Apache Atlas Python API Docs | dltHub

Apache Atlas is a metadata governance and data catalog platform exposing REST APIs to manage types, entities, lineage and discovery. The REST API base URL is https://{atlas_host}:{port}/api/atlas/v2 and Authentication varies by deployment: Kerberos/SPNEGO (Negotiate) or HTTP Basic; Atlas does not provide a built-in Bearer token mechanism by default..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Apache Atlas data in under 10 minutes.

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What data can I load from Apache Atlas?

Here are some of the endpoints you can load from Apache Atlas:

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How do I authenticate with the Apache Atlas API?

Atlas typically relies on HTTP authentication configured by the cluster: either Kerberos/SPNEGO (use HTTP Negotiate) or HTTP Basic (username/password). Include Content-Type: application/json and Accept: application/json headers.

1. Get your credentials

1. Determine authentication mode used by your Atlas deployment (check with your cluster admin). 2A. If HTTP Basic: obtain a service account username/password from admin. 2B. If Kerberos/SPNEGO: obtain a Kerberos principal and keytab and perform kinit to get a ticket.
2. Test with curl: curl -u username:password -H 'Accept: application/json' 'https://atlas_host:port/api/atlas/v2/types/typedefs' or curl --negotiate -u : --keytab KEYTAB -b /tmp/krb5cc 'https://...'

2. Add them to .dlt/secrets.toml

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[sources.apache_atlas_source]
username = "atlas_user"
password = "atlas_password"
# or for Kerberos, store keytab path (optional):
keytab = "/path/to/keytab"
kerberos_principal = "user@REALM"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Apache Atlas API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python apache_atlas_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline apache_atlas_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset apache_atlas_data
The duckdb destination used duckdb:/apache_atlas.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline apache_atlas_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads entity and search_basic from the Apache Atlas API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def apache_atlas_source(username, password (or keytab/kerberos_principal for Kerberos)=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{atlas_host}:{port}/api/atlas/v2",
            "auth": {
                "type": "http_basic (or kerberos for SPNEGO)",
                "password": username, password (or keytab/kerberos_principal for Kerberos),
            },
        },
        "resources": [
            {"name": "entity", "endpoint": {"path": "entity"}},
            {"name": "search_basic", "endpoint": {"path": "search/basic", "data_selector": "entities"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="apache_atlas_pipeline",
        destination="duckdb",
        dataset_name="apache_atlas_data",
    )
    load_info = pipeline.run(apache_atlas_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("apache_atlas_pipeline").dataset()
sessions_df = data.entity.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM apache_atlas_data.entity LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("apache_atlas_pipeline").dataset()
data.entity.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Apache Atlas data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you receive HTTP 401/403, verify whether Atlas is configured for Kerberos (use --negotiate / kinit) or HTTP Basic. For Basic auth confirm username/password; for Kerberos confirm a valid ticket and principal.

Pagination and result limits

Search and list endpoints support query parameters (limit, offset). Ensure you pass appropriate 'limit' and 'offset' or use saved searches to page through large result sets.

Common API errors

• 400 Bad Request: malformed JSON or invalid query parameters.
• 401/403: auth or permission issues.
• 404 Not Found: invalid GUID or resource not present.
• 500/502/503: server‑side errors; check Atlas server logs.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack