Well, before going to explore how to use python to get the salesforce data, we need to know why data extraction is necessary. Getting the data is mandatory as part of the backup process, can easily integrate with the third party vendors as well as systems, and the data can be stored in the spreadsheet for easy reporting purposes.However to know the deep insights of why and when to get the salesforce data, Salesforce Certification Training is very beneficial.
Data extraction from Salesforce could be done manually or automatically, and it has always been in CSV format.
As a business analyst, users may regularly encounter the problem with no easy usability to organize or optimize personalized report exports from our Salesforce besides analysis or center console building. In this blog post we are going to use the python in order to fetch the salesforce data effectively.We will examine 3 techniques of computerized data retrieval and their advantages and disadvantages:
- By utilizing the Python package ‘simple _salesforce'
- REST API for Salesforce
- Timetabling email attachments in Salesforce (beta)
Utilizing the python package ‘simple _salesforce:
One easy fix designers discovered would be to use the Python package ‘simple salesforce,' that also allows users to access Salesforce documented data directly into the Python using the ‘Salesforce' feature to record in algorithmically and also a GET function to fetch the report. These are best used since trying to access a pre-built custom report which needs to still be outsourced on a regular basis, possibly for other reasons.
Custom reports would then be driven into some kind of database or through SFTP using this template. This alternative is compatible with both Salesforce Lightning as well as Salesforce Classic.
To make the appropriate script work, guys would need to gather reliable data such as:
- Gather the username and password details that one uses to login into the salesforce account.
- Immediately after the login you will be assigned with a salesforce security token. One can find this token once they are logged into their personal salesforce account, just go tube settings tabMy personal informationReset the security token.
- One can locate the salesforce company ID in the beginning of the URL of your salesforce instance.
- Next the salesforce reporting ID which can be found by opening the reporting section in the salesforce and that you wish to download the data.
The following is a script where you need to give your details so that you can fetch your data very easily.
from simple_salesforce import Salesforce
import requests
import pandas as pd
import csv
from io import StringIO
# Sign into Salesforce
sf = Salesforce(username='user@company.com',
password='password',
security_token='token')
# Set report details
sf_org = 'https://company.salesforce.com/'
report_id = 'report_id'
export_params = '?isdtp=p1export=1enc=UTF-8xf=csv'
# Download report
sf_report_url = sf_org + report_id + export_params
response = requests.get(sf_report_url, headers=sf.headers, cookies={'sid': sf.session_id})
new_report = response.content.decode('utf-8')
report_df = pd.read_csv(StringIO(new_report))
Next we will discuss the pros and cons of this process.
Pros:
- The package seems to be straightforward to use and be expanded with additional reports.
- Python does have a low barrier to entry for experts to use.
Cons:
- Because Simple_salesforce' is just not fully recognized by Salesforce, this could tear with just about any site notifications.
Salesforce REST API:
Salesforce seems to have its own SQL-style API, this same Salesforce Object Query Language (SOQL), besides data access, to extensive online supporting documents. It collects information from Salesforce objects utilizing SQL syntax. It is the most dependable as well as officially supported platform for data pipeline projects.The SOQL method is often supported either by Python package ‘simple salesforce.' For instance, the query here will drag all records from Contact object where the last name is Mottershead as well as format the outcomes into such a data frame. This method is best suited for developing new news stories or retrieving vast amounts of data in large quantities.
# Query Salesforce
data = sf.query("SELECT Id, Email FROM Contact WHERE LastName = 'Mottershead'")
# Set up columns
id = []
email = []
for record in data['records']:
id.append(record['Id'])
email.append(record['Email'])
record_dict = {}
record_dict['Id'] = id
record_dict['Email'] = email
df = pd.DataFrame(record_dict)
Pros and Cons:
Pros:
- The most dependable way of integrating to and fetching Salesforce report information
- Comprehensive documentation is available on the Salesforce website.
Cons:
- Higher market hurdle to get started and optimize, particularly if evaluating straight to REST API.
- SQL understanding required
Scheduling the email attachment for salesforce:
Salesforce must have released details of a beta release for connecting the CSVs file documents with the email scheduling format. Initially, just an overview of the document was accessible but was sent throughout the original email, so it's a significant improvement! This technique is effective for simple models that require monitoring, like current morning contributes or agreements that are about to expire for sales teams.
Pros and Cons:
Pros:
- It is very simple to disperse customized reports to customers.
- Without the use of any additional tools, it is accessible via the Salesforce homepage.
Cons:
- Data limits are initially listed at 15,000 rows as well as 30 columns, that datasets frequently exceed.
- Challenging to allow proper ingestion from email attachments into file Email is not really a safe form however, guess it depends on the responsiveness of the original data.
Conclusion:
I hope the above mentioned is quite enough to know how to use python in order to extract your salesforce data. Had any queries drop them in the comments section to get clarified.
