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Letting Data Speak!
Case Study
AWS Cloud-based Data Pipeline for Industry 4.0 Insights
About the Client
An Industry 4.0 solution provider serving the manufacturing industry
Challenge
The client needed to develop a comprehensive solution for manufacturing companies to gain business insights and enable data science applications. Multiple machines in manufacturing facilities were generating vast amounts of data points using Industry 4.0 devices and posting them to an MQTT endpoint. The challenge was to create an AWS cloud-based data pipeline to ingest this data and calculate fact tables for dashboard visualization and future data science purposes.
Key Results
Developed an AWS cloud-based data pipeline capable of processing data from multiple (100k+) Industry 4.0 devices sending messages every second
Created a scalable system to populate multiple fact tables for generating business insights at various organizational levels and time intervals
Implemented data APIs to provide seamless access to the data warehouse for a third-party application layer
Solution
The team implemented a comprehensive AWS-based solution to address the client's needs:
Utilized Confluent Kafka MQTT proxy to listen to MQTT messages from Industry 4.0 devices, mapping each MQTT topic to a universal Kafka topic
Developed multiple data pipelines using Python:A pipeline to ingest raw data from Confluent Kafka MQTT proxy into a PostgreSQL database A pipeline to process historical data and populate fact tables with business insights at various levels (hourly to yearly, machine to organization) A pipeline to synchronize master data from the application layer to the data warehouse
Created data APIs using Flask to provide requested data in JSON format, enabling the third-party team to generate violations and dashboards
Deployed the entire solution on AWS EC2 instances, with AWS Lambda functions for automated shutdown and restart to optimize cost management
Technologies Used
AWS (EC2, Lambda)
Confluent Kafka MQTT proxy
Python
PostgreSQL
Flask
MQTT
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