Loading Salesforce Data to DuckDB with Python using dlt

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Welcome to our technical documentation on how to load data from salesforce, a potent cloud platform that enhances business operations and customer relationship management, to duckdb, a swift in-process analytical database with a rich SQL dialect and deep client API integrations. This process is facilitated by dlt, an open-source python library. Further information about salesforce can be found at Salesforce.com. This guide will walk you through the steps of data loading, offering a comprehensive understanding of how dlt, salesforce, and duckdb interact.

dlt Key Features

• Automated maintenance: dlt features schema inference and evolution alerts, and with its short, declarative code, maintenance is simplified. Learn more about schema enforcement and curation.

• Run it where Python runs: dlt can be run on Airflow, serverless functions, notebooks, and more. It doesn't require external APIs, backends, or containers, and scales on both micro and large infrastructure. Learn more about scaling and finetuning.

• User-friendly interface: dlt provides a declarative interface that is user-friendly and removes knowledge obstacles for beginners while empowering senior professionals. Learn more about getting started with dlt.

• Data 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 governance support in dlt pipelines.

• Join the dlt community: Get involved with the dlt community, give the library a star on GitHub, ask questions and share how you use the library on Slack, and report problems and make feature requests. Learn more about joining the dlt 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 DuckDB:

pip install "dlt[duckdb]"

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

# create a new directory
mkdir salesforce_pipeline
cd salesforce_pipeline
# initialize a new pipeline with your source and destination
dlt init salesforce duckdb
# 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[duckdb]>=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!

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 DuckDB 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='duckdb', 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 DuckDB 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 allows you to deploy your pipelines using Github Actions. This method is free and provides a CI/CD runner for your tasks.
• Deploy with Airflow: You can deploy your dlt pipelines using Airflow. This method is particularly useful if you are using Google Composer, a managed Airflow environment provided by Google.
• Deploy with Google Cloud Functions: dlt also supports deployment with Google Cloud Functions. This method allows you to run your pipelines in response to events on Google Cloud Platform.
• Other Deployment Methods: dlt supports several other deployment methods. You can find more information on these methods here.

The running in production section will teach you about:

• Monitor Your Pipeline: dlt provides robust monitoring capabilities to keep track of your pipeline's performance and status. You can easily monitor your pipeline with dlt to ensure everything is running as expected. Learn more about this feature here.
• Set Up Alerts: With dlt, you can set up alerts to notify you of any issues or changes in your pipeline. This feature ensures that you are always aware of the status of your pipeline and can take action if necessary. Learn more about setting up alerts here.
• Set Up Tracing: Tracing is a powerful feature provided by dlt that allows you to keep track of the execution of your pipeline. It provides detailed information about each step of the pipeline, making it easier to debug and optimize your pipeline. Learn more about setting up tracing 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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