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Loading Pipedrive Data to PostgreSQL Using Python's dlt Library

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This technical documentation provides guidance on using the dlt Python library to load data from Pipedrive, a business CRM tool, to PostgreSQL, an open source object-relational database system. The dlt library simplifies the process of extracting data from Pipedrive, transforming it as needed, and loading it into PostgreSQL. This allows you to leverage the powerful features of PostgreSQL to manage and analyze your Pipedrive data. For more information on Pipedrive, visit https://pipedrive.com.

dlt Key Features​

  • Comprehensive Data Pipeline: dlt offers functionality to support the entire extract and load process. It provides effortless loading via a schema discovery, versioning and evolution engine that ensures you can "just load" any data with row and column level lineage. More details here.

  • Support for Multiple Destinations: dlt supports loading data to a variety of destinations like Google BigQuery, Amazon Redshift, Postgres, and more. This enables flexibility in choosing the right data storage for your use case. Refer to the full list of available destinations here.

  • Robust Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. These features contribute to better data management practices, compliance adherence, and overall data governance. More information can be found here.

  • Transformations After Loading: dlt provides several options for transformations after loading the data. You can use the dlt SQL client to query the loaded data and perform transformations using SQL statements. You can also fetch query results as Pandas data frames and perform transformations using Pandas functionalities. More about this here.

  • Easy to Scale and Fine-tune: dlt offers several mechanism and configuration options to scale up and fine-tune pipelines. It supports running extraction, normalization and load in parallel, and writing sources and resources that are run in parallel via thread pools and async execution. More about scaling and fine-tuning can be found 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 PostgreSQL:

pip install "dlt[postgres]"

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

# create a new directory
mkdir pipedrive_pipeline
cd pipedrive_pipeline
# initialize a new pipeline with your source and destination
dlt init pipedrive postgres
# 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[postgres]>=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.postgres.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 = 5432
connect_timeout = 15

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 PostgreSQL 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='postgres', dataset_name="pipedrive_data"
    )
    load_info = pipeline.run(pipedrive_source())
    print(load_info)


def load_selected_data() -> None:
    """Shows how to load just selected tables using `with_resources`"""
    pipeline = dlt.pipeline(
        pipeline_name="pipedrive", destination='postgres', 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='postgres', 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 PostgreSQL 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 simple way to deploy your pipeline using Github Actions. It's a free CI/CD runner that can be scheduled to run at specific times.
  • Deploy with Airflow: With dlt, you can easily deploy your pipeline using Airflow, a platform used to programmatically author, schedule and monitor workflows.
  • Deploy with Google Cloud Functions: dlt also supports deployment on Google Cloud Functions, a serverless execution environment for building and connecting cloud services.
  • Other Deployment Options: dlt provides various other ways to deploy your pipeline. You can check out these other deployment options to find the one that best suits your needs.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides robust tools for monitoring your data pipeline. This includes tracking the progress of your pipeline, checking the status of jobs, and inspecting load information. Learn more about how to monitor your pipeline here.
  • Set Up Alerts: It's important to stay informed about any issues that might occur during the running of your pipeline. dlt allows you to set up alerts to notify you of any errors or issues that arise. Find out how to set up alerts here.
  • Enable Tracing: Tracing provides valuable insights into the runtime behavior of your pipeline. It allows you to track the timing information of various steps and inspect the configuration and secret values. Learn more about setting 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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