Python Data Loading from salesforce to databricks using dlt

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This page provides technical documentation on how to use the open-source Python library, dlt, to load data from Salesforce to Databricks. Salesforce is a cloud platform that simplifies business operations and customer relationship management, covering areas such as sales, marketing, and customer service. On the other hand, Databricks is a unified data analytics platform, developed by the original creators of Apache Spark™, which expedites innovation by integrating data science, engineering, and business. The dlt library plays a crucial role in facilitating this data transfer. For more details about Salesforce, please visit https://www.salesforce.com/.

dlt Key Features

• Automated Maintenance: dlt offers automated maintenance with schema inference and evolution and alerts. It requires short declarative code, making maintenance simple. Read more
• Run it where Python runs: dlt can run on Airflow, serverless functions, notebooks, and more. It does not require external APIs, backends, or containers, and scales on micro and large infrastructures alike. Read more
• User-friendly, Declarative Interface: dlt provides a user-friendly, declarative interface that is easy for beginners to understand while empowering senior professionals. Read more
• Join the dlt community: You can ask questions and share how you use the library on Slack. You can also report problems and make feature requests on GitHub. Join the community
• Transformations after Loading: dlt offers several transformation options after loading the data. You can use dbt, the dlt SQL client, or Pandas to perform transformations on your data. Read more

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 Databricks:

pip install "dlt[databricks]"

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

# create a new directory
mkdir salesforce_pipeline
cd salesforce_pipeline
# initialize a new pipeline with your source and destination
dlt init salesforce databricks
# 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[databricks]>=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.databricks.credentials]
catalog = "catalog" # please set me up!
server_hostname = "server_hostname" # please set me up!
http_path = "http_path" # please set me up!
access_token = "access_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 Databricks 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='databricks', 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 Databricks 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 can be deployed using Github Actions, which is a CI/CD runner. You can specify when the Github Action should run using a cron schedule expression. You can learn more about this process here.
• Deploy with Airflow: dlt can also be deployed using Airflow, a platform used to programmatically author, schedule and monitor workflows. Google provides a managed Airflow environment known as Google Composer. You can learn more about deploying with Airflow here.
• Deploy with Google Cloud Functions: dlt can be deployed on serverless environments like Google Cloud Functions. This is useful for running pipelines that do not require a full-blown Airflow instance. You can learn more about this process here.
• Other Deployment Methods: dlt offers flexibility in deployment and can be run wherever Python runs. This includes various other environments and platforms. You can explore other deployment methods here.

The running in production section will teach you about:

• Monitor Your Pipeline: dlt provides comprehensive tools for monitoring your pipeline in production. It allows you to inspect and save load info and trace data, which can be crucial for diagnosing problems or optimizing performance. Learn more about it here.
• Set Up Alerts: With dlt, you can set up alerts to be notified about any issues or important events in your pipeline. This feature can be especially useful in a production environment where timely response to problems is critical. Check out the guide on setting up alerts here.
• Set Up Tracing: dlt also allows you to set up tracing for your pipeline. Tracing can help you understand the execution flow of your pipeline and identify any bottlenecks or issues. You can find more information 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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