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To move data from Zendesk Talk, begin by reviewing Zendesk's API documentation. Familiarize yourself with the Talk API endpoints, such as those for retrieving call data, recordings, or other relevant information. Ensure your Zendesk account has the necessary API permissions to access this data.
Log into your Google Cloud account and navigate to the Pub/Sub section. Create a new Pub/Sub topic where the Zendesk Talk data will be published. Define any necessary Pub/Sub subscriptions that will consume this data later on.
Use OAuth or API tokens to authenticate your requests to the Zendesk Talk API. Create a script (using a programming language like Python) that leverages this authentication to connect to Zendesk Talk and fetch the required data. Test your script to ensure it retrieves the correct data without errors.
Once data is retrieved, transform it into a JSON format suitable for Google Pub/Sub. This may involve structuring the data to include necessary fields such as timestamps, call details, and metadata. Ensure the data adheres to any specific format requirements of your Pub/Sub subscribers.
Using the Google Cloud Client Libraries, write a function within your script that publishes the transformed JSON data to the previously created Pub/Sub topic. Incorporate error handling to manage potential issues during the publishing process.
Automate the data retrieval and publishing process by scheduling your script to run at regular intervals. Use cron jobs (on Unix/Linux systems) or Task Scheduler (on Windows) to execute the script, ensuring timely updates of Zendesk Talk data to Pub/Sub.
Implement logging within your script to monitor the data transfer process. Log successful data retrievals, transformations, and publishing actions, as well as errors that occur during these steps. Utilize Google Cloud's monitoring tools to track Pub/Sub activity and troubleshoot issues as they arise.
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.
Zendesk Talk is a cloud-based Voice over Internet Protocol (VoIP) system that enables phone communication for customer support teams from within the Zendesk support ticketing solution. Yet another way Zendesk successfully heightens the customer experience, Zendesk Talk offers the capability to access phone numbers in more than 40 countries, making global communication personal.
Zendesk Talk's API provides access to various types of data related to customer support and communication. The categories of data that can be accessed through the API are:
1. Call data: This includes information about incoming and outgoing calls, such as call duration, call recordings, and call transcripts.
2. Agent data: This includes information about agents, such as their availability, status, and performance metrics.
3. Ticket data: This includes information about support tickets, such as ticket status, priority, and customer information.
4. Voicemail data: This includes information about voicemails, such as voicemail transcripts and recordings.
5. Queue data: This includes information about call queues, such as queue status, wait times, and queue metrics.
6. Call routing data: This includes information about call routing, such as routing rules, routing history, and routing performance metrics.
7. IVR data: This includes information about IVR (Interactive Voice Response) systems, such as IVR menus, IVR prompts, and IVR performance metrics.
Overall, Zendesk Talk's API provides a comprehensive set of data that can be used to analyze and improve customer support and communication processes.
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:





