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Begin by exporting the required data from My Hours. Log in to your My Hours account, navigate to the relevant project or timesheet data, and use the export feature to download the data in CSV format. Ensure that the CSV file is saved to a location on your computer that is easily accessible.
Once you have your CSV file, open it using a spreadsheet application like Microsoft Excel or Google Sheets. Clean and format the data as needed, ensuring there are no empty headers, and that the data types are consistent with what you plan to use in BigQuery. Save the clean CSV file.
If you haven't already, set up a Google Cloud Project. Go to the Google Cloud Console, sign in with your Google account, and create a new project. Make sure to enable billing and set up necessary permissions for accessing BigQuery.
In the Google Cloud Console, navigate to BigQuery. Create a new dataset by clicking on the "Create Dataset" option. Specify the dataset ID, data location, and other optional settings as needed. This dataset will serve as the container for your tables.
Before importing your CSV, define the schema for your BigQuery table. This includes specifying the field names and data types that correspond to the columns in your CSV file. You can do this via the Google Cloud Console by creating a new table and entering schema details manually.
With the dataset and schema in place, you can now upload your CSV file. In BigQuery, navigate to your dataset and select "Create Table." Choose "Upload" as the source, select your CSV file, and configure the table name and schema. Make sure to match the CSV file columns with the schema fields you defined.
After the upload completes, verify that the data has been correctly imported into BigQuery. Run some basic queries to check the data integrity and ensure that all fields have been imported as expected. If there are any discrepancies, you may need to adjust your CSV file or schema and repeat the upload process.
By following these steps, you can successfully move data from My Hours to BigQuery without relying on external 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.
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:
