Loading Data from Salesforce to Azure Cosmos DB with dlt in Python

tip

We will be using the dlt PostgreSQL destination to connect to Azure Cosmos DB. You can get the connection string for your Azure Cosmos DB database as described in the Azure Cosmos DB Docs.

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Loading data from Salesforce to Azure Cosmos DB can streamline your business operations and customer relationship management. Salesforce is a cloud platform that covers sales, marketing, and customer service, while Azure Cosmos DB is a fully managed NoSQL and relational database designed for modern app development. By using the open-source python library dlt, you can efficiently transfer data between these platforms. dlt simplifies the process, ensuring data consistency and reliability. For more information about Salesforce, visit this link.

dlt Key Features

• Pipeline Metadata: dlt pipelines leverage metadata for governance capabilities, including load IDs for tracking data loads and facilitating data lineage. Learn more.
• Schema Enforcement and Curation: Ensure data consistency and quality by enforcing and curating schemas, maintaining data integrity and standardized data handling practices. Read more.
• Schema Evolution: Stay proactive with governance by receiving alerts on schema changes, allowing necessary actions like reviewing and validating changes. Discover more.
• Scaling and Finetuning: Scale up and finetune pipelines with parallel processing, thread pools, async execution, and configurable memory buffers. Explore more.
• Building Blocks: Utilize existing building blocks to create sources, set up incremental loading, define schema, and more for a robust pipeline. Find out 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 Azure Cosmos DB:

pip install "dlt[postgres]"

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

# create a new directory
mkdir salesforce_pipeline
cd salesforce_pipeline
# initialize a new pipeline with your source and destination
dlt init salesforce postgres
# 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[postgres]>=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.postgres]
dataset_name = "dataset_name" # please set me up!

[destination.postgres.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 = 5432
connect_timeout = 15

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 Azure Cosmos DB 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='postgres', 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 Azure Cosmos DB 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: Learn how to set up continuous integration and deployment for your dlt pipelines using Github Actions.
• Deploy with Airflow: Follow this guide to deploy your dlt pipelines using Airflow, including setup with Google Composer.
• Deploy with Google Cloud Functions: Detailed instructions on deploying dlt pipelines using Google Cloud Functions.
• Explore other deployment options: Discover various other methods to deploy your dlt pipelines here.

The running in production section will teach you about:

• How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline in production to ensure everything runs smoothly. Read more
• Set up alerts: Set up alerts to get notified about any issues or anomalies in your dlt pipeline. Read more
• And set up tracing: Implement tracing to track the execution flow and performance of your dlt pipeline. Read more

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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