Loading Data from salesforce to motherduck using Python dlt Library

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dlt is an open-source Python library that aids in data management. This technical documentation provides a guide on how to use dlt to load data from Salesforce to MotherDuck. Salesforce is a cloud platform that enhances business operations and customer relationship management, including sales, marketing, and customer service. On the other hand, MotherDuck is an in-process analytical database known for its speed and robust SQL dialect, along with deep integrations into client APIs. You can find more information about Salesforce at https://www.salesforce.com/. This guide will walk you through the steps needed to successfully transfer data from Salesforce to MotherDuck using dlt.

dlt Key Features

• Pipeline Metadata Utilization: dlt pipelines leverage metadata to provide governance capabilities. This includes load IDs, which consist of a timestamp and pipeline name. Load IDs enable incremental transformations and data vaulting by tracking data loads and facilitating data lineage and traceability. Read More
• Schema Enforcement and Curation: dlt empowers users to enforce and curate schemas, ensuring data consistency and quality. Schemas define the structure of normalized data and guide the processing and loading of data. Read More
• Schema Evolution Alerts: dlt enables proactive governance by alerting users to schema changes. When modifications occur in the source data’s schema, dlt notifies stakeholders, allowing them to take necessary actions. Read More
• Scaling and Finetuning: dlt offers several mechanisms and configuration options to scale up and finetune pipelines. This includes running extraction, normalization, and load in parallel, writing sources and resources that are run in parallel via thread pools and async execution, and finetuning the memory buffers, intermediary file sizes, and compression options. Read More
• Community Support: dlt is a constantly growing library that supports many features and use cases needed by the community. Users can join the dlt Slack community to find recent releases or discuss what they can build with dlt. Join the Community

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 MotherDuck:

pip install "dlt[motherduck]"

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

# create a new directory
mkdir salesforce_pipeline
cd salesforce_pipeline
# initialize a new pipeline with your source and destination
dlt init salesforce motherduck
# 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:

simple-salesforce>=1.12.4
dlt[motherduck]>=0.3.5

You now have the following folder structure in your project:

salesforce_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── salesforce/                   # folder with source specific files
│   └── ...
├── salesforce_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.salesforce]
user_name = "user_name" # please set me up!
password = "password" # please set me up!
security_token = "security_token" # please set me up!

[destination.motherduck.credentials]
database = "database" # please set me up!
password = "password" # 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 Salesforce source in our docs.
• Read more about setting up the MotherDuck 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 salesforce_pipeline.py, as well as a folder salesforce 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:

#!/usr/bin/env python3
"""Pipeline to load Salesforce data."""
import dlt
from salesforce import salesforce_source


def load() -> None:
    """Execute a pipeline from Salesforce."""

    pipeline = dlt.pipeline(
        pipeline_name="salesforce", destination='motherduck', dataset_name="salesforce_data"
    )
    # Execute the pipeline
    load_info = pipeline.run(salesforce_source())

    # Print the load info
    print(load_info)


if __name__ == "__main__":
    load()

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

python salesforce_pipeline.py

4. Inspecting your load result

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline salesforce info

You can also use streamlit to inspect the contents of your MotherDuck destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline salesforce 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 supports deployment with Github Actions. This allows you to use a CI/CD runner that you can use for free. You can find the step by step guide on how to do this here.
• Deploy with Airflow: dlt can be deployed with Airflow. This provides a way to programmatically author, schedule and monitor data pipelines. The step by step guide on how to deploy dlt with Airflow can be found here.
• Deploy with Google Cloud Functions: dlt can be deployed with Google Cloud Functions. This allows you to run your code without thinking about servers and only pay for the compute time you consume. The step by step guide on how to deploy dlt with Google Cloud Functions can be found here.
• Other Deployment Options: dlt supports other deployment options as well. You can explore all the different ways to deploy dlt here.

The running in production section will teach you about:

• Monitor Your Pipeline: dlt provides a comprehensive monitoring system that allows you to keep track of your data pipeline's performance and status. Learn more about it here.
• Set Up Alerts: Stay informed about any issues or changes in your pipeline through dlt's alerting feature. It allows you to set up custom alerts for various events and anomalies. Find out more here.
• Enable Tracing: With dlt's tracing feature, you can easily track the execution of your pipeline and debug any issues that may arise. Learn how to set it up here.

Available Sources and Resources

For this verified source the following sources and resources are available

Source salesforce

"Salesforce source provides comprehensive business data, covering customer details, sales opportunities, product pricing, and marketing campaigns."

Resource Name	Write Disposition	Description
account	merge	Represents an individual or organization that interacts with your business
campaign	replace	Represents a marketing initiative or project designed to achieve specific goals
contact	replace	Represents an individual person associated with an account or organization
lead	replace	Represents a prospective customer/individual/org. that has shown interest in a company's products/services
opportunity	merge	Represents a sales opportunity for a specific account or contact
pricebook_2	replace	Used to manage product pricing and create price books
pricebook_entry	replace	Represents a specific price for a product in a price book
product_2	replace	Used for managing and organizing your product-related data within the Salesforce ecosystem
sf_user	replace	Represents an individual who has access to a Salesforce org or instance
user_role	replace	Represents a role within the organization's hierarchy

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