How to load data from BigQuery to Clickhouse

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

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Set up a BigQuery connector in Airbyte

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

Set up Clickhouse for your extracted BigQuery 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 BigQuery to Clickhouse 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: Extract Data from BigQuery

Begin by extracting the necessary data from BigQuery. This can be done by writing a SQL query in the BigQuery console to select the data you need. Once you have your query, run it and export the results as a CSV or JSON file. You can do this through the BigQuery web interface by selecting the "Export" option after running your query.

After exporting the data from BigQuery, download the CSV or JSON file to your local machine or a server where you have access. Make sure the file is complete and check for any export limits, especially if you're handling large datasets.

Before importing data into ClickHouse, ensure you have a database and table ready to receive the data. Use the ClickHouse client or web interface to create a database and table. Define the table schema according to the structure of your exported data, ensuring data types are compatible.

If not already installed, set up the ClickHouse client on your machine or server. You can download it from the official ClickHouse repository or use a package manager like `apt` for Ubuntu or `yum` for CentOS. This client will be used to execute queries and import data into ClickHouse.

Depending on the data structure differences between BigQuery and ClickHouse, you might need to transform your data. Use scripting languages like Python or Bash to modify CSV or JSON files. This could involve changing data formats, adapting time zones, or modifying field names to match the ClickHouse table schema.

Use the ClickHouse client to import the data. For CSV, you can use the `clickhouse-client` command line tool with the `--query` option to execute an `INSERT INTO` statement that reads from your CSV file. The basic syntax would be:
```bash
clickhouse-client --query="INSERT INTO your_table FORMAT CSV" < your_data.csv
```
Replace `your_table` with your actual table name and `your_data.csv` with your data file path.

After the import process is complete, verify that the data has been correctly transferred by running queries in ClickHouse. Compare a sample of data between BigQuery and ClickHouse to ensure consistency. Check for any discrepancies and address them by re-importing or correcting the data.
By following these steps, you can effectively transfer data from BigQuery to ClickHouse manually without relying on third-party connectors or integrations.