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Begin by logging into your MailerLite account. Navigate to the "Subscribers" section and select the list you wish to export. Use the "Export" option to download the subscriber data as a CSV file. Ensure all necessary data fields are included in the export.
Open the exported CSV file in a spreadsheet editor such as Microsoft Excel or Google Sheets. Review the data to ensure it is clean and structured correctly. Make any necessary adjustments to column headers or data formatting to align with the schema you plan to use in TiDB.
If you haven't already, install TiDB on your local machine or set it up in your desired cloud environment. Follow the official TiDB installation guide to configure your database instance. Ensure that your TiDB server is running and accessible.
Determine the structure of your database in TiDB. Use a MySQL client, as TiDB is compatible with MySQL protocols, to connect to your TiDB instance. Create a new database and define the necessary tables and fields that match the structure of your CSV data using SQL `CREATE TABLE` statements.
Use a script or a simple tool to convert your CSV data into SQL `INSERT` statements. This script can be a custom Python or shell script that reads each row from the CSV and generates the corresponding SQL command. Ensure that the data types in the SQL commands match those defined in your TiDB schema.
Connect to your TiDB instance using a MySQL client. Execute the SQL `INSERT` statements generated from your CSV file to import the data. This can be done by running the SQL script directly from the client or using a batch file to execute all commands at once.
Once the data is imported, perform a series of checks to ensure data integrity. Query the TiDB tables to verify that all data has been transferred correctly and completely. Cross-reference with the original CSV file to ensure no data is missing or misaligned. Adjust any discrepancies as needed.
By following these steps, you can successfully transfer data from MailerLite to TiDB 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.
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?
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