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Python Data Loading from pipedrive to duckdb using dlt Library

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Welcome to our technical documentation on how to load data from Pipedrive to DuckDB using the open-source Python library, dlt. Pipedrive is a cloud-based sales CRM tool that helps businesses manage leads and deals. On the other hand, DuckDB is an in-process analytical database with a rich SQL dialect and deep integrations into client APIs. With dlt, you can efficiently transfer data from Pipedrive to DuckDB for further analysis. For more information about Pipedrive, visit

dlt Key Features

  • Automated Maintenance: With schema inference and evolution and alerts, and with short declarative code, maintenance becomes simple. More details can be found here.
  • Pipeline Metadata Utilization: dlt pipelines leverage metadata to provide governance capabilities. This metadata includes load IDs, which consist of a timestamp and pipeline name. Learn more about this feature here.
  • Schema Enforcement and Curation: dlt empowers users to enforce and curate schemas, ensuring data consistency and quality. Read more about this feature here.
  • Schema Change Alerts: dlt enables proactive governance by alerting users to schema changes. When modifications occur in the source data’s schema, dlt notifies stakeholders. More information can be found here.
  • DuckDB Destination: DuckDB is a supported destination for dlt pipelines. You can configure file formats to load data into DuckDB. Find out more about this feature here.

Getting started with your pipeline locally

0. Prerequisites

dlt requires Python 3.8 or higher. Additionally, you need to have the pip package manager installed, and we recommend using a virtual environment to manage your dependencies. You can learn more about preparing your computer for dlt in our installation reference.

1. Install dlt

First you need to install the dlt library with the correct extras for DuckDB:

pip install "dlt[duckdb]"

The dlt cli has a useful command to get you started with any combination of source and destination. For this example, we want to load data from Pipedrive to DuckDB. You can run the following commands to create a starting point for loading data from Pipedrive to DuckDB:

# create a new directory
mkdir pipedrive_pipeline
cd pipedrive_pipeline
# initialize a new pipeline with your source and destination
dlt init pipedrive duckdb
# install the required dependencies
pip install -r requirements.txt

The last command will install the required dependencies for your pipeline. The dependencies are listed in the requirements.txt:


You now have the following folder structure in your project:

├── .dlt/
│ ├── config.toml # configs for your pipeline
│ └── secrets.toml # secrets for your pipeline
├── pipedrive/ # folder with source specific files
│ └── ...
├── # your main pipeline script
├── requirements.txt # dependencies for your pipeline
└── .gitignore # ignore files for git (not required)

2. Configuring your source and destination credentials

The dlt cli will have created a .dlt directory in your project folder. This directory contains a config.toml file and a secrets.toml file that you can use to configure your pipeline. The automatically created version of these files look like this:

generated config.toml

# put your configuration values here

log_level="WARNING" # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see
dlthub_telemetry = true

generated secrets.toml

# put your secret values and credentials here. do not share this file and do not push it to github

pipedrive_api_key = "pipedrive_api_key" # please set me up!

2.1. Adjust the generated code to your usecase

Further help setting up your source and destinations
  • Read more about setting up the Pipedrive source in our docs.
  • Read more about setting up the DuckDB destination in our docs.

3. Running your pipeline for the first time

The dlt cli has also created a main pipeline script for you at, as well as a folder pipedrive that contains additional python files for your source. These files are your local copies which you can modify to fit your needs. In some cases you may find that you only need to do small changes to your pipelines or add some configurations, in other cases these files can serve as a working starting point for your code, but will need to be adjusted to do what you need them to do.

The main pipeline script will look something like this:

import dlt
from pipedrive import pipedrive_source

def load_pipedrive() -> None:
"""Constructs a pipeline that will load all pipedrive data"""
# configure the pipeline with your destination details
pipeline = dlt.pipeline(
pipeline_name="pipedrive", destination='duckdb', dataset_name="pipedrive_data"
load_info =

def load_selected_data() -> None:
"""Shows how to load just selected tables using `with_resources`"""
pipeline = dlt.pipeline(
pipeline_name="pipedrive", destination='duckdb', dataset_name="pipedrive_data"
# Use with_resources to select which entities to load
# Note: `custom_fields_mapping` must be included to translate custom field hashes to corresponding names
load_info =
"products", "deals", "deals_participants", "custom_fields_mapping"
# just to show how to access resources within source
pipedrive_data = pipedrive_source()
# print source info
# list resource names
# print `persons` resource info
# alternatively

def load_from_start_date() -> None:
"""Example to incrementally load activities limited to items updated after a given date"""
pipeline = dlt.pipeline(
pipeline_name="pipedrive", destination='duckdb', dataset_name="pipedrive_data"

# First source configure to load everything except activities from the beginning
source = pipedrive_source()
source.resources["activities"].selected = False

# Another source configured to activities starting at the given date (custom_fields_mapping is included to translate custom field hashes to names)
activities_source = pipedrive_source(
since_timestamp="2023-03-01 00:00:00Z"
).with_resources("activities", "custom_fields_mapping")

# Run the pipeline with both sources
load_info =[source, activities_source])

if __name__ == "__main__":
# run our main example
# load_pipedrive()
# load selected tables and display resource info
# load_selected_data()
# load activities updated since given date

Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:


4. Inspecting your load result

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline pipedrive info

You can also use streamlit to inspect the contents of your DuckDB destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline pipedrive show

5. Next steps to get your pipeline running in production

One of the beauties of dlt is, that we are just a plain Python library, so you can run your pipeline in any environment that supports Python >= 3.8. We have a couple of helpers and guides in our docs to get you there:

The Deploy section will show you how to deploy your pipeline to

  • Deploy with Github Actions: dlt can be deployed using Github Actions. This is a CI/CD runner that you can use for free. You need to specify when the GitHub Action should run using a cron schedule expression.
  • Deploy with Airflow: You can deploy dlt using Airflow. This method involves creating an Airflow DAG for your pipeline script that you should customize. The DAG uses dlt Airflow wrapper to make this process trivial.
  • Deploy with Google Cloud Functions: dlt can also be deployed with Google Cloud Functions. This is a serverless execution environment for building and connecting cloud services.
  • Other Deployment Options: There are also other ways to deploy dlt depending on your specific requirements and the resources you have available.

The running in production section will teach you about:

  • Monitor Your Pipeline: dlt provides robust tools for monitoring your data pipeline. It allows you to inspect and save load info, trace runtime, and alert on schema changes. You can learn more about it here.
  • Set Up Alerts: With dlt, you can easily set up alerts to notify you of any issues or changes in your data pipeline. This helps ensure your pipeline is running smoothly and efficiently. Check out how to do it here.
  • Set Up Tracing: Tracing is a crucial aspect of running a data pipeline. dlt allows you to get the runtime trace from the pipeline, which contains timing information on extract, normalize and load steps. Learn how to set up tracing here.

Available Sources and Resources

For this verified source the following sources and resources are available

Source pipedrive

Pipedrive source provides comprehensive data on sales activities, customer interactions, deals, and user information.

Resource NameWrite DispositionDescription
activitiesmergeRefers to scheduled events or tasks associated with deals, contacts, or organizations
custom_fields_mappingreplaceMapping for custom fields in Pipedrive
dealsmergePotential sale or transaction that you can track through various stages
deals_flowmergeRepresents the flow of deals in Pipedrive
deals_participantsmergeRepresents the participants of deals in Pipedrive
leadsmergeProspective customers or individuals that have shown interest in a company's products or services
organizationsmergeCompany or entity with which you have potential or existing business dealings
personsmergeIndividual contact or lead with whom sales deals can be associated
productsmergeGoods or services that a company sells, which can be associated with deals
usersmergeIndividual with a unique login credential who can access and use the platform

Additional pipeline guides

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!


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