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Begin by logging into your My Hours account. Navigate to the reports or data export section where you can download your data. Choose the desired data range and format, typically CSV or JSON, since these formats are commonly supported and easy to manipulate. Download the exported file to your local system.
If you haven't already, set up a Typesense server. You can do this by following the installation guide on the Typesense website. Installation can be done via Docker, binary files, or package managers depending on your operating system. Ensure the server is running and accessible.
Open the exported file from My Hours using a text editor or spreadsheet software. Examine the structure of the data, noting the field names and data types. This understanding will help you map the data correctly into Typesense's schema.
Create a schema for a Typesense collection that matches the structure of your My Hours data. This involves defining field names, data types, and any indexing or sorting options. Use Typesense’s API to create the collection with this schema on your running server.
Write a script in a programming language of your choice (such as Python) to read the exported My Hours file. Transform the data into the format required by Typesense, ensuring it matches the schema you defined. This might involve converting data types or adjusting field names.
Use the Typesense API to import the transformed data into your newly created collection. This typically involves sending HTTP POST requests with your data in JSON format to the appropriate endpoint on your Typesense server.
After loading the data, perform checks to ensure everything was imported correctly. Use the Typesense search and retrieval functionalities to query the data and verify that it matches what was exported from My Hours. Make adjustments to the data or schema if necessary.
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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