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Loading Pipedrive Data to AWS Athena using Python's dlt Library

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This page provides technical documentation on how to load data from Pipedrive, a business messaging app, into AWS Athena, an interactive query service by Amazon. This process is facilitated using dlt, an open-source Python library. dlt simplifies and streamlines data transfer between these platforms, ensuring efficient and reliable data analysis. Our implementation also supports Iceberg tables, enhancing the querying capabilities in AWS Athena. 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.
  • Schema Enforcement and Curation: dlt empowers users to enforce and curate schemas, ensuring data consistency and quality. By adhering to predefined schemas, pipelines maintain data integrity and facilitate standardized data handling practices. Read more about adjusting a schema here.
  • Performance Scaling and Finetuning: dlt offers several mechanism and configuration options to scale up and finetune pipelines, including running extraction, normalization, and load in parallel. Learn more about performance here.
  • AWS Athena / Glue Catalog Destination: The Athena destination in dlt stores data as parquet files in S3 buckets and creates external tables in AWS Athena. You can then query those tables with Athena SQL commands. Read more about this destination here.
  • Community Support: dlt is a constantly growing library that supports many features and use cases needed by the community. Join the dlt 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 AWS Athena:

pip install "dlt[athena]"

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

# create a new directory
mkdir pipedrive_pipeline
cd pipedrive_pipeline
# initialize a new pipeline with your source and destination
dlt init pipedrive athena
# 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[athena]>=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.athena]
query_result_bucket = "query_result_bucket" # please set me up!
athena_work_group = "athena_work_group" # please set me up!

[destination.athena.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!

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 AWS Athena 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='athena', 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='athena', 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='athena', 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 Athena 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 involves setting up a CI/CD runner to execute your pipeline at specified intervals.
  • Deploy with Airflow: Using Google's managed Airflow environment, Google Composer, you can deploy your dlt pipeline. This method involves creating an Airflow DAG for your pipeline script.
  • Deploy with Google Cloud Functions: dlt also supports deployment with Google Cloud Functions. This serverless execution environment runs your code in response to events without requiring you to manage the underlying infrastructure.
  • Other Deployment Options: There are other ways to deploy your dlt pipeline. Explore more options here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides you with the capability to monitor your pipeline in production. You can inspect and save load info, trace, and schema changes. Check out the guide on how to monitor your pipeline.
  • Set Up Alerts: To ensure your pipeline runs smoothly, dlt allows you to set up alerts. These alerts can notify you of any issues or changes to your pipeline. Learn how to set up alerts.
  • Set Up Tracing: To understand the performance and behavior of your pipeline, dlt enables you to set up tracing. Tracing can provide valuable insights into your pipeline's operations. Find out how to set up tracing.

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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