Load Azure Tables data to Microsoft Fabric

Azure Table Storage is a service that stores structured NoSQL data in the cloud, providing a REST API for accessing tables and entities. Everything needed to build a working Azure Tables → 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 Azure Tables 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 Azure Tables 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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Azure Tables API at a glance

These values come from the Azure Tables API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Azure Tables API?

Azure Table Storage supports multiple authentication methods including Microsoft Entra ID (OAuth 2.0 bearer token), Shared Key, and Shared Access Signature (SAS). Requests require the 'Authorization' header along with a date-time header ('Date' or 'x-ms-date') and 'x-ms-version'.

1. Get your credentials

1. Log in to the Azure portal (portal.azure.com).
2. Navigate to your Storage Account resource.
3. In the left-hand menu, under the "Security + networking" section, click "Access keys".
4. You will see two access keys (Key 1 and Key 2) and their corresponding connection strings.
5. Use the "Show" button to reveal the keys, or use the "Copy" icon to copy the connection string or the account key directly.

2. Add them to .dlt/secrets.toml

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[sources.azure_tables_source]
azure_account_name = "your_account_name_here"
azure_account_key = "your_primary_or_secondary_key_here"

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 Azure Tables data can I load into Microsoft Fabric?

These are the Azure Tables endpoints dlt can load into Microsoft Fabric:

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How do I load only new Azure Tables records?

Azure Tables exposes x-ms-continuation-NextTableName, x-ms-continuation-NextPartitionKey, x-ms-continuation-NextRowKey on {table_name}, 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": "entities", "endpoint": {
    "path": "{table_name}",
    "data_selector": "value",
    "incremental": {"cursor_path": "x-ms-continuation-NextTableName, x-ms-continuation-NextPartitionKey, x-ms-continuation-NextRowKey", "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 Azure Tables pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading tables and entities from the Azure Tables API into Microsoft Fabric:

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

@dlt.source
def azure_tables_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{account-name}.table.core.windows.net",
            "auth": {"type": "bearer", "token": token},
        },
        "resources": [
            {"name": "entities", "endpoint": {"path": "{table_name}", "data_selector": "value"}},
            {"name": "tables", "endpoint": {"path": "Tables", "data_selector": "value"}}
        ],
    }
    yield from rest_api_resources(config)


def load_azure_tables_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="azure_tables_pipeline",
        destination="fabric",
        dataset_name="azure_tables_data",
    )
    load_info = pipeline.run(azure_tables_source())
    print(load_info)


if __name__ == "__main__":
    load_azure_tables_to_fabric()

Run it with python azure_tables_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 Azure Tables 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("azure_tables_pipeline").dataset()
df = data.entities.df()
print(df.head())

SQL:

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SELECT * FROM azure_tables_data.entities LIMIT 10;

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

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How do I deploy the Azure Tables 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 Azure Tables 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 Azure Tables 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