AWS Metadata Report Generator
1. Introduction:
The AWS Metadata Report Generator is an automated tool designed to streamline the management of weather or environmental data. It allows users to upload CSV files containing time-series data (typically related to weather parameters like temperature, wind speed, humidity, etc.) and generates comprehensive reports on data availability. These reports ensure that users can easily track which data is complete, which is missing, and identify potential issues in their datasets. Furthermore, the tool organizes and uploads these files to a secure GitHub repository for easy long-term access and organization.
2. How It Works:
Breakdown of how the AWS Metadata Report Generator functions:
Uploading Data:
- The user uploads a CSV file containing data. This file should include a Date column to indicate the time at which each data point was recorded.
Report Generation:
- Once the file is uploaded, the tool processes it by identifying the available and missing data for each station and month.
- It generates a Data Availability Report that highlights the status of the data for each variable and each day in the uploaded dataset.
Storing Data and Reports:
- After the report is generated, the system uploads both the original data file and the generated report to a GitHub repository.
- Files are stored in an organized folder structure based on the station name and year/month for easy access and management.
3. Key Features:
²Automated Report Generation:
Once the file is uploaded, the system automatically generates a detailed data availability report. This report highlights the days where data is available, missing, or incomplete.
²Seamless GitHub Integration:
The system integrates with GitHub, uploading both the original dataset and the generated report in a structured manner, ensuring files are well-organized and easy to retrieve.
²Comprehensive Data Tracking:
The tool tracks data availability for multiple variables (e.g., temperature, wind speed, humidity) and provides detailed insights on the status of each variable, day by day, month by month.
²Clear, Easy-to-Understand Reports:
The reports are generated without complex tables, focusing on providing the user with simple, high-level insights into the data’s completeness. Missing data is clearly flagged, and variables are tracked for availability.
4. Data Availability Report Overview:
The generated Data Availability Report gives the user a summary of the availability of data for the uploaded data set. The report provides insights on:
²Available Data:
o The dates on which data has been successfully recorded and is available for each variable.
o The tool will highlight the periods when data is fully available for each variable, indicating complete coverage.
²Missing Data:
o Days where data is missing or unavailable. Missing data may be identified due to system issues, incomplete sensor readings, or other factors.
o The report also lists the specific variables that have missing data for those days.
²Partial Data:
o The report highlights when data for a particular variable is only partially available (i.e., some readings are missing).
o In such cases, the report indicates how much of the data is available (e.g., "70% available") and which days are impacted.
²Variable Availability:
o For each variable (e.g., outdoor temperature, wind speed, solar radiation), the system tracks its availability over the month. The report will flag whether a variable was fully available, partially available, or missing for any given day or period.
²Month-by-Month Overview:
o The system generates an overview of the data availability for each month, summarizing the status of the data across all days in that month.
5. Benefits of the AWS Metadata Report Generator:
²Time Efficiency:
The tool automates the data processing, eliminating the need for manual inspection of each data point. Users can quickly see the status of their data without diving into complex analysis.
²Data Organization:
By storing data and reports in a structured GitHub repository, users can easily manage their files and keep track of different versions of datasets over time.
²Comprehensive Insights:
The tool offers detailed insights into the data's availability, which helps users identify potential issues early, such as missing data points or variables with inconsistent readings.
²Clear Reporting:
Reports are generated in an easy-to-read format that focuses on availability and missing data. This makes it simpler for users to understand the status of their dataset without needing technical knowledge.
6. GitHub Repository Organization:
The system uploads both the CSV data file and the generated report to a GitHub repository. The files are organized into folders named based on the station and year/month.
²Data Files:
The original uploaded data file is stored under a folder named after the station and the month it corresponds to (e.g., /station_name/2024-01.csv).
²Reports:
The generated metadata report is stored under a metadata_reports folder, named according to the station and the corresponding report period (e.g., /metadata_reports/station_name_metadata_report.csv).
This folder structure ensures that all files are organized in a way that makes it easy to access and manage them later.
7. Conclusion:
The AWS Metadata Report Generator simplifies the process of managing, analyzing, and storing environmental or weather data. With its automated report generation and integration with GitHub, users can quickly assess the availability and quality of their data without spending hours manually inspecting each data point. The clear, organized reports provide valuable insights into the completeness of data, while the GitHub integration ensures that everything is stored securely and is easy to access.
This tool makes it easier for users to manage large datasets and helps them maintain high-quality, reliable data for further analysis.
Note:
Login the vercel using github
Vercel Password : metdata@fect#2024
GitHub token : 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 (don’t pass it through the insecure or social media platforms)
Hosting on Vercel
Vercel is used to host both the frontend (user interface for CSV uploads) and the backend (API for processing data and uploading reports). The platform provides a seamless deployment process with built-in CI/CD, allowing rapid updates and scalability.
Application Structure
I. The application consists of three main components:
II. Frontend : A UI where users upload CSV files.
III. Backend (API Routes): Handles file processing, data validation, and report generation.
IV. GitHub Integration: Uploads processed files and reports to a structured GitHub repository.
How It Works
Step 1: Upload CSV File
I. Users access the frontend via the Vercel-hosted web application.
II. A file input allows users to select and upload a CSV file.
III. The file is sent to the backend for processing.
Step 2: Data Processing & Report Generation
a. The backend API reads the CSV file and checks for:
i. Available data
ii. Missing data
iii. Partially available data
b. A Data Availability Report is generated summarizing the dataset.
c. The report is stored temporarily before being uploaded to GitHub.
Step 3: Upload to GitHub
I. The original CSV and the generated report are pushed to a GitHub repository.
II. Files are organized using a structured naming convention:
III. /station_name/YYYY-MM.csv (original data)
IV. /metadata_reports/station_name_metadata_report.csv (report)
V. The GitHub API is used for authentication and file uploads.
Vercel Setup & Deployment
1. Vercel Configuration
2. Project Repository: Connected to GitHub for automatic deployment.
3. Environment Variables:
I. GITHUB_TOKEN: Used for authenticating with the GitHub API.
II. REPO_OWNER: GitHub repository owner.
III. REPO_NAME: Target GitHub repository.
API Routes (Backend)
I. /api/upload: Handles file uploads and validation.
II. /api/process: Reads CSV data and generates reports.
III. /api/github-upload: Pushes files to GitHub using the API.
Prepared By: Ann Keerthana
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