Python Data Loading from salesforce
to azure cloud storage
with dlt
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This document provides technical guidance on how to use the open-source Python library dlt
to load data from Salesforce
, a cloud-based platform for business operations and customer relationship management, into Azure Cloud Storage
. Azure Cloud Storage
is a service provided by Microsoft to store large amounts of data in formats like JSONL, Parquet, or CSV, facilitating the creation of data lakes. dlt
simplifies the process of transferring data from Salesforce
to Azure Cloud Storage
, ensuring seamless data integration. For more information about Salesforce
, visit https://www.salesforce.com/.
dlt
Key Features
Salesforce Integration:
dlt
provides a verified source for Salesforce, enabling seamless data extraction and loading from the Salesforce platform to your preferred destination.Robust Data Governance:
dlt
pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn more about these features here.Data Tracing:
dlt
supports data lineage and schema lineage, enabling better traceability and understanding of your data sources. Find out more here.Flexible Deployment Options: With
dlt
, you can easily deploy your data pipelines from different branches, local folders, or git repositories. Learn how to do this here.Filesystem and Bucket Integration:
dlt
supports data storage in remote file systems and bucket storages like S3, Google Storage, or Azure Blob Storage. Find out how to set this up here.
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 Cloud Storage
:
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 Azure Cloud Storage
. You can run the following commands to create a starting point for loading data from Salesforce
to Azure Cloud Storage
:
# create a new directory
mkdir salesforce_pipeline
cd 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:
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.filesystem]
dataset_name = "dataset_name" # please set me up!
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!
2.1. Adjust the generated code to your usecase
The default filesystem destination is configured to connect to AWS S3. To load to Azure Cloud Storage, update the [destination.filesystem.credentials]
section in your secrets.toml
.
[destination.filesystem.credentials]
azure_storage_account_name="Please set me up!"
azure_storage_account_key="Please set me up!"
By default, the filesystem destination will store your files as JSONL
. You can tell your pipeline to choose a different format with the loader_file_format
property that you can set directly on the pipeline or via your config.toml
. Available values are jsonl
, parquet
and csv
:
[pipeline] # in ./dlt/config.toml
loader_file_format="parquet"
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 Azure Cloud Storage
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 deploy your
dlt
pipeline using GitHub Actions with a step-by-step guide. Read more - Deploy with Airflow and Google Composer: Follow this guide to deploy your
dlt
pipeline using Airflow and Google Composer. Read more - Deploy with Google Cloud Functions: This guide shows you how to deploy your
dlt
pipeline using Google Cloud Functions. Read more - Other Deployment Options: Explore various other methods to deploy your
dlt
pipeline, including detailed walkthroughs and examples. Read more
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 smooth and reliable data processing. How to Monitor your pipeline - Set up alerts: Configure alerts to stay informed about the status and performance of your
dlt
pipeline, helping you to quickly address any issues that arise. Set up alerts - Set up tracing: Implement tracing to gain detailed insights into the execution of your
dlt
pipeline, allowing for easier debugging and optimization. And set up tracing
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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