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Start by accessing the Zendesk Talk API to extract the data you need. You will need to authenticate using an API token or OAuth. Use the Zendesk Talk API endpoints to fetch call data, such as call records, voicemails, or other relevant metrics. You can perform these API requests with tools like curl or a custom script in Python, JavaScript, or another language of your choice.
Once you have the raw data from Zendesk Talk, transform it into a structured format such as CSV, JSON, or Parquet. This step is crucial for organizing the data into a schema that can be easily ingested by Databricks. Write a script to parse the JSON responses from the API and convert them into your desired format. Ensure the data is cleaned and validated during this process.
Since Databricks Lakehouse can read data from cloud storage, you need to export your structured data files to either AWS S3 or Azure Blob Storage. If you're using AWS, create an S3 bucket and ensure you have the right permissions to upload data. For Azure, set up a blob storage container and obtain the necessary access keys or SAS tokens.
Use command-line tools like `aws s3 cp` for AWS S3 or Azure CLI for Blob Storage to upload your transformed data files to the cloud storage. Ensure the files are stored in a location and with permissions that Databricks Lakehouse can access. Verify the upload by listing the contents of the storage location.
Set up a Databricks cluster that will process your data. Choose the appropriate cluster configuration based on your data size and processing requirements. Make sure to attach the necessary libraries for reading data from your chosen cloud storage if they are not included by default.
Use Databricks notebooks to read the data files from your cloud storage into a Databricks DataFrame. Use Spark's built-in functionality to load CSV, JSON, or Parquet files. For example, use `spark.read.csv("s3://your-bucket/your-file.csv")` for S3 or `spark.read.csv("wasbs://your-container@your-account.blob.core.windows.net/your-file.csv")` for Azure Blob Storage.
After loading the data into Databricks, perform any necessary transformations or validations. Once verified, write the data to a table within Databricks Lakehouse using Spark SQL or DataFrame API. Use commands like `dataFrame.write.format("delta").saveAsTable("your_table_name")` to persist the data in Delta Lake format, allowing for efficient queries and analysis.
This guide provides a direct method for transferring data from Zendesk Talk to Databricks Lakehouse using API calls and cloud storage, without relying on additional third-party connectors.
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:
