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Loading Salesforce Data to AlloyDB with dlt in Python

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We will be using the dlt PostgreSQL destination to connect to AlloyDB. You can get the connection string for AlloyDB from the GCP AlloyDB Console.

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Salesforce is a cloud platform that streamlines business operations and customer relationship management, covering sales, marketing, and customer service. AlloyDB for PostgreSQL is a fully managed, PostgreSQL-compatible database service designed for demanding workloads, including hybrid transactional and analytical processing. It combines a Google-built database engine with a cloud-based, multi-node architecture to deliver enterprise-grade performance, reliability, and availability. This documentation explains how to load data from Salesforce to AlloyDB using the open-source Python library dlt. For more information about the source, visit Salesforce.

dlt Key Features​

  • Automated Maintenance: With schema inference and evolution and alerts, and with short declarative code, maintenance becomes simple. Learn more
  • Run Anywhere Python Runs: On Airflow, serverless functions, notebooks. No external APIs, backends or containers, scales on micro and large infra alike. Learn more
  • User-friendly Interface: Declarative interface that removes knowledge obstacles for beginners while empowering senior professionals. Learn more
  • Scalability: Offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques for efficient processing of large datasets. Learn more
  • Transformation Options: Use dbt, SQL client, or Pandas for transforming data before or after loading, ensuring data quality and consistency. Learn 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 AlloyDB:

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

# 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 AlloyDB 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 AlloyDB 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 use GitHub Actions to deploy your dlt pipeline with a cron schedule or on push events. Read more
  • Deploy with Airflow and Google Composer: Follow this guide to deploy your dlt pipeline using Airflow and Google Composer, including setting up your GitHub repository and initializing deployment. Read more
  • Deploy with Google Cloud Functions: Discover how to deploy your dlt pipeline using Google Cloud Functions for a serverless deployment approach. Read more
  • Other Deployment Methods: Explore various other methods to deploy your dlt pipeline, including using different cloud services and CI/CD tools. Read more

The running in production section will teach you about:​

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline to ensure smooth operation and quick identification of issues. Read more here.
  • Set up alerts: Stay informed about the status of your dlt pipeline by setting up alerts. This guide walks you through the steps to configure alerts for various events. Find out how here.
  • Set up tracing: Implement tracing to gain insights into the execution of your dlt pipeline. This helps in debugging and understanding performance bottlenecks. Learn how to set 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 NameWrite DispositionDescription
accountmergeRepresents an individual or organization that interacts with your business
campaignreplaceRepresents a marketing initiative or project designed to achieve specific goals
contactreplaceRepresents an individual person associated with an account or organization
leadreplaceRepresents a prospective customer/individual/org. that has shown interest in a company's products/services
opportunitymergeRepresents a sales opportunity for a specific account or contact
pricebook_2replaceUsed to manage product pricing and create price books
pricebook_entryreplaceRepresents a specific price for a product in a price book
product_2replaceUsed for managing and organizing your product-related data within the Salesforce ecosystem
sf_userreplaceRepresents an individual who has access to a Salesforce org or instance
user_rolereplaceRepresents a role within the organization's hierarchy

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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