Load Pipedrive data to Amazon Redshift

Pipedrive is a CRM platform for managing deals, contacts, and sales pipelines. Everything needed to build a working Pipedrive → Amazon Redshift 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 Pipedrive to Amazon Redshift 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 Pipedrive 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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Pipedrive API at a glance

These values come from the Pipedrive API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Pipedrive API?

All requests require the personal API token to be provided in the x-api-token HTTP header.

1. Get your credentials

To obtain your Pipedrive API token, log in to your Pipedrive account in a web browser. Click on your profile icon or initials (typically in the top-right or bottom-left corner of the sidebar) and select 'Personal preferences'. Navigate to the 'API' tab. Here you will find your personal API token. If no token exists, or if you need a new one, click the button to generate it. Note that generating a new token will invalidate any existing one used by other integrations.

2. Add them to .dlt/secrets.toml

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[sources.pipedrive_source]
api_token = "your_api_token_here"
company_domain = "your_company_domain"

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 Pipedrive data can I load into Amazon Redshift?

These are the Pipedrive endpoints dlt can load into Amazon Redshift:

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How do I load only new Pipedrive records?

Pipedrive exposes update_time on deals, 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": "deals", "endpoint": {
    "path": "deals",
    "data_selector": "data",
    "incremental": {"cursor_path": "update_time", "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 Pipedrive pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading deals and leads from the Pipedrive API into Amazon Redshift:

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

@dlt.source
def pipedrive_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{company_domain}.pipedrive.com/api/v1",
            "auth": {"type": "api_key", "api_key": api_token, "name": "x-api-token", "location": "header"},
        },
        "resources": [
            {"name": "deals", "endpoint": {"path": "deals", "data_selector": "data"}},
            {"name": "persons", "endpoint": {"path": "persons", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_pipedrive_to_redshift() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pipedrive_pipeline",
        destination="redshift",
        dataset_name="pipedrive_data",
    )
    load_info = pipeline.run(pipedrive_source())
    print(load_info)


if __name__ == "__main__":
    load_pipedrive_to_redshift()

Run it with uv run python pipedrive_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 Amazon Redshift as the destination?

Install the extra with uv add "dlt[redshift]", then add the Amazon Redshift credentials to .dlt/secrets.toml:

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[destination.redshift.credentials]
host = "<cluster endpoint>"
port = 5439
database = "<database>"
username = "<user>"
password = "<password>"

Without staging, dlt loads with INSERT statements; for larger loads add staging="filesystem" to the pipeline and an S3 bucket_url under [destination.filesystem], and dlt loads with COPY. See the dlt Amazon Redshift destination docs.

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How do I query Pipedrive data in Amazon Redshift?

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("pipedrive_pipeline")

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

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

# Step 3: Read the data
df = deals.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       deals         13
1     persons          3
"""

The columns are the fields the Pipedrive 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 Pipedrive to Amazon Redshift 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 Pipedrive 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 Pipedrive 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