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Begin by logging into your Mailjet account. Navigate to the section where your emails and associated data are stored. Use Mailjet's export feature to download the necessary data, such as email lists, campaign statistics, or logs, in a CSV or Excel format. This is typically done through the Mailjet dashboard under the 'Contacts' or 'Campaigns' sections.
After downloading your data, open the CSV or Excel file using a spreadsheet application like Microsoft Excel or Google Sheets. Review the dataset for any inconsistencies or unnecessary information. Clean the data by removing duplicates, correcting any errors, and ensuring the data structure aligns with your requirements for Starburst Galaxy.
Depending on your Starburst Galaxy configuration, you may need to transform the data format. This might involve converting date formats, changing column headers, or normalizing values to match Starburst's required schema. Use formulas and data manipulation tools within your spreadsheet application to perform these transformations.
Once you've prepared and transformed your data, save it in a compatible format for Starburst Galaxy. Typically, Starburst can ingest data from CSV, JSON, or Parquet files. Choose the format best suited for your data characteristics and Starburst setup, and ensure the file is saved with the correct extension.
Starburst Galaxy can access data stored in cloud storage services like Amazon S3, Google Cloud Storage, or Azure Blob Storage. Upload your prepared data file to a cloud storage bucket you have access to. Ensure the bucket permissions allow Starburst Galaxy to read the data.
Log into your Starburst Galaxy account. Set up a catalog or schema to connect with the cloud storage service where your data is stored. This usually involves specifying the path to the data file and configuring any necessary authentication details, such as access keys or service accounts, to grant Starburst Galaxy access to your data.
Once the data is accessible in Starburst Galaxy, use SQL queries to validate the data import. Check that all fields are correctly imported and that the data integrity is maintained. Perform sample queries to ensure that you can interact with the data as intended. Adjust your setup or data transformation as needed based on the results of your queries.
By following these steps, you can manually transfer and set up your Mailjet data in Starburst Galaxy 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 Mail is an email marketing platform that allows businesses to create, send, and track email campaigns. It offers a user-friendly interface with drag-and-drop tools for designing emails, as well as advanced features such as segmentation, automation, and A/B testing. Mailjet Mail also provides real-time analytics to track the performance of email campaigns, including open rates, click-through rates, and conversion rates. With its robust API, Mailjet Mail can integrate with other marketing tools and platforms, making it a versatile solution for businesses of all sizes. Overall, Mailjet Mail helps businesses to engage with their customers and drive conversions through effective email marketing.
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:
