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Begin by exporting your data from My Hours. Log into your My Hours account and navigate to the reports or data export section. Use the available export options to download your data in a suitable format like CSV or Excel. This data will serve as the source for your data transfer process.
Once you've exported the data, inspect it to ensure it's clean and organized. You may need to remove any unnecessary columns or rows, and ensure consistent formatting. This step is crucial to avoid errors during the data upload to S3 and subsequent processing.
Log in to your AWS Management Console and navigate to the S3 service. Create a new S3 bucket (if you don’t already have one) where you will store the exported data. Name your bucket according to AWS naming conventions and choose a region closest to your location for better performance.
Use the AWS Management Console to upload your prepared data file to the S3 bucket. Navigate to the bucket you created, select 'Upload,' and follow the prompts to upload your CSV or Excel file. Ensure that the correct permissions are set so that AWS Glue can access the file.
Create an IAM role that AWS Glue will use to access the S3 bucket. In the IAM console, create a new role with the 'AWS Glue' service as the trusted entity. Attach the 'AmazonS3ReadOnlyAccess' policy to this role, allowing Glue to read from your S3 bucket.
In the AWS Glue Console, set up a new Glue Crawler. Configure the crawler to use the IAM role you created, and specify your S3 bucket as the data source. The crawler will scan your data files, infer the schema, and create a table in the AWS Glue Data Catalog, which you can use for data processing jobs.
Run the Glue Crawler to populate the AWS Glue Data Catalog with your data’s schema. Once the crawler completes, navigate to the Data Catalog in the AWS Glue Console to verify that the table was created correctly. You can now use AWS Glue jobs to transform and analyze your data as needed.
By following these steps, you'll successfully move your data from My Hours to S3 and set it up for processing with AWS Glue, all without the need for 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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