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First, ensure you have access to Freshdesk's API. You'll need an API key, which can be found in your Freshdesk account under Profile Settings. The API key is essential for authenticating requests.
Determine which data you need to export, such as tickets, contacts, or companies. Freshdesk's API documentation provides endpoints for various data types, so familiarize yourself with the available options.
Write a script to make API requests to Freshdesk. You can use a programming language like Python to send HTTP GET requests to the relevant Freshdesk API endpoint. Include your API key in the request headers for authentication.
Example using Python:
```python
import requests
import json
api_key = 'your_api_key'
domain = 'your_domain'
endpoint = f'https://{domain}.freshdesk.com/api/v2/tickets'
headers = {
'Content-Type': 'application/json',
}
response = requests.get(endpoint, headers=headers, auth=(api_key, 'X'))
data = response.json()
```
Freshdesk API responses may be paginated. Check the API documentation for pagination details and modify your script to loop through pages if necessary. Typically, you will need to use query parameters like 'page' and 'per_page' to navigate through pages.
Extract the fields you need from the API response. The data returned will likely be in JSON format. Parse this JSON to access specific fields you want to include in your CSV file.
Example of extracting data:
```python
tickets = []
for ticket in data:
tickets.append({
'id': ticket['id'],
'subject': ticket['subject'],
'status': ticket['status']
# Add other fields as needed
})
```
Convert the extracted data into CSV format. You can use Python's `csv` module to write the data to a CSV file. Define the columns based on the fields you extracted.
Example of converting to CSV:
```python
import csv
with open('freshdesk_data.csv', mode='w', newline='') as file:
writer = csv.DictWriter(file, fieldnames=['id', 'subject', 'status'])
writer.writeheader()
for ticket in tickets:
writer.writerow(ticket)
```
Execute your script to ensure it runs correctly and outputs the data to a CSV file as expected. Verify the CSV file by opening it in a spreadsheet application to check that the data is accurate and complete. Adjust the script as necessary to handle any issues or errors.
By following these steps, you can successfully move data from Freshdesk to a local CSV file without using third-party connectors or integrations.
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: