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/tableau.md

Represents a Tableau Dashboard asset.

```APIDOC
## TableauDashboard

::: pyatlan.model.assets.tableau_dashboard.TableauDashboard
```

--------------------------------

### SodaCheck

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/data-quality.md

Represents SodaCheck assets.

```APIDOC
## SodaCheck

::: pyatlan.model.assets.core.soda_check.SodaCheck
```

--------------------------------

### SAP Asset Types

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/sap.md

This section lists the available SAP asset types that can be represented and managed within the Atlan Python SDK. Each type corresponds to a specific SAP entity.

```APIDOC
## SAP

::: pyatlan.model.assets.s_a_p.SAP

## SapErpTable

::: pyatlan.model.assets.sap_erp_table.SapErpTable

## SapErpColumn

::: pyatlan.model.assets.sap_erp_column.SapErpColumn

## SapErpCdsView

::: pyatlan.model.assets.sap_erp_cds_view.SapErpCdsView

## SapErpAbapProgram

::: pyatlan.model.assets.sap_erp_abap_program.SapErpAbapProgram

## SapErpTransactionCode

::: pyatlan.model.assets.sap_erp_transaction_code.SapErpTransactionCode

## SapErpComponent

::: pyatlan.model.assets.sap_erp_component.SapErpComponent

## SapErpFunctionModule

::: pyatlan.model.assets.sap_erp_function_module.SapErpFunctionModule

## SapErpView

::: pyatlan.model.assets.sap_erp_view.SapErpView
```

--------------------------------

### Spark Asset

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/orchestration.md

Represents a Spark asset.

```APIDOC
## Spark Asset

### Description
Represents a Spark asset.

### Model
::: pyatlan.model.assets.core.spark.Spark
```

--------------------------------

### SchemaRegistry Class

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/core.md

Represents a Schema Registry in Atlan. Used for managing data schemas.

```APIDOC
## SchemaRegistry

::: pyatlan.model.assets.core.schema_registry.SchemaRegistry
```

--------------------------------

### DatabricksMetricView

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/databricks.md

Represents a Metric View for Databricks assets.

```APIDOC
## DatabricksMetricView

::: pyatlan.model.assets.core.databricks_metric_view.DatabricksMetricView
```

--------------------------------

### DatabricksAIModelVersion

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/databricks.md

Represents an AI Model Version for Databricks assets.

```APIDOC
## DatabricksAIModelVersion

::: pyatlan.model.assets.core.databricks_a_i_model_version.DatabricksAIModelVersion
```

--------------------------------

### Catalog Class

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/core.md

Represents a Catalog in Atlan. This class is used for organizing and managing data assets.

```APIDOC
## Catalog

::: pyatlan.model.assets.core.catalog.Catalog
```

--------------------------------

### LookerFolder

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/looker.md

Represents a Looker Folder asset.

```APIDOC
::: pyatlan.model.assets.looker_folder.LookerFolder
```

--------------------------------

### Kafka Asset

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/streaming.md

Represents a Kafka asset within the Atlan data catalog.

```APIDOC
## Kafka

::: pyatlan.model.assets.kafka.Kafka
```

--------------------------------

### Azure Asset Representation

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/cloud-storage.md

Represents an Azure asset in Atlan.

```APIDOC
## Azure

::: pyatlan.model.assets.azure.Azure
```

--------------------------------

### CogniteAsset

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/cognite.md

Represents an asset within Cognite.

```APIDOC
## CogniteAsset

::: pyatlan.model.assets.cognite_asset.CogniteAsset
```

--------------------------------

### DatabricksVolume

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/databricks.md

Represents a Volume asset in Databricks.

```APIDOC
## DatabricksVolume

::: pyatlan.model.assets.core.databricks_volume.DatabricksVolume
```

--------------------------------

### TableauSite Asset Model

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/tableau.md

Represents a Tableau Site asset.

```APIDOC
## TableauSite

::: pyatlan.model.assets.tableau_site.TableauSite
```

--------------------------------

### Matillion Asset

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/orchestration.md

Represents a Matillion asset.

```APIDOC
## Matillion Asset

### Description
Represents a Matillion asset.

### Model
::: pyatlan.model.assets.core.matillion.Matillion
```

--------------------------------

### Source Tag Cache

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/cache.md

Handles caching for source-specific tags.

```APIDOC
## Source Tag Cache

### Description
Manages cached tags associated with data sources, enabling faster access to source-specific metadata.

### Module
`pyatlan.cache.source_tag_cache`
```

--------------------------------

### AIModelVersion Asset

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/ai.md

Represents a specific version of an AI model within Atlan.

```APIDOC
## AIModelVersion Asset

### Description
Represents a specific version of an AI model within Atlan.

### Class
`pyatlan.model.assets.core.a_i_model_version.AIModelVersion`
```

--------------------------------

### Resource Class

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/core.md

Represents a Resource in Atlan. This class is used for various types of data resources.

```APIDOC
## Resource

::: pyatlan.model.assets.core.resource.Resource
```

--------------------------------

### Tag Class

Source: https://github.com/atlanhq/atlan-python/blob/main/docs/api/assets/core.md

Represents a Tag in Atlan. Used for categorizing and classifying assets.

```APIDOC
## Tag

::: pyatlan.model.assets.core.tag.Tag
```