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Begin by logging into your MailerLite account. Navigate to the dashboard and locate the export feature for your subscribers, campaigns, or any data you wish to transfer. Export the data as a CSV or Excel file, ensuring that the export includes all necessary fields such as email addresses, names, and any custom fields you have set up.
Open the exported file in a spreadsheet application like Microsoft Excel or Google Sheets. Review the data for any inconsistencies or formatting issues. Ensure that the column headers are correctly named, as these will become the field names in BigQuery. Save the cleaned file as a CSV, which is compatible with BigQuery.
Access the Google Cloud Console (console.cloud.google.com) and create a new project if you haven't already. This project will host your BigQuery datasets. Remember the Project ID, as you'll need it later for accessing BigQuery.
Within the Google Cloud Console, navigate to BigQuery. Create a new dataset where you will store your MailerLite data. Name the dataset appropriately to reflect the data it will contain. This step organizes your data within BigQuery and prepares it for table creation.
Go to the Google Cloud Console and access the Storage section. Create a new bucket or use an existing one to upload your CSV file. Ensure the file is in the correct format and accessible from your BigQuery project. Note the bucket name and the file path, as you will need these to load data into BigQuery.
In the BigQuery section of the Google Cloud Console, create a new table within the dataset you previously set up. Choose the option to create a table from Google Cloud Storage. Input the path to your CSV file in the format `gs://[BUCKET_NAME]/[FILE_NAME].csv`. Configure the schema to match the columns of your CSV file, either manually or using the auto-detect feature.
Once the data is loaded, examine your new table to ensure that all data has been accurately imported. Run sample queries to test the integrity and accessibility of your data. This verification step confirms that your data transfer process was successful and your data is ready for analysis.
By following these steps, you can efficiently move data from MailerLite to BigQuery without relying on any 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.
MailerLite is an intuitive email marketing solution for people of all skill levels. Simplicity is the core principle behind our solutions. We provide drag-and-drop content editors, simplified subscriber management, and advanced automation that are easy to set up. MailerLite is a distributed team of over 130 people living and working in 40 countries. Our international team enables us to better serve our customers around the world.
MailerLite's API provides access to a wide range of data related to email marketing campaigns. The following are the categories of data that can be accessed through MailerLite's API:
1. Subscribers: This category includes data related to subscribers such as their email address, name, location, and subscription status.
2. Campaigns: This category includes data related to email campaigns such as the subject line, content, delivery time, and open and click-through rates.
3. Lists: This category includes data related to email lists such as the name of the list, the number of subscribers, and the date the list was created.
4. Segments: This category includes data related to segments such as the name of the segment, the criteria used to create the segment, and the number of subscribers in the segment.
5. Automation: This category includes data related to automated email campaigns such as the trigger, content, and delivery time.
6. Forms: This category includes data related to forms such as the name of the form, the number of submissions, and the date the form was created.
7. Reports: This category includes data related to email campaign reports such as the number of opens, clicks, bounces, and unsubscribes.
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:
