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Begin by reviewing the Freshcaller API documentation. Familiarize yourself with the available endpoints, authentication methods, and data formats. This knowledge is essential for extracting data from Freshcaller effectively.
Create an API key in Freshcaller. Log in to your Freshcaller account, navigate to the Developer section, and generate an API key. Ensure you have the necessary permissions to access the data you intend to transfer.
Write a script in a programming language of your choice (e.g., Python, Node.js) to extract data from Freshcaller. Use the API key to authenticate and fetch the required data. Make HTTP GET requests to the relevant endpoints and store the responses in a structured format like JSON or CSV.
Process the extracted data to fit the format expected by Google Pub/Sub. This may involve transforming JSON objects or CSV rows into a series of messages. Each message should be a JSON object or string that encapsulates a single data unit.
Access the Google Cloud Console and create a new project or use an existing one. Enable the Pub/Sub API for your project. Ensure you have the necessary permissions to publish messages to Pub/Sub topics.
In the Google Cloud Console, navigate to Pub/Sub and create a new topic. This topic will be the destination for your Freshcaller data. Note the topic name, as you’ll need it in your script to publish messages.
Extend your script to include the Google Cloud Client Library for your chosen programming language. Authenticate using a service account key JSON file. Write code to publish the formatted messages to the Pub/Sub topic you created. Ensure each message is successfully published and handle any errors accordingly.
By following these steps, you can manually transfer data from Freshcaller to Google Pub/Sub without relying on third-party 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.
Setup a connection to your Freshcaller site in minutes, and select the Freshcaller collections you want to replicate.
Freshcaller's API provides access to a wide range of data related to call center operations. The following are the categories of data that can be accessed through Freshcaller's API:
1. Call data: This includes information about incoming and outgoing calls, call duration, call recordings, and call transcripts.
2. Agent data: This includes information about agents, such as their availability, status, and performance metrics.
3. Queue data: This includes information about call queues, such as the number of calls waiting, the average wait time, and the number of agents available.
4. IVR data: This includes information about Interactive Voice Response (IVR) systems, such as the number of calls handled by the IVR, the number of calls transferred to agents, and the success rate of the IVR.
5. Ticket data: This includes information about tickets created from calls, such as the status of the ticket, the agent assigned to the ticket, and the resolution time.
6. Analytics data: This includes information about call center performance metrics, such as call volume, call abandonment rate, and average handle time.
Overall, Freshcaller's API provides a comprehensive set of data that can be used to monitor and optimize call center operations.
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?
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