Complete Solution System Architecture

Complete Solution System Architecture

Overview

• The document outlines the architecture of a complete solution system, particularly focusing on the backend operations involved in data processing.

Components of the System Architecture

User Interaction

• Web Portal

  • Function: Allows users to upload datasets through a user-friendly interface.

  • Functionality: User uploads their dataset via the web portal.

Data Management

• API Data Management

  • Function: Handles all data requests and responses from the front end to the backend.

  • Mechanism: - A backend API oversees the management and orchestration of data transfers.

Processing Framework

• Function-Based Processing

  • Utilization: Azure Functions are employed to trigger processing jobs based on the incoming data requests.

Photogrammetry Processing

• Photogrammetry Output

  • Function: The photogrammetry engine is designated to generate outputs post data processing.

Data Storage Solutions

• Azure Blob Processed Storage

  • Function: Processed data is securely stored within Azure Blob Storage for further retrieval and analysis.

  • This is crucial for maintaining integrity and availability of data after processing.

• Azure Blob Raw Storage

  • Function: Raw data inputs from the users are also stored in Azure Blob Storage, ensuring that original datasets can be preserved and accessed as necessary.

Application Services

• Azure App Service Processing

  • Function: Azure App Service plays a pivotal role in processing uploaded data.

  • It supports the functional requirements needed for the business logic and execution of application tasks.

Batch Processing and Analytics

• Azure Batch/AKS

  • Function: Utilizes GPU resources either via Azure Batch or Azure Kubernetes Service (AKS) to support heavy computation tasks that are needed for data processing needs.

  • Critical for scaling and optimizing resource use based on processing demands.

Insights and Analytics

• Agricultural Analytics Engine

  • Function: This component processes insights and analytical data derived from the processed datasets.

  • It helps in deriving valuable analytical results relevant to agriculture.

Result Storage

• SQL and Blob Storage

  • Storage Mechanism: Results from the processing are stored in both the SQL database as well as in the processed Blob Storage.

  • This dual storage approach enables efficient querying and retrieval for further analysis or reporting.

This architecture ensures a robust framework for handling, processing, and delivering data insights effectively, leveraging cloud resources for efficiency and scalability.