Load HubSpot data to Azure Blob Storage

HubSpot CRM provides APIs for reading and managing CRM object records such as contacts, companies, deals, and custom objects. Everything needed to build a working HubSpot → Azure Blob Storage 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 HubSpot to Azure Blob Storage 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 HubSpot 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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HubSpot API at a glance

These values come from the HubSpot API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the HubSpot API?

Create a HubSpot private app and use its access token in the HTTP Authorization header as Authorization: Bearer . Search requests also require Content-Type: application/json; the token must have scopes permitting the requested CRM object access.

1. Get your credentials

In the HubSpot account, open Development > Legacy apps (or the account’s Private Apps area), then create a private app. Configure the app name and grant the required CRM object read scopes, such as crm.objects.contacts.read, crm.objects.companies.read, crm.objects.deals.read, or the corresponding scope for each object you will extract. Create the app, open its details, select the Auth tab, click Show token, and copy the private app access token. Send it on every request as the HTTP header Authorization: Bearer YOUR_TOKEN. A super admin is required to access and manage private apps. Do not use the deprecated developer API key/hapikey for CRM object extraction; HubSpot documents private access tokens and OAuth as the supported API authentication methods. For incremental extraction, use the CRM v3 search endpoint POST https://api.hubapi.com/crm/v3/objects/{objectType}/search, not the ordinary GET collection endpoint: submit a filterGroups filter on hs_lastmodifieddate with operator GTE (or GT), a millisecond timestamp value, and a deterministic ascending sort on hs_lastmodifieddate; request the property explicitly if needed. Set limit up to 200. Continue by copying paging.next.after from each response into the next request body’s after field; after is the search pagination cursor and is not the GET endpoint’s query-string paging mechanism. Persist the high-water mark and overlap the boundary or use GTE to avoid missing records updated at the checkpoint.

2. Add them to .dlt/secrets.toml

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[sources.hubspot_source]
api_key = "your_hubspot_private_app_access_token"

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 HubSpot data can I load into Azure Blob Storage?

These are the HubSpot endpoints dlt can load into Azure Blob Storage:

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How do I load only new HubSpot records?

HubSpot exposes hs_lastmodifieddate on crm/v3/objects/{objectType}, 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": "crm_objects", "endpoint": {
    "path": "crm/v3/objects/{objectType}",
    "data_selector": "results",
    "incremental": {"cursor_path": "hs_lastmodifieddate", "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 HubSpot pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading POST /crm/v3/objects/{objectType}/search and GET /crm/v3/objects/{objectType}/{recordId} from the HubSpot API into Azure Blob Storage:

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

@dlt.source
def hubspot_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.hubapi.com",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "crm_objects", "endpoint": {"path": "crm/v3/objects/{objectType}", "data_selector": "results"}},
            {"name": "crm_object_search", "endpoint": {"path": "crm/v3/objects/{objectType}/search", "data_selector": "results", "method": "POST"}}
        ],
    }
    yield from rest_api_resources(config)


def load_hubspot_to_azure_blob_storage_filesystem() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="hubspot_pipeline",
        destination="filesystem",
        dataset_name="hubspot_data",
    )
    load_info = pipeline.run(hubspot_source())
    print(load_info)


if __name__ == "__main__":
    load_hubspot_to_azure_blob_storage_filesystem()

Run it with uv run python hubspot_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 configure Azure Blob Storage as the destination?

dlt writes to Azure Blob Storage through its filesystem destination; the az:// scheme in bucket_url selects it. Install the extra with uv add "dlt[az]", then add to .dlt/config.toml and .dlt/secrets.toml:

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# .dlt/config.toml
[destination.filesystem]
bucket_url = "az://<container>/<path>"

# .dlt/secrets.toml
[destination.filesystem.credentials]
azure_storage_account_name = "<storage account name>"
azure_storage_account_key = "<storage account key>"

dlt writes JSONL by default; pass loader_file_format="parquet" to pipeline.run() for Parquet. Delta and Iceberg table formats are supported too. See the dlt filesystem destination docs.

Changing bucket_url is all it takes to write the same data to Amazon S3, Google Cloud Storage or local files instead; see dlt's filesystem destination for all four.

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How do I query HubSpot data in Azure Blob Storage?

After a run, pipeline.dataset() holds the loaded tables, one per resource. A Relation is a query on that dataset, either a whole table or a SQL string, and reading it gives you the data as a Pandas DataFrame, a PyArrow table or Python tuples. Nothing is read until you ask for it. A filesystem destination holds files rather than tables, so dlt reads them through DuckDB views and the interface is the same as for a SQL destination; for frequent reads load Delta or Iceberg tables, which DuckDB reads only as far as the query needs (see reading data from filesystem).

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import dlt

pipeline = dlt.pipeline("hubspot_pipeline")

# Step 1: Get the dataset from the pipeline
dataset = pipeline.dataset()

# Step 2: Get a Relation, either a whole table...
crm_objects = dataset.table("crm_objects")
# ...or any SQL query against Azure Blob Storage
sample = dataset("SELECT * FROM crm_objects LIMIT 100")

# Step 3: Read the data
df = crm_objects.df()  # or .df(chunk_size=50) for a large table
arrow_table = sample.arrow()  # a PyArrow Table
rows = sample.fetchall()  # a list of Python tuples

# Which tables were loaded, and how big are they?
print(dataset.row_counts().df())
"""
          table_name  row_count
0        crm_objects         13
1  crm_object_search          3
"""

The columns are the fields the HubSpot API returns. Your agent can run the same queries through the MCP server's execute_sql_query tool, and uv run dlthub show opens the Pipeline Dashboard with the schema, load metrics and a query box. See access loaded data in Python and exploring it in marimo notebooks.

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How do I deploy the HubSpot to Azure Blob Storage 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 HubSpot 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 HubSpot 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