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Familiarize yourself with the Freshdesk API documentation. Freshdesk offers a RESTful API that allows you to access and extract data such as tickets, contacts, and companies. Identify the endpoints that provide the data you need to migrate.
Freshdesk uses API keys for authentication. Obtain your Freshdesk API key from your Freshdesk account settings. This key will be used to authenticate your API requests. Ensure you have the necessary permissions to access the data you plan to extract.
Write a script or use a command-line tool like `curl` to send HTTP GET requests to the Freshdesk API endpoints. Use your API key in the request headers for authentication. Parse the JSON response to extract the data fields you need.
Once you have extracted the data, transform it into a format suitable for import into Oracle. This might involve converting JSON data to CSV or directly to SQL insert statements, depending on your preference and the size of the data.
Ensure your Oracle database is ready to accept new data. This involves having the necessary tables and columns created that match the data structure from Freshdesk. Use SQL commands to create tables if they do not exist.
Use Oracle's SQLLoader or a custom script to import the prepared data into your Oracle database. SQLLoader allows you to load data from flat files into the Oracle database efficiently. Configure the control file to map your data correctly to the database schema.
After loading the data, verify that the data has been imported correctly. Run SQL queries to compare the data in Oracle with the original data from Freshdesk. Ensure data types, lengths, and values are consistent and address any discrepancies immediately.
By following these steps, you can efficiently transfer data from Freshdesk to an Oracle database without relying on 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:
