How to load data from Zendesk Sunshine to BigQuery

Learn how to use Airbyte to synchronize your Zendesk Sunshine data into BigQuery within minutes.

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Zendesk Sunshine connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up BigQuery for your extracted Zendesk Sunshine data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Zendesk Sunshine to BigQuery in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
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How to Sync to Manually

Step 1: Understand Zendesk Sunshine API

Begin by familiarizing yourself with the Zendesk Sunshine API. Review the API documentation to understand how to authenticate, make requests, and retrieve the data you need. Pay attention to rate limits and pagination, which will affect how you extract data.

Configure your environment to authenticate API requests to Zendesk Sunshine. This typically involves generating an API token in the Zendesk admin interface and using basic authentication with your email and the token in your API requests.

Write a script (using a language such as Python, JavaScript, or Ruby) to extract data from Zendesk Sunshine using the API. Make GET requests to the relevant endpoints to retrieve the data you need, handling pagination if necessary. Store the extracted data in a structured format such as JSON or CSV.

Once you have your data, transform it into a format compatible with BigQuery. Ensure that your data types align with BigQuery's supported data types. You may need to flatten nested JSON objects or convert date formats to ensure compatibility.

In the Google Cloud Console, create a new dataset and table in BigQuery to hold your Zendesk data. Define the schema for the table based on the transformed data structure, specifying field names and types that match your transformed data.

Use the `bq` command-line tool or Google Cloud client libraries to load your transformed data into BigQuery. If using the command-line tool, the command might look like:
```bash
bq load --source_format=NEWLINE_DELIMITED_JSON .


```
Ensure that your data file is accessible, and the schema matches the table schema in BigQuery.

After loading, run queries in BigQuery to verify that the data has been imported correctly. Check for any discrepancies or errors. Once verified, automate the extraction, transformation, and loading (ETL) process using a scheduling tool like cron jobs or by implementing a script with a loop and delay to run periodically.

By following these steps, you can move data from Zendesk Sunshine to BigQuery without relying on third-party connectors, maintaining control over the entire ETL process.