SIENTIAPDE-1084
Update README.md to enhance the architecture diagrams for both Scouter and FakeData workflows, improving clarity and structure by adopting a consistent flowchart format and detailing service interactions.
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54
README.md
54
README.md
@@ -75,17 +75,12 @@ The **Scouter** workflow is the main entry point for data processing pipelines.
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#### Architecture
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```mermaid
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flowchart TB
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subgraph " "
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A[1. get_last_data_timestamp] --- B[2. load_latest_data] --- C[3. put_last_data_timestamp] --- D[4. core_scouter ⬛]
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end
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subgraph " "
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Redis[(Redis)] ~~~ MongoDB[(MongoDB)] ~~~ Redis2[(Redis)]
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end
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flowchart LR
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A[1. get_last_data_timestamp] --> B[2. load_latest_data] --> C[3. put_last_data_timestamp] --> D[4. core_scouter ⬛]
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A -.-> Redis
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B -.-> MongoDB
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C -.-> Redis2
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A -.-> Redis[(Redis)]
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B -.-> MongoDB[(MongoDB)]
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C -.-> Redis
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```
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### Key Components
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@@ -152,6 +147,27 @@ The **CoreScouter** workflow implements the core data processing pipeline for in
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}
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```
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#### Architecture
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```mermaid
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flowchart TB
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subgraph workflow [" "]
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A[1. get_last_data_timestamp] --> B[2. load_latest_data] --> C[3. put_last_data_timestamp] --> D[4. core_scouter ⬛]
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end
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subgraph services [" "]
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Redis1[(Redis)] ~~~ MongoDB[(MongoDB)] ~~~ Redis2[(Redis)] ~~~ CoreOut[Output]
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end
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A -.-> Redis1
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B -.-> MongoDB
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C -.-> Redis2
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D -.-> CoreOut
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style workflow fill:none,stroke:none
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style services fill:none,stroke:none
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```
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### 3. FakeData Workflow (`fake_data.py`)
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The **FakeData** workflow generates synthetic industrial sensor data for testing and development purposes. It's designed to simulate realistic data flows without requiring actual industrial data sources.
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@@ -184,6 +200,24 @@ The **FakeData** workflow generates synthetic industrial sensor data for testing
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}
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```
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#### Architecture
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```mermaid
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flowchart TB
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subgraph workflow [" "]
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A[1. generate_and_send_data]
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end
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subgraph services [" "]
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Kafka[(Kafka)]
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end
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A -.-> Kafka
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style workflow fill:none,stroke:none
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style services fill:none,stroke:none
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```
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## 📋 Prerequisites
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- Python 3.11+
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