How to load data from Mailjet SMS to BigQuery

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

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Mailjet SMS 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 Mailjet SMS 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 Mailjet SMS 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 Mailjet SMS

Begin by logging into your Mailjet account. Navigate to the SMS section and locate the data you wish to export. Use Mailjet's built-in export functionality to download the data. This can usually be done by selecting the data and choosing an 'Export' option, which typically provides a CSV file.

Once you have the CSV file, open it using a spreadsheet tool like Excel or Google Sheets. Ensure that the data is clean and formatted correctly for BigQuery. This means checking for consistent data types in each column, removing any unnecessary headers, and ensuring there are no empty rows or columns.

If you haven't already, go to the Google Cloud Console and create a new project. This project will serve as the space where your BigQuery data warehouse will reside. Make sure to enable billing for your project, as it's required to use BigQuery.

Within your Google Cloud project, navigate to BigQuery. Create a new dataset by clicking on the "Create Dataset" button. Choose a name for your dataset and configure any necessary settings, such as data location and expiration. This dataset will store your tables.

Inside the dataset you created, set up a new table that matches the schema of your CSV file. Click on "Create Table," and in the source section, select "Create table from" and choose "Empty table." Define the table schema manually by adding fields that correspond to the columns in your CSV file, specifying the appropriate data types (e.g., STRING, INTEGER, TIMESTAMP).

Go back to BigQuery and select the dataset and table you created. Click on "Create Table" again, but this time, choose "Upload" as the source and select your formatted CSV file. Ensure the schema matches what you've set up for the table. Use the "Write preference" option to choose whether to append or overwrite existing data. Initiate the upload process and let BigQuery handle the data import.

Once the upload is complete, verify that the data has been imported correctly by running a few queries in BigQuery. Use simple SQL queries to check the data integrity and ensure that all fields are populated as expected. This step ensures that the data migration was successful and that your data is ready for analysis or further processing.

By following these steps, you can effectively move data from Mailjet SMS to BigQuery without relying on third-party connectors or integrations.