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Loading Pipedrive Data to AWS S3 with Python Using dlt

Connecting other file destinations

This document describes how to set up loading to aws 3, but our filesystem source can not only load to s3, but also to Google Cloud Storage, Google Drive, Azure, or local filesystem. Learn more about this here.

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This guide provides technical instructions on how to use dlt, an open-source Python library, to load data from Pipedrive to AWS S3. Pipedrive is a cloud-based Customer Relationship Management tool that helps businesses manage their sales processes. On the other hand, AWS S3 is a storage service that can be used as a staging area for other destinations or to quickly build a data lake. With dlt, you can seamlessly extract data from Pipedrive and store it in AWS S3, enhancing your data management practices. For more information about Pipedrive, visit https://pipedrive.com.

dlt Key Features​

  • Pipeline Metadata: dlt pipelines leverage metadata to provide governance capabilities, ensuring data consistency and traceability. Read more about it here.
  • Schema Enforcement and Curation: dlt empowers users to enforce and curate schemas, maintaining data integrity and facilitating standardized data handling practices. Learn more here.
  • Schema Evolution: dlt alerts users to schema changes, allowing them to take necessary actions and maintain proactive governance. Find more details here.
  • Scaling and Finetuning: dlt provides various mechanisms and configuration options to scale up and fine-tune pipelines. Read more about it here.
  • Advanced Usage: dlt supports many advanced features and use cases. You can join the dlt community on Slack to find recent releases or discuss what you can build with dlt. Join 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 AWS S3:

pip install "dlt[filesystem]"

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 AWS S3. You can run the following commands to create a starting point for loading data from Pipedrive to AWS S3:

# create a new directory
mkdir my-pipedrive-pipeline
cd my-pipedrive-pipeline
# initialize a new pipeline with your source and destination
dlt init pipedrive filesystem
# 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[filesystem]>=0.3.5

You now have the following folder structure in your project:

my-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:

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

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.filesystem]
bucket_url = "bucket_url" # please set me up!

[destination.filesystem.credentials]
aws_access_key_id = "aws_access_key_id" # please set me up!
aws_secret_access_key = "aws_secret_access_key" # please set me up!
Further help setting up your source and destinations

Please consult the detailed setup instructions for the AWS S3 destination in the dlt destinations documentation.

Likewise you can find the setup instructions for Pipedrive source in the dlt verifed sources documentation.

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='filesystem', 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='filesystem', 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='filesystem', 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 AWS S3 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 allows you to deploy your pipelines using Github Actions. This CI/CD runner is essentially free and can be scheduled using a cron schedule expression.
  • Deploy with Airflow: You can also deploy your pipelines with Airflow. This is especially useful if you are using Google Composer, a managed Airflow environment provided by Google.
  • Deploy with Google Cloud Functions: If you are using Google Cloud, dlt allows you to deploy your pipelines with Google Cloud Functions. This serverless execution environment allows you to build and connect cloud services with code.
  • Other Deployment Options: dlt provides a variety of other deployment options for your pipelines. Check out the deployment walkthroughs to learn more.

The running in production section will teach you about:​

  • Monitoring your pipeline: Once your pipeline is up and running, it's important to keep an eye on its performance. dlt provides tools for monitoring your pipeline, allowing you to ensure everything is running smoothly. Find out more about how to monitor your pipeline here.
  • Setting up alerts: Stay informed about any issues with your pipeline by setting up alerts. dlt allows you to configure alerts that will notify you in case of any problems with your pipeline. Learn how to set up alerts here.
  • Tracing your pipeline: dlt provides tracing capabilities, allowing you to track the execution of your pipeline and identify any potential bottlenecks or issues. Discover how to set up tracing for your pipeline 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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