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Start by logging into your My Hours account. Navigate to the reports or data export section and choose the data set you wish to export. Typically, My Hours will allow you to export data in CSV or Excel format. Download the file to your local machine.
Open the exported file in a spreadsheet application like Microsoft Excel or Google Sheets. Review the data to ensure all necessary fields are present and clean up any inconsistencies or errors. Ensure that your data fields match the schema you plan to use in Weaviate.
If not already installed, set up Weaviate on your local machine or server. You can do this by following the installation instructions provided on the Weaviate website or GitHub repository. You may choose to use Docker for a straightforward setup.
Access the Weaviate dashboard or use the Weaviate API to define your schema. This includes creating classes and properties that match the structure of your data from My Hours. Ensure that the data types in Weaviate correspond to those in your data file.
Convert your cleaned CSV or Excel data into JSON format, which is required for importing into Weaviate. You can use a script written in Python, JavaScript, or another programming language to automate this conversion. Each row in your CSV should be represented as a JSON object.
With your JSON data ready, use the Weaviate RESTful API to import the data into your Weaviate instance. This involves making HTTP POST requests to the appropriate Weaviate endpoints, typically the `/objects` endpoint, to create new objects as per your defined schema.
Once the data is imported, verify the success of the operation by querying the Weaviate database. You can use either the Weaviate console or API to perform search queries and ensure that your data is correctly stored and retrievable. Check for any errors or missing entries and re-import if necessary.
By following these steps, you can successfully move your data from My Hours to Weaviate without relying on third-party 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:
