Software Architecture Design Process
+February 12, 2025
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- Data Architecture First +
- Error-Resilient/Security Architecture First +
- Practice Questions +
Why prioritize Data Pipeline before Software Architecture?
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- Data is the foundation of decision-making and AI models +
- Software architecture depends on well-structured data flows +
- Scalable data pipelines prevent downstream bottlenecks +
- Ensuring high-quality data improves system reliability +
How Data Pipeline Architecture improve scalability?
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- Supports distributed storage and processing +
- Enables real-time and batch data handling +
- Reduces dependency on monolithic systems +
- Allows independent scaling of data and compute layers +
Why good data flow critical before software architecture?
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- Clean, structured data prevents application inefficiencies +
- Ensures smooth integration across multiple services +
- Reduces rework caused by poor data management +
- Provides a single source of truth for all applications +
Data Pipeline
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+ + + Raw Sensor Data | Device + +----+----------+------+---------+ + | id | sensor | water_hydration | + +----+----------+----------------+ + | 1 | SensorA | 45 | + +----+----------+----------------+ + + +----+----------+------+---------+ + | id | sensor | water_hydration | + +----+----------+----------------+ + | 2 | SensorB | 38 | + +----+----------+----------------+ + + +----+----------+------+---------+ + | id | sensor | water_hydration | + +----+----------+----------------+ + | 3 | SensorC | 40 | + +----+----------+----------------+ + ++ +
+
+ Data Lake
+ +----+----------+-----------+
+ | id | raw_data | timestamp |
+ +----+----------+-----------+
+ | 1 | {JSON} | 12:01:30 |
+ | 2 | {JSON} | 12:02:10 |
+ | 3 | {JSON} | 12:02:10 |
+ | 1 | {JSON} | 09:08:10 |
+ | 2 | {JSON} | 11:10:10 |
+ | 3 | {JSON} | 01:06:10 |
+ | 1 | {JSON} | 04:05:10 |
+ | 2 | {JSON} | 12:04:10 |
+ | 3 | {JSON} | 01:06:10 |
+ | 1 | {JSON} | 04:05:10 |
+ | 2 | {JSON} | 12:04:10 |
+ | 3 | {JSON} | 01:06:10 |
+ | 1 | {JSON} | 04:05:10 |
+ | 2 | {JSON} | 12:04:10 |
+ +----+----------+-----------+
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+ + + Data Warehouse + +----+----------+--------------+ + | id | sensor | avg_hydration | + +----+----------+--------------+ + | 1 | SensorA | 46 | + | 2 | SensorB | 39 | + +----+----------+--------------+ + ++ +
+ + Dashboard + +----------+------------------+ + | Sensor | Avg Hydration (%)| + +----------+------------------+ + | SensorA | 46 | + | SensorB | 39 | + +----------+------------------+ + ++
Database Pipeline
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+ + + Raw Sensor Data | SQLite + ++ +
+ + Data Lake | MongoDB + ++ + +
+ + Data Warehouse | MySQL + ++ + +
+ + Dashboard | IndexedDB + ++
Caching vs Persisting
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+ + + Sensor Cache | SQLite + ++ +
+ + Lake Persisted | MongoDB + ++ +
+ + Warehouse Cache | MySQL + ++ +
+ + Dashboard Cache | IndexedDB + ++
Full Loading
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- Loads all data at once +
- Higher initial load time +
- Useful for smaller datasets or when all data is needed +
Partial Loading
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- Loads only a subset of data +
- Faster initial load time +
- Useful for large datasets or when only a part of data is required +
Full Loading and/or Partial Loading
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+ + + Sensor Cache | SQLite + ++ +
+ + Lake Persisted | MongoDB + ++ +
+ + Warehouse Cache | MySQL + ++ +
+ + Dashboard Cache | IndexedDB + ++
Loading Triggers
+ +| Trigger Type | +Description | +
|---|---|
| Time-based | +Data is loaded at specific time intervals (e.g., every 5 minutes) | +
| Event-based | +Data is loaded when a specific event occurs (e.g., user action, system change, value + changed) | +
+ Relevant NIST Publication
+ NIST IR 8259A: A + Profile for IoT Security +Error Handling in IoT Systems (Architecture Perspective)
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- Decouple error handling from business logic using middleware layers +
- Event-driven architecture for detecting errors in real-time +
- Use of centralized logging services for error monitoring +
- Fallback mechanisms for fault tolerance and service continuity +
Error Logs and Business Logic Components
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+ + + Business Logic + +------------------------+ + | Handle IoT device | + | interactions, data | + | processing, and | + | communication | + +------------------------+ + ++ +
+ + Error Logs + +------------------------+ + | Logs errors from | + | Business Logic | + | component, including | + | device failure, | + | performance issues, | + | and system faults | + +------------------------+ + ++
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+ Monitoring Infrastructure in IoT Systems (Architecture Perspective)
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- Distributed monitoring across IoT devices via a central control plane +
- Use of microservices for collecting and processing device health data +
- Integration with cloud-based IoT platforms (e.g., AWS IoT, Azure IoT Hub) +
- Real-time streaming analytics and alert systems for device performance +
+ Diagnostics in IoT Systems (Architecture Perspective)
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- Distributed diagnostics using edge devices for localized analysis +
- Automated diagnosis with AI/ML models at the edge or cloud +
- Centralized reporting and troubleshooting workflows +
- Data-driven insights for root cause analysis of system failures +
+ Over-the-Air (OTA) Updates in IoT Systems (Architecture Perspective)
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- Modular firmware design for seamless OTA updates +
- Integration of OTA update pipelines within CI/CD systems +
- Secure transmission of updates with encryption and integrity checks +
- Version control and rollback mechanisms for devices +
+ Review Architecture Design Principles
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- Data Architecture First +
- Security and Privacy +
- Data Integrity +
- Feature Toggle +
- Monolithic Architecture +
- Layered Architecture +