dlt Python Library: Loading Pipedrive Data to Dremio

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This page provides technical documentation on how to use the open-source Python library, dlt, to load data from Pipedrive, a business messaging app that connects people to the information they need. The data loaded from Pipedrive can then be used with Dremio, a data lakehouse solution that offers flexibility, scalability, and performance to meet leaders at all stages of their data journey. By using dlt, you can bring users closer to the data and leverage the full potential of Pipedrive and Dremio. For more information about Pipedrive, visit https://pipedrive.com.

dlt Key Features

• Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn more about these features here.
• Scalability and Finetuning: dlt offers several mechanisms and configuration options to scale up and finetune pipelines, including running extraction, normalization, and load in parallel. Read more about performance here.
• Versatile Data Extraction: dlt provides scalable data extraction by leveraging iterators, chunking, and parallelization techniques, and utilizes implicit extraction DAGs for efficient API calls. Learn more here.
• Post-Loading Transformations: After loading the data, you have several options for transformations, including using dbt, the dlt SQL client, or Pandas. Learn more about these options here.
• Community Support: dlt is a constantly growing library supported by a vibrant community. You can join the community on Slack to find recent releases or discuss what you can build with dlt. Join the community 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 Dremio:

pip install "dlt[dremio]"

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 Dremio. You can run the following commands to create a starting point for loading data from Pipedrive to Dremio:

# create a new directory
mkdir pipedrive_pipeline
cd pipedrive_pipeline
# initialize a new pipeline with your source and destination
dlt init pipedrive dremio
# 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:

dlt[dremio]>=0.3.5

You now have the following folder structure in your project:

pipedrive_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── pipedrive/                   # folder with source specific files
│   └── ...
├── pipedrive_pipeline.py        # 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

[runtime]
log_level="WARNING"  # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see https://dlthub.com/docs/telemetry
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

[sources.pipedrive]
pipedrive_api_key = "pipedrive_api_key" # please set me up!

[destination.dremio]
dataset_name = "dataset_name" # please set me up!
staging_data_source = "staging_data_source" # please set me up!

[destination.dremio.credentials]
database = "database" # please set me up!
password = "password" # please set me up!
username = "username" # please set me up!
host = "host" # please set me up!
port = 32010

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 Dremio 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 pipedrive_pipeline.py, 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='dremio', dataset_name="pipedrive_data"
    )
    load_info = pipeline.run(pipedrive_source())
    print(load_info)
    print(pipeline.last_trace.last_normalize_info)


def load_selected_data() -> None:
    """Shows how to load just selected tables using `with_resources`"""
    pipeline = dlt.pipeline(
        pipeline_name="pipedrive", destination='dremio', 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 = pipeline.run(
        pipedrive_source().with_resources(
            "products", "deals", "deals_participants", "custom_fields_mapping"
        )
    )
    print(load_info)
    # just to show how to access resources within source
    pipedrive_data = pipedrive_source()
    # print source info
    print(pipedrive_data)
    print()
    # list resource names
    print(pipedrive_data.resources.keys())
    print()
    # print `persons` resource info
    print(pipedrive_data.resources["persons"])
    print()
    # alternatively
    print(pipedrive_data.persons)


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='dremio', 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 = pipeline.run([source, activities_source])
    print(load_info)


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
    # load_from_start_date()

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

python pipedrive_pipeline.py

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 Dremio 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 provides a seamless integration with Github Actions to automate your deployment process. You can schedule your pipeline to run at specific intervals and it also supports manual triggering.

• Deploy with Airflow: dlt allows you to deploy your pipeline with Airflow. It generates an Airflow DAG for your pipeline script which you can customize according to your needs.

• Deploy with Google Cloud Functions: dlt supports deployment with Google Cloud Functions. It enables you to run your pipeline in a fully managed environment where Google Cloud handles all the infrastructure for you.

• Other Deployment Options: dlt offers a variety of other deployment options to suit your specific needs. You can explore these options here.

The running in production section will teach you about:

• Monitor your pipeline: dlt provides tools to monitor your pipeline and ensure it's running smoothly. Check out this guide on how to monitor your pipeline.
• Set up alerts: Stay informed about the status of your pipeline with alerts. Learn how to set up alerts with dlt.
• Set up tracing: Tracing provides valuable insights into the performance and behavior of your pipeline. Here's a guide on how to set up tracing in dlt.

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 Name	Write Disposition	Description
activities	merge	Refers to scheduled events or tasks associated with deals, contacts, or organizations
custom_fields_mapping	replace	Mapping for custom fields in Pipedrive
deals	merge	Potential sale or transaction that you can track through various stages
deals_flow	merge	Represents the flow of deals in Pipedrive
deals_participants	merge	Represents the participants of deals in Pipedrive
leads	merge	Prospective customers or individuals that have shown interest in a company's products or services
organizations	merge	Company or entity with which you have potential or existing business dealings
persons	merge	Individual contact or lead with whom sales deals can be associated
products	merge	Goods or services that a company sells, which can be associated with deals
users	merge	Individual with a unique login credential who can access and use the platform

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