How to load data from Zendesk Chat to BigQuery

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

Try Airbyte Cloud Manual method (the hard way)

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Zendesk Chat 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 Chat 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 Chat 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: Export Data from Zendesk Chat

Start by exporting the data you need from Zendesk Chat. You can typically export chat data by accessing the chat dashboard or admin center in Zendesk. Look for options to download chat logs or transcripts in a CSV or JSON format. Ensure that you have the necessary permissions to perform this export.

Step 2: Prepare Data for BigQuery

Once you've exported the data, inspect the file(s) to understand the structure. Clean and format the data to match the schema you plan to use in BigQuery. This may involve transforming data types, renaming columns, or removing unnecessary fields. Use tools like Excel, Google Sheets, or a script in Python or another language to prepare the data.

Step 3: Set Up Google Cloud Project

If you haven't already, set up a Google Cloud Platform (GCP) project. Go to the Google Cloud Console, create a new project, and enable billing. This project will be where your BigQuery instance resides. Ensure you have the necessary permissions to create and manage BigQuery resources.

Step 4: Create a BigQuery Dataset

In the Google Cloud Console, navigate to the BigQuery section. Create a new dataset within your project. A dataset is a container that holds your tables. Choose a suitable dataset ID and set the data location (e.g., US or EU) according to your needs. This dataset will store the chat data.

Step 5: Set Up Google Cloud Storage (GCS) Bucket

Create a Google Cloud Storage bucket to temporarily store your data files. In the Google Cloud Console, navigate to the Cloud Storage section and create a new bucket with a unique name. Upload your prepared CSV or JSON file(s) to this bucket. This step ensures that the data is accessible to BigQuery for importing.

Step 6: Load Data into BigQuery

Use the BigQuery Console or the bq command-line tool to load your data from the GCS bucket into BigQuery. In the BigQuery Console, choose "Create Table" and select "Google Cloud Storage" as the source. Configure the schema based on your prepared data and choose appropriate data types for each column. Execute the load job to create the table and import your data.

Step 7: Verify and Query Data in BigQuery

After loading the data, verify that the import was successful by checking the table in BigQuery. Run some sample queries to ensure the data is structured correctly and accessible. This validation step helps confirm that the data transfer process was successful and your data is now ready for analysis in BigQuery.

By following these steps, you can move data from Zendesk Chat into BigQuery without relying on third-party connectors or integrations.