Dynatrace AI Observability Python API Docs | dltHub

Dynatrace Grail Query API executes DQL queries against data stored in Grail, supporting synchronous results and asynchronous polling. The REST API base URL is https://{your-environment-id}.apps.dynatrace.com/platform/storage/query/v1 and all requests require a Platform OAuth Bearer token.

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 add "dlt[hub]" and start loading Dynatrace AI Observability data in under 10 minutes.

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What data can I load from Dynatrace AI Observability?

Here are some of the endpoints you can load from Dynatrace AI Observability:

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How do I authenticate with the Dynatrace AI Observability API?

Send the platform OAuth access token in the HTTP header Authorization: Bearer <access_token>. Grail Query API requests also use Content-Type: application/json; the documented execute example uses the environment URL https://{your-environment-id}.apps.dynatrace.com/platform/storage/query/v1/query

. Obtain an OAuth token with the client-credentials flow from https://sso.dynatrace.com/sso/oauth2/token; the token request uses Content-Type: application/x-www-form-urlencoded, grant_type=client_credentials, client_id, client_secret, and an appropriate scope. Do not use the classic Api-Token authorization scheme for this Platform API.

1. Get your credentials

1. Sign in to Dynatrace Account Management with an account administrator account, or use an account that has permission to manage platform tokens. 2. Open Account Management and select Platform tokens. 3. Choose to create a Platform token; provide a name and expiration (or choose no expiration where allowed). 4. Select the minimum scopes required by the Grail query and the target data. The token's scopes do not grant more access than the assigned user or service user; the user must also have the necessary Grail IAM permissions. For querying Grail, grant the relevant permissions such as storage:buckets
   plus the table permission for the data being queried (for example storage:logs
   , storage:metrics
   , storage:events
   , storage:bizevents
   , or storage:spans
   ). 5. Generate the token and copy its value immediately; Dynatrace displays it only once. 6. Store it in a secrets manager or environment variable, never in dashboard code or source control. Send it in the HTTP Authorization header as Bearer . For an interactive setup, open Dynatrace, search for and select Dynatrace API, select the Grail/DQL Query definition, then use the lock/Authorize control to enter the token and try the endpoint. The Grail Query API is asynchronous: POST /query
   , then use the returned request token with /query
   .

2. Add them to .dlt/secrets.toml

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[sources.dynatrace_ai_observability_source]
api_key = "your_dynatrace_platform_token"

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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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 Dynatrace AI Observability 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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uv run python dynatrace_ai_observability_pipeline.py

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

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Pipeline dynatrace_ai_observability_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset dynatrace_ai_observability_data
The duckdb destination used duckdb:/dynatrace_ai_observability.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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uv run dlthub 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 /query

and /query from the Dynatrace AI Observability 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 dynatrace_ai_observability_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{your-environment-id}.apps.dynatrace.com/platform/storage/query/v1",
            "auth": {"type": "bearer", "token": api_token},
        },
        "resources": [
            {"name": "query_execute", "endpoint": {"path": "query:execute", "data_selector": "records", "method": "POST"}},
            {"name": "query_poll", "endpoint": {"path": "query:poll", "data_selector": "records"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="dynatrace_ai_observability_pipeline",
        destination="duckdb",
        dataset_name="dynatrace_ai_observability_data",
    )
    load_info = pipeline.run(dynatrace_ai_observability_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("dynatrace_ai_observability_pipeline").dataset()
sessions_df = data.query_execute.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM dynatrace_ai_observability_data.query_execute LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("dynatrace_ai_observability_pipeline").dataset()
data.query_execute.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 Dynatrace AI Observability 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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Next steps

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

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

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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