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Prerequisites:
- Access to a Freshdesk account with API privileges.
- Access to a Google account with Google Sheets and Google Developers Console.
- Basic knowledge of REST APIs and JSON.
- Familiarity with a programming language such as Python, JavaScript, etc.
- Go to the Google Developers Console (https://console.developers.google.com/).
- Create a new project or select an existing one.
- Enable the Google Sheets API for your project.
- Go to “Credentials” and create credentials for your application. You’ll typically want to create an OAuth 2.0 client ID.
- Download the JSON file with your credentials.
- Install the Google client library in your development environment, for example, for Python, use
pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib
.
Use the downloaded credentials JSON to authenticate your application. Follow the instructions in the library documentation to set up the OAuth flow and obtain the necessary tokens.
- Use the Google Sheets API to create a new spreadsheet if you don’t already have one set up.
- Store the spreadsheet ID for later use.
- Log in to your Freshdesk account.
- Go to your profile settings to find your API key.
- Use Freshdesk’s REST API to fetch the data you want to move to Google Sheets. You’ll need to make HTTP GET requests to the appropriate Freshdesk API endpoints.
- For example, to get tickets, you’d call
https://<yourdomain>.freshdesk.com/api/v2/tickets
. - Use your Freshdesk API key for authentication by including it in the request header.
Once you have the JSON response from Freshdesk, parse and format the data to match the structure of the Google Sheets where you want to insert it.
- Use the Google Sheets API to insert the formatted data into the spreadsheet.
- You may need to use the spreadsheets.values.append method, which allows you to append new rows to a spreadsheet.
- Implement error handling for both APIs to catch any issues during the data retrieval or insertion process.
- Validate the data before inserting it into Google Sheets to ensure it matches the expected format.
Example in Python:
Here’s a very basic example of how you might use Python to move data from Freshdesk to Google Sheets:
import requests
from googleapiclient.discovery import build
from google.oauth2.credentials import Credentials
# Google Sheets setup
SCOPES = ['https://www.googleapis.com/auth/spreadsheets']
creds = Credentials.from_authorized_user_file('token.json', SCOPES)
service = build('sheets', 'v4', credentials=creds)
spreadsheet_id = 'your_spreadsheet_id_here'
# Freshdesk setup
freshdesk_domain = 'yourdomain'
freshdesk_api_key = 'your_api_key'
headers = {'Content-Type': 'application/json', 'Authorization': 'Basic ' + freshdesk_api_key}
# Fetch data from Freshdesk
response = requests.get(f'https://{freshdesk_domain}.freshdesk.com/api/v2/tickets', headers=headers)
tickets = response.json()
# Format data for Google Sheets
values = [[ticket['id'], ticket['subject'], ticket['status']] for ticket in tickets]
# Insert data into Google Sheets
body = {'values': values}
result = service.spreadsheets().values().append(
spreadsheetId=spreadsheet_id,
range='Sheet1',
valueInputOption='RAW',
body=body
).execute()
Remember to replace 'your_spreadsheet_id_here', 'yourdomain', and 'your_api_key' with your actual spreadsheet ID, Freshdesk domain, and API key.
- To automate this process, you can create a script or a small application that runs at regular intervals.
- You can host this script on a server or use a scheduler like cron jobs for Unix/Linux systems or Task Scheduler for Windows.
- Keep your API keys and credentials secure.
- Follow best practices for storing and handling sensitive information, such as using environment variables or secure credential storage services.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Freshdesk is a service provided by Freshworks for handling the entire spectrum of customer engagement. A customer support software based in the Cloud, Freshdesk provides a scalable solution for managing customer support simply and efficiently. Freshdesk enables teams to track incoming tickets from a variety of channels; provide support across multiple platforms including phone, chat, and other messaging apps; categorize, prioritize, and assign tickets; prepare preformatted answer to common customer support questions; and much more.
Freshdesk's API provides access to a wide range of data related to customer support and service management. The following are the categories of data that can be accessed through Freshdesk's API:
1. Tickets: Information related to customer support tickets, including ticket ID, status, priority, and requester details.
2. Contacts: Data related to customer contacts, including contact ID, name, email address, and phone number.
3. Agents: Information about support agents, including agent ID, name, email address, and role.
4. Companies: Data related to companies that use Freshdesk for customer support, including company ID, name, and domain.
5. Conversations: Information related to customer conversations, including conversation ID, status, and participants.
6. Knowledge base: Data related to the knowledge base, including articles, categories, and folders.
7. Surveys: Information related to customer satisfaction surveys, including survey ID, status, and responses.
8. Time entries: Data related to time entries for support agents, including time spent on tickets and activities.
9. Custom fields: Information related to custom fields created in Freshdesk, including field ID, name, and value.
Overall, Freshdesk's API provides access to a comprehensive set of data that can be used to improve customer support and service management.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: