Load HDInsight Interactive Query data to Microsoft Fabric

HDInsight Interactive Query provides a REST API to submit and manage Hive queries within Azure HDInsight clusters. Everything needed to build a working HDInsight Interactive Query → Microsoft Fabric 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 HDInsight Interactive Query to Microsoft Fabric pipeline

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

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 HDInsight Interactive Query 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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HDInsight Interactive Query API at a glance

These values come from the HDInsight Interactive Query API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the HDInsight Interactive Query API?

Requests require an Authorization header with a Bearer token acquired via Microsoft Entra ID (OAuth 2.0). Some operations may also require an X-Requested-By header.

1. Get your credentials

HDInsight clusters typically use one of two authentication methods: basic authentication or Entra ID OAuth 2.0. For basic authentication, use the cluster admin username and password defined during cluster creation. To obtain Entra ID bearer tokens, register an application in the Azure portal, grant it appropriate cluster access, and perform a client_credentials flow request to https://login.microsoftonline.com/{tenant_id}/oauth2/v2.0/token. Alternatively, you can programmatically retrieve gateway settings (containing credentials) using the Azure Management API: POST to https://management.azure.com/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.HDInsight/clusters/{clusterName}/getGatewaySettings?api-version={api-version}.

2. Add them to .dlt/secrets.toml

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[sources.hdinsight_interactive_query_source]
username = "admin"
password = "your_cluster_password"
cluster_name = "your_cluster_name"
# If using Entra ID OAuth 2.0
# client_id = "your_app_id"
# client_secret = "your_app_secret"
# tenant_id = "your_tenant_id"

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 HDInsight Interactive Query data can I load into Microsoft Fabric?

These are the HDInsight Interactive Query endpoints dlt can load into Microsoft Fabric:

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How do I load only new HDInsight Interactive Query records?

The HDInsight Interactive Query API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "cluster_status", "endpoint": {
    "path": "templeton/v1/status",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "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 HDInsight Interactive Query pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading templeton and api from the HDInsight Interactive Query API into Microsoft Fabric:

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

@dlt.source
def hdinsight_interactive_query_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://CLUSTERNAME.azurehdinsight.net/api/v1/clusters/CLUSTERNAME",
            "auth": {"type": "bearer", "token": token},
        },
        "resources": [
            {"name": "cluster_status", "endpoint": {"path": "templeton/v1/status"}},
            {"name": "hive_version", "endpoint": {"path": "templeton/v1/version/hive"}}
        ],
    }
    yield from rest_api_resources(config)


def load_hdinsight_interactive_query_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="hdinsight_interactive_query_pipeline",
        destination="fabric",
        dataset_name="hdinsight_interactive_query_data",
    )
    load_info = pipeline.run(hdinsight_interactive_query_source())
    print(load_info)


if __name__ == "__main__":
    load_hdinsight_interactive_query_to_fabric()

Run it with python hdinsight_interactive_query_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 HDInsight Interactive Query data in Microsoft Fabric?

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("hdinsight_interactive_query_pipeline").dataset()
df = data.cluster_status.df()
print(df.head())

SQL:

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SELECT * FROM hdinsight_interactive_query_data.cluster_status LIMIT 10;

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

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How do I deploy the HDInsight Interactive Query to Microsoft Fabric 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 HDInsight Interactive Query 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 HDInsight Interactive Query 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