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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.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Mailjet is one of the affordable software for email marketing campaigns SMS campaigns, newsletter creation, email template building etc. Mailjet permits you to send transactional SMS messages using our Send SMS API. The Mailjet Transactional SMS API offers a straight-forward way to add SMS functionalities to third-party applications. Mailjet's SMS API allows you to send text messages to users around the globe through a simple RESTful API.
Mailjet SMS's API provides access to various types of data related to SMS messaging. The categories of data that can be accessed through the API are as follows:
1. Account data: This includes information about the user's Mailjet SMS account, such as account ID, API key, and account balance.
2. Message data: This includes details about the SMS messages sent and received through the Mailjet SMS platform, such as message ID, sender ID, recipient number, message content, and delivery status.
3. Contact data: This includes information about the contacts or recipients of SMS messages, such as contact ID, phone number, and contact attributes.
4. Campaign data: This includes data related to SMS campaigns, such as campaign ID, campaign name, and campaign statistics.
5. Analytics data: This includes data related to SMS message performance, such as delivery rates, open rates, click-through rates, and conversion rates.
6. Integration data: This includes data related to the integration of Mailjet SMS with other platforms or applications, such as integration ID, integration type, and integration status.
Overall, Mailjet SMS's API provides comprehensive access to data related to SMS messaging, enabling users to track and optimize their SMS campaigns for maximum effectiveness.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
