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Python Data Loading from pipedrive to microsoft sql server with dlt

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This technical documentation provides insights on how to load data from pipedrive, a business-focused messaging app, to Microsoft SQL Server, a relational database management system (RDBMS), using the open-source Python library, dlt. The process involves establishing a connection to a SQL Server instance or database and communicating using Transact-SQL. Detailed information about the source, pipedrive, can be accessed at https://pipedrive.com. The guide aims to simplify the data loading process, leveraging the functionalities offered by dlt to ensure seamless and efficient data management.

dlt Key Features​

  • Automated Maintenance: dlt provides automated maintenance with schema inference and evolution and alerts. This feature reduces the time spent on manual maintenance and development. More details can be found here.

  • Running Anywhere Python Runs: dlt can run on any platform where Python runs, including Airflow, serverless functions, and notebooks. It does not require external APIs, backends, or containers, making it scalable for various infrastructures. Check out more here.

  • Schema Enforcement and Curation: dlt allows users to enforce and curate schemas, ensuring data consistency and quality. It maintains data integrity and facilitates standardized data handling practices. Learn more about this feature here.

  • Support for Multiple Destinations: dlt supports a wide range of destinations, including popular databases like Microsoft SQL Server. It provides a detailed guide on how to configure and use each destination. More information can be found here.

  • Post-Loading Transformations: dlt offers several options for transformations after loading the data, such as using dbt, the dlt SQL client, or Pandas. These options allow you to shape and manipulate the data before or after loading it. Find out more 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 Microsoft SQL Server:

pip install "dlt[mssql]"

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

# create a new directory
mkdir pipedrive_pipeline
cd pipedrive_pipeline
# initialize a new pipeline with your source and destination
dlt init pipedrive mssql
# 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[mssql]>=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.mssql.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 = 1433
connect_timeout = 15
driver = "driver" # 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 Microsoft SQL Server 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='mssql', 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='mssql', 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='mssql', 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 Microsoft SQL Server 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 offers a seamless integration with Github Actions that allows you to set up your own CI/CD pipeline. You can schedule your workflows and automate your deployment process.
  • Deploy with Airflow: With dlt, you can easily deploy your pipelines using Airflow. The library provides a command that generates an Airflow DAG for your pipeline script and guides you through the deployment process.
  • Deploy with Google Cloud Functions: dlt supports deployment with Google Cloud Functions. This allows you to execute your data pipeline in a serverless environment, which can be triggered by HTTP requests or Cloud events.
  • Explore Other Deployment Options: dlt is designed to be flexible and adaptable, providing you with various ways to deploy your pipelines. You can explore 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 powerful tools for monitoring your data pipeline. You can easily track the status of your pipeline, inspect the data, and even save the load information for future reference. Learn more on how to monitor your pipeline.
  • Set Up Alerts: With dlt, you can set up alerts to notify you of any changes or issues in your pipeline. This feature ensures you are always up-to-date with your pipeline's performance and can quickly address any problems. Check out the guide on how to set up alerts.
  • Enable Tracing: Tracing allows you to track the flow of data through your pipeline, providing valuable insights into its operation and performance. dlt makes it easy to set up tracing for your pipeline. Read more about 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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