How To Automate Data Extraction From Salesforce Using Python?

A Practical Guide To Extracting Salesforce Data Automatically Using Python

Did you know that over 70% of professionals feel overwhelmed by data management tasks? It's a big issue! With manual data extraction, we have limited data query options that restrict your Salesforce data migration capabilities.

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Besides, one must spend hours running reports in Salesforce and downloading CSV files for each report. Then, import the same data and manipulate it. Finally, it can be pasted into Google Sheets or dashboards for reporting.

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Why spend hours pulling reports and importing data? After all, which business has that much time and resources to waste?

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A better way is to automate data extraction from Salesforce using Python and perform further analyses.

Here's a simple guide by Minuscule to help you automate data extraction.

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Pre-Requisites For Salesforce Data Migration Using Python

Before we move towards the 'HOW,' here are some requirements for extracting data from Salesforce using Python. Checklist for all of them:

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Enable API Access

• First, start by installing Salesforce. Then, sign in and click on the Setup icon.
• Type Profiles and select it.
• Click on the profile you're using (e.g., System Administrator).
• In the profile settings, ensure that the API Enabled checkbox is checked.

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Unique Salesforce Credentials

You'll need your Salesforce username and password, which is typically the email address used to log in. Ensure your account has API access (which you should have checked in the previous step).

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Python Is installed On Your System

Check if Python is installed on your machine. If not, download and install Python from their official website.

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Salesforce Domain URL

• Click on the Setup icon in your developer's account.
• There, in the menu, click Company Settings >My Domain.
• Your current Domain URL will appear.It typically looks like - https://<your_instance>.salesforce.com. You need to use it for the Python code.

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Necessary Libraries

Simple Salesforce, pandas, and others are important libraries that connect Salesforce with Python:

• Simple-salesforce: A REST API client for Salesforce.
• Pandas: For data manipulation.
• Requests: To handle API requests.

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So, you must import these libraries. Open your CLI and enter these commands:

pip install simple-salesforce

pip install pandas

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Security Token

You may need access to Security Token for your API calls:

• In your Salesforce instance, click on the View Profile icon on the top right.
• Now, Select Settings from the dropdown menu.
• Look for the My Personal Information section on the screen's left side.
• There, scroll a little, and you will find the “Reset security token” option.

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A new security token will be sent to your registered email address. Keep this handy for your API connection.

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Report ID

If you want to download any kind of Salesforce report directly, you will need the Report ID:

• Go to Reports.
• In the URL, you will see something like https://<your_instance>.salesforce.com/00OXXXXXXXXXXXX.
• The Report ID is the portion at the end of the Domain URL. (e.g., 00OXXXXXXXXXXXX). It is imported as a Data Frame and is used to access your report via API.

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Steps To Extract Data From Salesforce Using Python

Following these steps, you can efficiently automate data extraction from Salesforce using Python:

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Step 1: Install And Set Up The Simple Salesforce Package

First, you need to install the Simple Salesforce package. Open your terminal and run the below command:

“pip install simple-salesforce”

This package lets you connect to Salesforce easily and perform operations like querying data.

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Step 2: Set Up API Access And Authentication

Use the simple-salesforce library to establish a connection:

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From Simple_Salesforce Import Salesforce

sf = Salesforce(

username='your_username',

password='your_password',

security_token='your_security_token'

)

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• Replace your _username and type your actual Salesforce username (it will be your email ID).
• Then, in your_password, you must enter your Salesforce password.
• Lastly, in 'your_security_token, enter your token. You can obtain the security token by resetting it if you don't have it. It will be sent to your email as discussed in the prerequisites.

Step 3: Fetching Data Into Data Frame

Now that you've set up everything, it's time to extract the data! You can do this by using two main approaches. Let's break down both options for you:

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Option 1: Download Pre-Built Salesforce Reports

If you already have custom reports in Salesforce, you can download them directly.

eport_id = 'your_report_id'

report_data = sf.rest().get(f'/services/data/vXX.X/analytics/reports/{report_id}')

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This will give you the report data in a JSON format, which you can then convert into a pandas DataFrame.

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Now that you have your data in a DataFrame, you can manipulate it as needed. For example, you can filter, sort, or aggregate the data to suit your reporting needs.

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Option 2 - Query Salesforce Data Using SOQL

SOQL (Salesforce Object Query Language) works like SQL but is tailored for Salesforce. You can query data directly from objects. This method gives you more flexibility in selecting the data you need. Here's how to do it:

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Step 1. Write Your SOQL Query

If you want to extract contact details, you might write: SELECT Id, Name, Email FROM Contact.

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Step 2: Execute The Query In Python

Now, use the following code to run your SOQL query and fetch the data:

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1. Define your SOQL query: query = "SELECT Id, Name, Email FROM Contact"
2. Execute the query: data = sf.query(query)
3. Convert the queried data into a DataFrame: import pandas as pd: df = pd.

           DataFrame(data['records']).drop(columns='attributes')

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Here, the drop(columns='attributes') part removes unnecessary metadata from the DataFrame, leaving you with clean data.

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Step 4: Automating Data Processing And Reporting

To run this extraction process automatically at specified intervals:

• Windows: Use Task Scheduler to run your Python script at desired times.
• Linux/Mac: Use cron jobs to schedule the script execution.

Add a line like this - 0 9 * * * /usr/bin/python3 /path/to/your_script.py

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For more robust scheduling, consider using Apache Airflow. You can also integrate Google Sheets or databases for real-time dashboards using libraries like pygsheets.

Ensure your script includes error handling and logging to monitor its performance and troubleshoot any issues.

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Enhance Your Data Pipeline With Automated Salesforce Extraction

Automating data extraction from Salesforce using Python saves time, reduces manual effort, and minimizes errors. With tools like simple-salesforce and pandas, you can efficiently pull data, process it, and even build automated reporting pipelines.

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If you need advanced automation solutions or help with Salesforce data integration , consider consulting with certified Salesforce partners. As a Salesforce consulting company at Minuscule Technologies, we simplify your work and make your data management smarter and faster!

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