Python dlt Guide: Load Salesforce Data to AWS S3

Connecting other file destinations

This document describes how to set up loading to aws 3, but our filesystem source can not only load to s3, but also to Google Cloud Storage, Google Drive, Azure, or local filesystem. Learn more about this here.

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This document provides guidance on using the open-source Python library dlt to load data from Salesforce, a cloud platform that enhances business operations and customer relationship management, to AWS S3, a remote file system and bucket storage service. Salesforce streamlines various business functions such as sales, marketing, and customer service. On the other hand, AWS S3 uses fsspec to simplify file operations and primarily serves as a staging area for other destinations. It can also be used to build a data lake swiftly. More information about Salesforce can be found at https://www.salesforce.com/. The dlt library plays a crucial role in this data loading process.

dlt Key Features

• Salesforce Integration: Salesforce is a dlt verified source that allows you to load data using the Salesforce API to the destination of your choice, supporting a variety of resources such as User, UserRole, Lead, Contact, Campaign, and more.

• Governance Support: dlt pipelines provide robust governance support through key mechanisms like pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn more about pipeline governance.

• Data Tracing: dlt supports identifiers, data lineage, and schema lineage, helping you trace the origin and transformations of your data. Find out more about data tracing.

• Advanced Deployment: dlt allows you to deploy from branches, local folders, or git repos, offering flexibility and control over your deployment process. Learn more about advanced deployment.

• Filesystem & Buckets Support: dlt can store data in remote file systems and bucket storages like S3, Google Storage, and Azure Blob Storage, making it a versatile tool for data management. Learn more about filesystem and buckets support.

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 AWS S3:

pip install "dlt[filesystem]"

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 AWS S3. You can run the following commands to create a starting point for loading data from Salesforce to AWS S3:

# create a new directory
mkdir my-salesforce-pipeline
cd my-salesforce-pipeline
# initialize a new pipeline with your source and destination
dlt init salesforce filesystem
# 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[filesystem]>=0.3.5

You now have the following folder structure in your project:

my-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:

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

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.filesystem]
bucket_url = "bucket_url" # please set me up!

[destination.filesystem.credentials]
aws_access_key_id = "aws_access_key_id" # please set me up!
aws_secret_access_key = "aws_secret_access_key" # please set me up!

Further help setting up your source and destinations

Please consult the detailed setup instructions for the AWS S3 destination in the dlt destinations documentation.

Likewise you can find the setup instructions for Salesforce source in the dlt verifed sources documentation.

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='filesystem', 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 AWS S3 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 provides a convenient way to deploy your pipelines using Github Actions. It allows you to specify a cron schedule expression to determine when the Github Action should run.
• Deploy with Airflow: You can also deploy your pipelines using Airflow. This is particularly useful if you are using Google's managed Airflow environment, Google Composer.
• Deploy with Google Cloud Functions: If you prefer to use serverless architecture, dlt supports deployment with Google Cloud Functions. This allows you to execute your pipelines in response to specific event triggers.
• Other Deployment Options: dlt offers a variety of other deployment options to suit your specific needs. You can explore these options 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 pipeline's performance and progress. Learn more about how to monitor your pipeline here.
• Set Up Alerts: Stay ahead of any potential issues with your pipeline by setting up alerts. dlt's alerting feature ensures that you are notified of any significant events or errors that occur during the pipeline's operation. Read more about setting up alerts here.
• Implement Tracing: Gain deeper insights into your pipeline's execution with dlt's tracing feature. Tracing allows you to track each step of your pipeline, making it easier to troubleshoot and optimize. 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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