How to load data from MailerSend to BigQuery

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

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a MailerSend 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 MailerSend 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 MailerSend 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: Access MailerSend API

Begin by accessing the MailerSend API. You need to authenticate using your API key, which can be found in your MailerSend account settings. Familiarize yourself with their API documentation to understand the endpoints available for data retrieval.

Use a programming language such as Python, JavaScript, or any language supporting HTTP requests to make a call to the MailerSend API. For instance, using Python, utilize the `requests` library to send a GET request to the relevant API endpoint to extract the data you need, such as email logs or recipient information.

Once you have the data from MailerSend, it might be in JSON format. Transform this data into a format suitable for BigQuery, typically CSV or JSONL (JSON Lines). Make sure the data types and structures match the schema you intend to use in BigQuery for seamless import.

To interact directly with BigQuery, install the Google Cloud SDK on your local machine. This will allow you to use the `bq` command-line tool. Configure the SDK by running `gcloud init` and following the prompts to authenticate and set up your Google Cloud project.

In your Google Cloud Console, create a new dataset in BigQuery where you will store the MailerSend data. Define a table schema that matches the data structure you prepared. You can do this via the console interface or by using SQL commands in the BigQuery UI.

Before importing data into BigQuery, upload the transformed data file (CSV or JSONL) to a Google Cloud Storage (GCS) bucket. Use the `gsutil` command-line tool (part of Google Cloud SDK) to upload your file: `gsutil cp [your-file-path] gs://[your-bucket-name]/`.

Use the `bq` command-line tool to load data from the GCS bucket into your BigQuery table. Execute a command like `bq load --source_format=[CSV/NEWLINE_DELIMITED_JSON] [your-dataset.your-table] gs://[your-bucket-name]/[your-file-name]`. Ensure your command specifies the correct schema if it's a JSON file, or that your CSV matches the table schema.

By following these steps, you can manually extract, transform, and load data from MailerSend into BigQuery without relying on third-party connectors or integrations.