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Begin by obtaining access to the Freshdesk API. Log into your Freshdesk account, navigate to Admin settings, and locate the API settings. Generate an API key which will be used to authenticate your requests. This key will allow you to programmatically access Freshdesk data such as tickets, contacts, and conversations.
Clearly define which data you need to export from Freshdesk. This could include tickets, customer information, or interaction history. Understanding your data requirements will help in writing precise API queries and ensure that you only export necessary data.
Create a script in a programming language like Python to extract data from Freshdesk. Use libraries such as `requests` to make HTTP GET requests to Freshdesk's API endpoints. Incorporate pagination handling since Freshdesk API may limit the number of records returned in a single call. Parse the JSON responses and store them in a structured format like dictionaries or lists.
Install and configure Elasticsearch on your local machine or server. Download the latest version from the official Elasticsearch website and follow the installation instructions for your operating system. Once installed, configure Elasticsearch by editing the `elasticsearch.yml` file to suit your environment, such as adjusting network settings or cluster configurations.
Transform the extracted data into a format suitable for Elasticsearch. This typically involves converting your data into JSON documents, ensuring that each document has a unique identifier. You may also need to map Freshdesk fields to Elasticsearch fields, ensuring that data types align correctly (e.g., date formats, text fields).
Create another script to ingest data into Elasticsearch. Use a library like `elasticsearch-py` in Python to interact with the Elasticsearch API. Implement bulk operations to efficiently upload data, especially if you have a large dataset. Ensure your script handles errors and retries failed operations to maintain data integrity.
After ingestion, verify that all data has been correctly transferred from Freshdesk to Elasticsearch. Perform sample queries using Elasticsearch�s Query DSL to ensure data is searchable and correctly indexed. Compare a subset of data in Elasticsearch against the original Freshdesk data to validate accuracy. Adjust your scripts as necessary based on the testing results.
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:
