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Log into your My Hours account using your preferred web browser. Ensure you have the necessary permissions to access and export the data you require.
Once logged in, locate the 'Reports' section within the My Hours interface. This section typically allows you to generate and customize reports based on your tracked data.
In the Reports section, choose the specific data set you wish to export. This could be based on a date range, specific projects, or tasks. Ensure you filter the data to capture exactly what you need in your CSV file.
After selecting the appropriate filters and data set, generate the report. My Hours will process your criteria and display the data in a tabular format. Verify that the data displayed matches your expectations.
Look for an export or download option within the report view. My Hours typically provides the ability to download reports in various formats. Select the CSV format for your download.
Once the CSV file is downloaded, choose a location on your local computer to save the file. Ensure the file is saved with a recognizable name and in a directory that is easy to locate later.
Open the saved CSV file using a spreadsheet application like Microsoft Excel or Google Sheets. Verify that all the data has been correctly exported and formatted. Check for completeness and accuracy to ensure no data is missing or corrupted during the export process.
By following these steps, you can efficiently export and save your data from My Hours to a local CSV file without relying on third-party tools.
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?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
