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Start by logging into your My Hours account. Navigate to the section where you can access your data (such as reports or timesheets). Use the export feature to download the data you need. Typically, this will be in a CSV or Excel format. Save the exported file on your computer.
Open the exported file using a spreadsheet application like Microsoft Excel or Google Sheets. Review the data to ensure all necessary fields are present and correctly formatted. You may need to clean the data by removing any unnecessary columns or rows and correcting any formatting issues.
Familiarize yourself with the data import requirements for Convex. This typically involves understanding the format, field names, and data types that Convex expects. You can refer to any documentation or user manuals provided by Convex for this purpose.
Create a mapping between your My Hours data fields and the required Convex fields. This involves aligning the column names and data formats from your My Hours export to match what Convex uses. You might need to rename columns, change date formats, or convert time units to ensure compatibility.
Using your spreadsheet application, carry out the necessary transformations based on your mapping. This might include formula-based calculations or manual adjustments to align with Convex's requirements. After transforming the data, validate it to ensure accuracy and completeness. Look for discrepancies or errors that need correction.
Log into your Convex account and navigate to the data import section. Follow the instructions provided by Convex to upload your prepared file. This usually involves selecting the file and confirming your mapping is correct. Initiate the import process and wait for it to complete.
After importing, verify that your data has been accurately transferred by comparing sample data in Convex with your original My Hours data. Check for any missing or misaligned data. If discrepancies are found, identify the cause and make necessary adjustments, then repeat the import process if needed.
By following these steps, you can manually transfer data from My Hours to Convex without relying on third-party connectors or integrations, ensuring precision and control over the data migration process.
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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