Challenge
Our client, a large-scale automotive components manufacturer, implemented a real-time health feedback solution for vehicles using their components. The system used IoT engine and sub-system data points that would notify the user of issues with the vehicles' components in real-time along with recommendations for correcting those issues. This system was built in Azure using Event Hub functionality and custom development.
While the solution was successful, it was also limited to only reactively responding to warnings and errors as they occurred. The company needed an analytics solution to create functionality for developing predictive models that provide meaningful proactive recommendations to the fleets. To help them build a successful machine learning project, they chose to collaborate with New Era Technology.
Solution
To support the high-level analysis and machine learning required to build predictive models, the company needed a large amount of data collected over an extended time frame. Since the current system only retained data long enough to provide real-time responses, New Era needed to build a second big data solution to capture and retain the IoT data and integrate manufacturing data that our client would use in the analytics process. To complete this objective, we designed and implemented a data warehouse to collect and organize the data and provide a data platform for high-end analytics and machine learning.
In addition to creating this solution, we also worked alongside the client and:
- Developed an Azure and cloud solution architecture
- Identified recommended technologies and planned for implementation
- Designed and developed an integration model to ingest enterprise source data
- Built the Azure data pipelines to process the near real-time data from devices
- Integrated data into a curated data model for BI & analytics
- Demonstrated the BI capabilities to prove the business value of the data
Ultimately, we built a modern data platform that uses the existing Event Hub functionality to output the data, Azure Data Factory to manage the data flow, and Snowflake Data Warehouse to store, organize, and integrate the data.
