This project aims to establish a system that periodically collects blood sugar data from external sensors, stores, and manages this data in a database. The primary components used for this system include Apache Airflow, Apache Kafka, and PostgreSQL, enabling efficient data processing and storage.
Architecture Components
External API: Blood Sugar Data Provider
This is an external API server that provides blood sugar data. The API returns blood sugar data collected from sensors in JSON format.
Apache Airflow: Workflow Management
Airflow manages workflows using DAGs (Directed Acyclic Graphs) to periodically call the external API and collect data. The collected data is then sent to the message broker via a Kafka producer.
Apache Kafka: Message Broker
Kafka is a real-time data streaming platform that receives data sent by Airflow as a topic and makes it available to Kafka consumers.
Kafka Consumer: Data Processing
The Kafka consumer subscribes to the Kafka topic and processes the incoming data, preparing it for storage.
PostgreSQL: Database
PostgreSQL serves as the database where the processed blood sugar data is stored for further analysis and visualization.
Data Flow
Data Collection: Airflow periodically sends HTTP requests to the External API to fetch the latest blood sugar data.
Data Transmission: The fetched data is serialized into JSON format and sent to Kafka as a message.
Data Consumption: Kafka consumers subscribe to the topic and receive the data, which they then process.
Data Storage: The processed data is inserted into the PostgreSQL database, where it can be queried and analyzed.
Benefits
Scalability: Using Kafka allows the system to handle a large volume of data with ease.
Reliability: Airflow ensures that data collection tasks are executed as per the schedule, with retry mechanisms in case of failures.
Efficiency: PostgreSQL provides a robust storage solution for managing and querying blood sugar data.
Modularity: Each component of the architecture can be scaled and managed independently, allowing for flexible system management.
Conclusion
This system effectively demonstrates how modern data engineering tools can be leveraged to build a reliable and scalable data collection and storage pipeline. By integrating Apache Airflow, Apache Kafka, and PostgreSQL, the system ensures efficient data handling, from collection to storage, making it an ideal solution for real-time data processing needs.