Use Kafka to stream data to Spark for real-time monitoring. The data will be incoming flow data (e.g., network traffic, sensor data).
Data Preprocessing:
The Spark stream should handle necessary data preprocessing operations:
Centering the data (e.g., standardization or normalization).
Handling missing values using fillna or similar techniques.
SageMaker Integration:
Once the data is processed, send it to AWS SageMaker endpoint for prediction. This model should make real-time predictions based on the incoming data.
After predictions are made, the results should be pushed back into Hive for historical data storage.
Data Storage:
Hive will act as the data warehouse for storing the historical prediction results.
HBase will be used to store the real-time predictions and serve as a fast, low-latency storage layer to provide real-time data for front-end dashboards.
Model Training:
Every time the Hive data warehouse reaches a certain threshold, trigger the model retraining process using SageMaker pipelines.
The XXL-Jobs framework will periodically check the size of the data warehouse and trigger the SageMaker pipeline to retrain the model based on new data.
Dashboard Integration:
The real-time predictions stored in HBase will be used to provide immediate updates to the front-end dashboard. This enables live data visualization for the end users
