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Before you begin, familiarize yourself with Everhour's API documentation. This will help you understand how to authenticate and request data. Ensure you have API access and that you know the endpoints you need to pull the desired data.
Log in to your AWS Management Console and create an S3 bucket where you will store the data from Everhour. Note the bucket name and the region, as you will need this information for configuring your script.
Develop a custom script in a language like Python to authenticate and extract data from Everhour using their API. This script should handle API authentication, send requests to the necessary endpoints, and handle pagination if applicable. Save the extracted data in a format like CSV or JSON.
Install and configure the AWS Command Line Interface (CLI) on your local machine or wherever your script will run. Run `aws configure` to set up your credentials, providing your AWS Access Key, Secret Key, default region, and output format. This allows your script to interact with AWS services.
Modify your extraction script to save the extracted data to a local file and then use the AWS CLI within the script to upload this file to your S3 bucket. Use a command like `aws s3 cp /path/to/local/file s3://your-bucket-name/filename` to perform the upload.
In the AWS Management Console, create an AWS Glue Crawler. This crawler will catalog the data stored in your S3 bucket. Define the data stores, IAM role, and schedule for the crawler to automatically detect and catalog any new data uploaded to your bucket.
Once the data is cataloged, create an AWS Glue ETL job to transform and process the data as needed. Define the script to read from the Data Catalog, perform any necessary transformations, and save the output back to S3 or another data store. Run the job to ensure the data is processed correctly.
By following these steps, you can manually move data from Everhour to AWS S3 and process it using AWS Glue 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.
Everhour is a time tracking and project management tool that helps businesses and teams to manage their time more efficiently. It integrates with popular project management tools like Asana, Trello, and Basecamp, allowing users to track time spent on tasks and projects directly from those platforms. Everhour also offers features like budget tracking, invoicing, and reporting, giving businesses a comprehensive view of their time and project management. With Everhour, teams can easily collaborate, manage their workload, and stay on top of deadlines, ultimately improving productivity and profitability.
Everhour's API provides access to a wide range of data related to time tracking and project management. The following are the categories of data that can be accessed through Everhour's API:
1. Time tracking data: This includes data related to the time spent on tasks, projects, and clients.
2. Project management data: This includes data related to projects, tasks, and subtasks, such as their status, due dates, and assignees.
3. User data: This includes data related to users, such as their name, email address, and role.
4. Billing data: This includes data related to billing, such as the amount billed, the currency used, and the payment status.
5. Reporting data: This includes data related to reports, such as the type of report, the date range, and the data included in the report.
6. Integration data: This includes data related to integrations with other tools, such as the name of the integration, the status, and the configuration settings.
Overall, Everhour's API provides a comprehensive set of data that can be used to track time, manage projects, and analyze performance.
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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