Load Apache Atlas data to DuckDB

Apache Atlas is a metadata governance and data catalog platform that provides REST APIs for managing types, entities, lineage, and discovery. Everything needed to build a working Apache Atlas → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Apache Atlas to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Apache Atlas to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Apache Atlas API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Apache Atlas API at a glance

These values come from the Apache Atlas API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Apache Atlas API?

Apache Atlas primarily supports HTTP Basic authentication using a username and password. Kerberos/SPNEGO authentication is also supported for secure environments, which typically requires a keytab and principal.

1. Get your credentials

Apache Atlas does not use a typical API key/dashboard setup for REST API authentication. Instead, it relies on standard enterprise authentication methods configured at the server level, primarily Kerberos, LDAP, or simple file-based authentication. To obtain credentials, you must: 1. Contact your cluster administrator to get a valid username and password (for basic or LDAP authentication) or a valid Kerberos principal and keytab file. 2. If using basic authentication, ensure your user account has the necessary permissions (often Atlas administrator privileges) assigned. 3. If the environment is Kerberos-secured, perform a 'kinit' to obtain a TGT (Ticket Granting Ticket) before making API calls.

2. Add them to .dlt/secrets.toml

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[sources.apache_atlas_source]
atlas_username = "your_username"
atlas_password = "your_password"
atlas_host = "your_atlas_host"
atlas_port = 21000

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Apache Atlas data can I load into DuckDB?

These are the Apache Atlas endpoints dlt can load into DuckDB:

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How do I load only new Apache Atlas records?

Apache Atlas exposes offset on v2/search/dsl, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "search_dsl", "endpoint": {
    "path": "v2/search/dsl",
    "data_selector": "entities",
    "incremental": {"cursor_path": "offset", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Apache Atlas pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v2/entity and /v2/types/typedefs from the Apache Atlas API into DuckDB:

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

@dlt.source
def apache_atlas_source(username=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{atlas_host}:{port}/api/atlas/v2",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": username},
        },
        "resources": [
            {"name": "search_dsl", "endpoint": {"path": "v2/search/dsl", "data_selector": "entities"}},
            {"name": "search_fulltext", "endpoint": {"path": "v2/search/fulltext", "data_selector": "entities"}}
        ],
    }
    yield from rest_api_resources(config)


def load_apache_atlas_to_duckdb() -> 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)


if __name__ == "__main__":
    load_apache_atlas_to_duckdb()

Run it with python apache_atlas_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Apache Atlas data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

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

SQL:

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SELECT * FROM apache_atlas_data.search_dsl LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Apache Atlas to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Apache Atlas loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Apache Atlas data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack