Load HubSpot data to Dremio

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These values come from the HubSpot API reference — the authoritative source if anything here looks out of date.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

These are the HubSpot endpoints dlt can load into Dremio:

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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 Dremio:

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


if __name__ == "__main__":
    load_hubspot_to_dremio()

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 Dremio as the destination?

Dremio loads from object storage, so dlt first writes the data to an S3 bucket through its filesystem staging destination; that is the staging="filesystem" in the code above. Install the extras with uv add "dlt[dremio,s3]", then add the staging bucket and the Dremio connection to .dlt/secrets.toml:

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[destination.filesystem]
bucket_url = "s3://<staging bucket>"

[destination.filesystem.credentials]
aws_access_key_id = "<access key id>"
aws_secret_access_key = "<secret access key>"

[destination.dremio]
staging_data_source = "<object storage source>"

[destination.dremio.credentials]
host = "<host>"
port = 32010
username = "<user>"
password = "<password or personal access token>"
database = "<data source>"

staging_data_source is the Object Storage source in Dremio that points at the staging bucket, and database is the Dremio source dlt loads into. port is the Arrow Flight port; set drivername = "grpc+tls" for TLS. See the dlt Dremio destination docs.

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How do I query HubSpot data in Dremio?

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.

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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 Dremio
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 Dremio 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