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Begin by exporting your data from My Hours. Log into your My Hours account and navigate to the section where you can export data. Typically, this involves accessing the "Reports" or "Data Export" functionality. Choose the data you wish to export and select a suitable format (e.g., CSV, Excel). Download the file to your local system.
Open the exported file to ensure the data is structured correctly. Clean the data if necessary, removing any unwanted columns or rows and ensuring consistent formatting. Save your cleaned data in a CSV format, as it is commonly used and easily processed by Databricks.
If you haven't already, set up an account with Databricks and create a new Lakehouse environment. This involves selecting your cloud provider (AWS, Azure, or Google Cloud Platform) and creating a cluster to process your data. Follow the Databricks setup instructions to configure your environment properly.
Before moving the data into Databricks Lakehouse, you need to upload it to cloud storage that is accessible by Databricks. Depending on your cloud provider, this could be AWS S3, Azure Blob Storage, or Google Cloud Storage. Use the cloud provider’s web interface or CLI to upload your cleaned CSV file to your designated storage bucket or container.
In your Databricks workspace, use the Databricks notebook interface to access the data stored in your cloud storage. You will need to configure your Databricks environment to have the appropriate permissions to read from your cloud storage. Use Spark to read the CSV file into a DataFrame. For example, in a Python notebook, you could use:
```python
df = spark.read.csv('path_to_your_cloud_storage_file', header=True, inferSchema=True)
```
Once the data is in a DataFrame, you can perform any transformations needed to prepare it for analysis and storage in the Lakehouse. This may include data type conversions, renaming columns, or filtering records. After transforming the data, write the DataFrame to a Delta Lake table within your Databricks Lakehouse environment:
```python
df.write.format('delta').mode('overwrite').save('/mnt/datalake/your-delta-table')
```
After loading the data into your Delta Lake table, verify that the data has been loaded correctly by querying the table in Databricks. Check for any discrepancies or errors. Additionally, configure access permissions to ensure that only authorized users can access the data. This may involve setting up role-based access controls (RBAC) within Databricks.
By following these steps, you can successfully move your data from My Hours into Databricks Lakehouse without using any 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?
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