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Begin by exporting the data from My Hours. This can typically be done by accessing the export feature within the My Hours platform. Choose the most comprehensive format available, such as CSV or Excel, to ensure all necessary data is included. Save this file securely on your local machine.
Open the exported file and review its contents. Cleanse your data by removing any unnecessary columns or correcting any discrepancies to ensure data consistency. This step helps prevent errors during the import process to TiDB.
If you haven't already, set up your TiDB environment. This involves installing TiDB on your server or using a cloud-based TiDB service. Ensure that your TiDB cluster is running and accessible. Create a database and tables within TiDB that correspond to the structure of your data from My Hours.
Convert your cleaned data into SQL statements. This involves creating `INSERT` statements for each row of data in your CSV or Excel file. Ensure that these statements match the schema of the tables you created in your TiDB database.
Using a command-line tool like `mysql` or a GUI tool like DBeaver that can connect directly to TiDB, establish a connection to your TiDB database. Ensure you have the necessary credentials and network permissions to access the database.
Execute the SQL `INSERT` statements generated in the previous step within your connected TiDB environment. This can be done by executing the SQL script file through the command line or by running the scripts directly in your SQL client.
After loading the data, verify that the migration was successful. Run queries to compare the number of records and check for consistency between the data in My Hours and the data now stored in TiDB. Make sure no data is missing or incorrectly loaded.
By following these steps meticulously, you can successfully transfer your data from My Hours to TiDB.
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.
My Hours was launched back in 2002 and it is a cloud-based time-tracking solution best suited for small teams and freelancers. Since then My Hours has been rewritten twice to meet the growing demands and it is a product of Spica, a company headquartered in Ljubljana with 100+ employees. The users of My Hours can start time tracking on unlimited projects and tasks in seconds which easily generates insightful reports and create invoices.
My Hours' API provides access to a variety of data related to time tracking and project management. The following are the categories of data that can be accessed through the API:
1. Time tracking data: This includes information about the time spent on tasks, projects, and clients. It includes start and end times, duration, and any notes or comments associated with the time entry.
2. Project data: This includes information about the projects being worked on, such as project name, description, status, and associated tasks.
3. Task data: This includes information about the individual tasks within a project, such as task name, description, status, and associated time entries.
4. Client data: This includes information about the clients being worked with, such as client name, contact information, and associated projects.
5. User data: This includes information about the users of the My Hours platform, such as user name, email address, and associated time entries, projects, and tasks.
Overall, the My Hours API provides a comprehensive set of data that can be used to analyze and optimize time tracking and project management processes.
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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