1. Identify the Data to Export: Decide which tables or data sets you need to export from MySQL.

2. Choose Export Format: BigQuery supports CSV, JSON, Avro, and Parquet formats. Choose a format that suits your needs (CSV is commonly used for simplicity).

3. Export the Data:

    - Connect to your MySQL database using a command-line tool or a database management tool like phpMyAdmin.

    - Use the `mysqldump` command to export your data. For example, to export a table to a CSV file, you can use:

      ```sh

      SELECT * FROM your_table_name

      INTO OUTFILE '/path_to_export/your_table_name.csv'

      FIELDS TERMINATED BY ','

      ENCLOSED BY '"'

      LINES TERMINATED BY '\n';

      ```

    - Make sure that the account used to run the `mysqldump` command has the necessary permissions to access the data and write to the file system.

1. Clean and Format Data: Ensure that the data is clean (e.g., no null bytes, properly escaped newlines, etc.) and conforms to BigQuery’s data types and format requirements.

2. Split Large Files: If you have very large CSV files, consider splitting them into smaller chunks to make the upload process more manageable and to avoid timeouts.

3. Compress Files (Optional): Compress the CSV files using GZIP to reduce upload time and storage costs in BigQuery.

1. Create a Google Cloud Storage Bucket:

    - Go to the Google Cloud Console.

    - Navigate to "Storage" and create a new bucket.

    - Set the storage class and location according to your needs.

2. Upload Files to GCS:

    - Use the `gsutil cp` command to upload your files to the GCS bucket:

      ```sh

      gsutil cp /path_to_export/*.csv gs://your-bucket-name/

      ```

    - Ensure that you have the necessary permissions to upload files to the GCS bucket.

1. Create a Dataset in BigQuery:

    - Go to the BigQuery console.

    - Create a new dataset where you will store your imported tables.

2. Create Table Schema:

    - Define the schema for your BigQuery table, matching the structure of the MySQL data you exported.

    - You can create the schema manually in the BigQuery UI or define it in a JSON file.

3. Load Data from GCS to BigQuery:

    - In the BigQuery UI, navigate to your dataset and click on "Create table".

    - Set the "Create table from" option to "Google Cloud Storage" and provide the path to your CSV files in the bucket.

    - Choose the file format and specify the schema.

    - Configure additional settings like field delimiter, encoding, etc., as per your CSV format.

    - Click on "Create table" to start the import process.

4. Verify Data Import:

    - Once the data is imported, run some queries to ensure that it looks correct and matches the source data in MySQL.

1. Remove Temporary Files:

    - After confirming the successful import, you can delete the exported files from your local system and the GCS bucket to avoid unnecessary storage charges.

2. Monitor Cost and Performance:

    - Check the BigQuery billing and performance to understand the cost implications of the data import and subsequent queries.