How to load data from ConfigCat to BigQuery

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

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a ConfigCat 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 ConfigCat 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 ConfigCat 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 Your Data

First, familiarize yourself with the data structure and format in ConfigCat. Identify the specific data you want to export, such as feature flags, configurations, or other relevant information. This understanding will help you in creating the appropriate extraction scripts.

ConfigCat provides an API that allows you to programmatically access your data. Obtain your API key from ConfigCat's dashboard and review the API documentation to understand how to authenticate and make requests to fetch the required data.

Develop a script using a programming language like Python, Node.js, or Ruby to connect to the ConfigCat API. Use the API key to authenticate and fetch the data. Ensure the script can handle pagination if the data set is large. Save the extracted data in a structured format such as JSON or CSV.

Transform the extracted data to match the schema required by BigQuery. This might involve reformatting JSON objects or converting data fields to match BigQuery data types. Write a script to automate the transformation process, ensuring consistency and accuracy.

Install and configure the Google Cloud SDK on your local machine or server. Authenticate with your Google Cloud account using `gcloud auth login` and set the appropriate project with `gcloud config set project [YOUR_PROJECT_ID]`. This setup is crucial for uploading data to BigQuery.

Before importing into BigQuery, you’ll need to upload your data to Google Cloud Storage (GCS). Use the `gsutil cp` command to transfer your transformed data files to a GCS bucket. Ensure the bucket is in the same region as your BigQuery dataset for optimal performance.

Use the BigQuery command-line tool or the BigQuery web UI to load the data from Google Cloud Storage into BigQuery. Define the dataset and table schema if they do not exist. Use a command like `bq load --source_format=NEWLINE_DELIMITED_JSON [DATASET_NAME].[TABLE_NAME] gs://[BUCKET_NAME]/[FILE_NAME]` to import the data. Verify that the data has been correctly imported by querying the table in BigQuery.

By following these steps, you can efficiently move data from ConfigCat to BigQuery manually, without relying on third-party connectors or integrations.