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To begin, you need to export the data from Zendesk Talk. This is typically done via Zendesk's native data export functionality. Navigate to the Zendesk Admin Center, go to the 'Talk' section, and look for the option to export call data. Exports can usually be done in CSV or JSON format. Download the desired dataset to your local machine.
Log in to your AWS Management Console and navigate to the S3 service. Create a new bucket where you will store your Zendesk Talk data. Take note of the bucket name and region, as you will need this information later. Ensure you configure the permissions to allow the necessary access for your data files.
Once your bucket is set up, upload the exported Zendesk Talk data files. You can do this via the AWS Management Console by navigating to your bucket and selecting the ‘Upload’ option. Ensure you maintain the organization of files, especially if you are dealing with multiple datasets or incremental data.
Before AWS Glue can process your data, ensure that the data format within S3 is compatible. AWS Glue natively supports formats like CSV, JSON, Parquet, etc. If your data is not in a supported format, you may need to manually convert it. This can be done using tools like pandas in Python scripts executed on local machines or within AWS Lambda.
In AWS Glue, set up a new Crawler to automatically populate your Glue Data Catalog with tables representing the data structure of your files in S3. Define the S3 location of your data and configure the crawler to point to your new bucket. Run the crawler to create the necessary table schema in the Glue Data Catalog.
With your data cataloged, create an AWS Glue ETL (Extract, Transform, Load) job. This job will allow you to process and transform your data as necessary. Define the source (S3) and target (another S3 bucket or database) within the Glue console, and write the necessary transformation script using Python or Scala.
Execute your AWS Glue job and monitor its progress through the AWS Glue console. You can set triggers to automate the job execution based on time schedules or events. Ensure you have logging enabled to troubleshoot any issues that arise during the data transformation and loading process.
Following these steps, you can successfully move data from Zendesk Talk to S3 and prepare it for AWS Glue processing 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.
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:
