How to load data from BigQuery to Postgres destination

Select the Data to Export

• Write a SQL query in BigQuery to select the data you want to export.
• Ensure that the data types in BigQuery are compatible with PostgreSQL data types.

Export to Google Cloud Storage

• Navigate to the BigQuery console.
• Run your query and click on the “Save Results” button.
• Choose “CSV” as the format and select your Google Cloud Storage bucket to export the data.

Download Data from Google Cloud Storage

• Go to the Google Cloud Storage console.
• Find your exported CSV file.
• Click on the file and then click on the “Download” button to save the file locally.

Install PostgreSQL

• If not already installed, download and install PostgreSQL from the official website or use a package manager for your operating system.

Create a Database and Table

• Log in to your PostgreSQL database using a tool like psql or PgAdmin.
• Create a new database or use an existing one.
• Create a table with the appropriate schema to match the data types and structure of the BigQuery data. For example:

‍

CREATE TABLE your_table_name (

    column1 datatype1,

    column2 datatype2,

    ...

);

Convert CSV to PostgreSQL Format

Ensure your CSV file matches the PostgreSQL import format:

• The first line should contain column headers.
• Data should be properly escaped and quoted if necessary.
• Date and time formats should match PostgreSQL’s expected format.

Copy Data to PostgreSQL

Use the COPY command in PostgreSQL to import the data. You can do this from the psql command line or through a SQL execution tool. For example:

COPY your_table_name FROM '/path/to/your/file.csv' DELIMITER ',' CSV HEADER;

If you’re executing the command from a remote location, you might need to use a tool like scp or rsync to transfer the file to a location accessible by the PostgreSQL server.

Verify the Import

• Run a few SELECT queries to ensure that the data has been imported correctly.
• Check for any import errors and make sure the data types have been correctly interpreted.

Remove Temporary Files

• Delete the CSV file from your local machine if it’s no longer needed.
• Optionally, remove the exported data from Google Cloud Storage to avoid unnecessary storage charges.

Check for Consistency

• Perform a thorough check of the data in PostgreSQL to ensure it matches the original data in BigQuery.
• Look for any discrepancies or data integrity issues and address them accordingly.

Step 5: Optimize and Secure the Data Transfer Process

Automate the Process (Optional)

• To automate this process, you can write a script that runs these steps at a scheduled time.
• Make sure to handle errors and exceptions in your script to avoid data inconsistencies.

Secure Data Transfer

• Ensure that the data transfer is secure, especially if the data contains sensitive information.
• Use secure methods to transfer the CSV file and consider encrypting the file before transferring it.

By following these steps, you can move data from BigQuery to PostgreSQL without the need for third-party connectors or integrations. Remember to test the entire process with a small subset of data before attempting to transfer large volumes of data.