Update tech_docs/airflow_mqtt.md
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### High-Level Overview of the Workflow for Ingesting and Processing Sensor Data
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#### Introduction
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This document provides a high-level overview of a scalable and flexible solution for ingesting sensor data using MQTT, transforming the data with dbt, and running machine learning (ML) workloads for predictive analysis. The workflow is orchestrated using Apache Airflow, which ensures efficient scheduling, monitoring, and management of tasks. This solution is designed to be portable and adaptable to different sensor data payloads, making it suitable for various applications in IoT, smart manufacturing, and beyond.
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### Workflow Components
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1. **MQTT Data Ingestion**
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2. **Data Storage**
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3. **Data Transformation (dbt)**
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4. **Machine Learning Inference**
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5. **Workflow Orchestration (Airflow)**
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6. **Portability and Flexibility**
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#### 1. MQTT Data Ingestion
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**Purpose**: Collect real-time data from sensor devices via MQTT.
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**Capabilities**:
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- **Real-Time Data Collection**: Ingest data continuously from various sensor devices.
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- **Event-Driven Architecture**: Use MQTT's lightweight protocol to handle high-frequency, real-time data streams efficiently.
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- **Scalability**: Easily scale to accommodate an increasing number of devices and data volume.
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#### 2. Data Storage
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**Purpose**: Store the ingested data for further processing and analysis.
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**Capabilities**:
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- **Flexible Storage Solutions**: Use SQLite for simple setups or scale up to databases like PostgreSQL or data warehouses like BigQuery for larger deployments.
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- **Persistent Storage**: Ensure data is stored reliably for subsequent transformation and analysis.
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#### 3. Data Transformation (dbt)
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**Purpose**: Clean, transform, and prepare the raw data for ML inference.
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**Capabilities**:
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- **Modular SQL Transformations**: Organize complex data transformations into manageable and reusable SQL models.
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- **Data Quality Assurance**: Use dbt's built-in testing framework to validate data quality at each transformation step.
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- **Documentation and Version Control**: Automatically generate documentation for data models and use version control to track changes.
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#### 4. Machine Learning Inference
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**Purpose**: Run ML models to make predictions based on the transformed data.
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**Capabilities**:
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- **Pre-Trained Model Integration**: Easily integrate and run pre-trained ML models for inference.
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- **Predictive Analytics**: Generate actionable insights by applying ML algorithms to the prepared data.
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- **Scalable ML Workloads**: Handle increasing data volumes and complexity by leveraging scalable ML infrastructure.
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#### 5. Workflow Orchestration (Airflow)
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**Purpose**: Orchestrate the entire workflow, ensuring each task runs in the correct sequence and at the scheduled time.
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**Capabilities**:
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- **Task Scheduling**: Schedule tasks to run at specific intervals or trigger them based on events.
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- **Dependency Management**: Ensure tasks are executed in the correct order by defining dependencies.
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- **Monitoring and Alerting**: Monitor the status of tasks and workflows, and receive alerts on failures or anomalies.
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- **Portability**: Easily deploy and manage workflows in different environments using Docker and Airflow's portable configuration.
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### Portability and Flexibility
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**Changing Data Payloads**:
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- **Adaptable Data Ingestion**: The MQTT subscriber can be configured to handle different data payloads by adjusting the payload processing logic.
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- **Flexible Data Models**: dbt models can be easily updated to accommodate new data fields or different sensor types without significant restructuring.
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- **Scalable ML Inference**: ML inference scripts can be adapted to use different models or handle new input features as required.
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**Combined Workflow Example**:
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1. **Ingest Data from Multiple Sensors**: Configure the MQTT subscriber to handle data from various sensors (e.g., temperature, humidity, pressure).
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2. **Transform and Aggregate Data**: Use dbt to clean, transform, and aggregate data from different sensors into a unified format.
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3. **Run Combined ML Inference**: Apply ML models to the aggregated data to generate comprehensive insights across all sensors.
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4. **Orchestrate with Airflow**: Define an Airflow DAG to manage the combined workflow, ensuring seamless execution of data ingestion, transformation, and ML inference tasks.
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#### Example Use Case
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Consider a smart factory scenario where various sensors (e.g., temperature, humidity, vibration) are deployed to monitor machinery performance. The combined workflow would:
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1. **Ingest Data**: Continuously collect real-time data from all sensors using MQTT.
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2. **Transform Data**: Use dbt to clean and aggregate data from different sensors, preparing it for analysis.
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3. **Run Inference**: Apply ML models to predict equipment failures or optimize maintenance schedules based on the aggregated sensor data.
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4. **Orchestrate Workflow**: Use Airflow to schedule and manage the entire process, ensuring data is processed in a timely and efficient manner.
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### Conclusion
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This solution provides a robust, scalable, and flexible approach to ingesting, transforming, and analyzing sensor data using MQTT, dbt, and Airflow. By leveraging the strengths of these tools, organizations can gain valuable insights from their sensor data, improve operational efficiency, and drive informed decision-making. The portability and adaptability of this solution make it suitable for a wide range of applications and environments, ensuring it can grow and evolve with the organization's needs.
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---
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### Complete Solution for Ingesting MQTT Sensor Data and Running ML Workloads
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#### Overview
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This solution demonstrates how to build a scalable, end-to-end data pipeline that ingests real-time sensor data using MQTT, processes and transforms the data using dbt, and runs machine learning (ML) inference to derive actionable insights. The entire workflow is orchestrated using Apache Airflow, ensuring efficient scheduling, monitoring, and management of tasks.
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#### Components
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1. **MQTT Data Ingestion**
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2. **Data Storage**
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3. **Data Transformation (dbt)**
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4. **Machine Learning Inference**
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5. **Workflow Orchestration (Airflow)**
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#### Architecture Diagram
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```plaintext
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+---------------------+
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| Sensor Devices |
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+---------+-----------+
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|
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v
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+---------+-----------+
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| MQTT Broker |
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+---------+-----------+
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v
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+---------+-----------+ +-------------------------+
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| Airflow Ingestion +----------> SQLite DB |
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| (Python) | +-------------------------+
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+---------+-----------+
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|
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v
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+---------+-----------+
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| dbt Models |
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+---------+-----------+
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|
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v
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+---------+-----------+
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| ML Inference |
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+---------+-----------+
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|
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v
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+---------+-----------+
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| Airflow Orchestration |
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+-------------------------+
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```
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### Detailed Breakdown
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#### 1. MQTT Data Ingestion
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**Purpose**: Collect real-time data from sensor devices via MQTT and store it in a database.
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**Implementation**:
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- **MQTT Subscriber**:
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- A Python script subscribes to the MQTT broker to receive sensor data.
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- The received data is processed and saved to an SQLite database.
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```python
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import paho.mqtt.client as mqtt
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import json
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import pandas as pd
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from datetime import datetime
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import sqlite3
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def on_message(client, userdata, message):
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payload = json.loads(message.payload.decode())
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process_payload(payload)
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def process_payload(payload):
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df = pd.DataFrame([payload])
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df['timestamp'] = datetime.now()
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conn = sqlite3.connect('/path/to/sensor_data.db')
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df.to_sql('raw_sensor_data', conn, if_exists='append', index=False)
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conn.close()
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def subscribe_to_mqtt():
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client = mqtt.Client()
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client.on_message = on_message
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client.connect("mqtt_broker_host", 1883, 60)
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client.subscribe("sensors/data")
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client.loop_forever()
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if __name__ == "__main__":
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subscribe_to_mqtt()
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```
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#### 2. Data Storage
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**Purpose**: Store the ingested sensor data for further processing and analysis.
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**Implementation**:
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- **SQLite Database**:
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- Data is stored in an SQLite database. For production, consider using a more robust database like PostgreSQL or a data warehouse like BigQuery.
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#### 3. Data Transformation (dbt)
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**Purpose**: Clean, transform, and prepare the raw data for ML inference.
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**Implementation**:
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- **dbt Project Setup**:
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- Initialize a dbt project and configure it to connect to the database.
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- Create dbt models to clean and transform the raw sensor data.
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```sql
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-- models/cleaned_sensor_data.sql
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WITH raw_data AS (
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SELECT
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*
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FROM
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{{ ref('raw_sensor_data') }}
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)
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SELECT
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sensor_id,
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timestamp,
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temperature,
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humidity
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FROM
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raw_data
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WHERE
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temperature IS NOT NULL
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AND humidity IS NOT NULL;
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```
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```sql
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-- models/aggregated_sensor_data.sql
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WITH cleaned_data AS (
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SELECT
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*
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FROM
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{{ ref('cleaned_sensor_data') }}
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)
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SELECT
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sensor_id,
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date_trunc('hour', timestamp) AS hour,
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AVG(temperature) AS avg_temperature,
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AVG(humidity) AS avg_humidity
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FROM
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cleaned_data
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GROUP BY
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sensor_id, date_trunc('hour', timestamp);
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```
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#### 4. Machine Learning Inference
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**Purpose**: Run ML models to make predictions based on the transformed data.
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**Implementation**:
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- **Inference Script**:
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- A Python script loads the transformed data, runs the ML model, and stores the predictions back in the database.
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```python
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import pandas as pd
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import sqlite3
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import joblib
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def load_transformed_data():
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conn = sqlite3.connect('/path/to/sensor_data.db')
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query = "SELECT * FROM aggregated_sensor_data"
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df = pd.read_sql_query(query, conn)
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conn.close()
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return df
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def run_inference(data):
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model = joblib.load('/path/to/your_model.pkl')
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predictions = model.predict(data[['avg_temperature', 'avg_humidity']])
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data['predictions'] = predictions
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return data
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def save_predictions(data):
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conn = sqlite3.connect('/path/to/sensor_data.db')
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data.to_sql('sensor_predictions', conn, if_exists='append', index=False)
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conn.close()
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if __name__ == "__main__":
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data = load_transformed_data()
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predictions = run_inference(data)
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save_predictions(predictions)
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```
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#### 5. Workflow Orchestration (Airflow)
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**Purpose**: Orchestrate the entire workflow, ensuring each task runs in the correct sequence and at the scheduled time.
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**Implementation**:
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- **Airflow DAG**:
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- Define an Airflow DAG to manage the workflow, including tasks for data ingestion, transformation, and ML inference.
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```python
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from airflow import DAG
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from airflow.operators.python_operator import PythonOperator
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from airflow.operators.bash_operator import BashOperator
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from airflow.utils.dates import days_ago
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from datetime import timedelta
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default_args = {
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'owner': 'airflow',
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'depends_on_past': False,
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'email_on_failure': False,
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'email_on_retry': False,
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'retries': 1,
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'retry_delay': timedelta(minutes=5),
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}
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dag = DAG(
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'sensor_data_pipeline',
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default_args=default_args,
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description='A DAG for processing sensor data',
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schedule_interval=timedelta(minutes=10),
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start_date=days_ago(1),
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catchup=False,
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)
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ingest_mqtt_data = PythonOperator(
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task_id='ingest_mqtt_data',
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python_callable=subscribe_to_mqtt,
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dag=dag,
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)
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transform_data = BashOperator(
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task_id='transform_data',
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bash_command='dbt run --profiles-dir /path/to/your/dbt/project',
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dag=dag,
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)
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def run_inference():
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import pandas as pd
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import sqlite3
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import joblib
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def load_transformed_data():
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conn = sqlite3.connect('/path/to/sensor_data.db')
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query = "SELECT * FROM aggregated_sensor_data"
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df = pd.read_sql_query(query, conn)
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conn.close()
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return df
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def make_predictions(data):
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model = joblib.load('/path/to/your_model.pkl')
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predictions = model.predict(data[['avg_temperature', 'avg_humidity']])
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data['predictions'] = predictions
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return data
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def save_predictions(data):
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conn = sqlite3.connect('/path/to/sensor_data.db')
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data.to_sql('sensor_predictions', conn, if_exists='append', index=False)
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conn.close()
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data = load_transformed_data()
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predictions = make_predictions(data)
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save_predictions(predictions)
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ml_inference = PythonOperator(
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task_id='run_inference',
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python_callable=run_inference,
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dag=dag,
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)
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ingest_mqtt_data >> transform_data >> ml_inference
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```
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### Project Structure
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```
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sensor_data_project/
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├── dags/
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│ └── sensor_data_pipeline.py
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├── dbt_project.yml
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├── models/
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│ ├── cleaned_sensor_data.sql
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│ └── aggregated_sensor_data.sql
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├── profiles.yml
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├── scripts/
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│ ├── mqtt_subscriber.py
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│ ├── ml_inference.py
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└── Dockerfile
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```
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### Conclusion
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This solution leverages MQTT for real-time data ingestion, dbt for data transformation, and Airflow for workflow orchestration, creating a robust pipeline that processes sensor data and applies machine learning models to generate actionable insights. The modular design allows for easy scalability and maintenance, making it suitable for a wide range of applications in IoT, smart manufacturing, and beyond.
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---
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### Detailed Orchestration with Airflow
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Orchestration with Airflow involves setting up Directed Acyclic Graphs (DAGs) that define a sequence of tasks to be executed in a specific order. This ensures that each step in the workflow is completed before the next one begins, and it allows for scheduling, monitoring, and managing the data pipeline efficiently.
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