We have partnered with Austroads to completely transform its data analytics pipeline, transitioning the transport agency off legacy systems to a highly scalable, automated technical architecture.
As growing volumes of heavy vehicle data pushed processing thresholds, we implemented advanced cloud, data, and continuous integration/continuous deployment pipelines using Databricks and AWS. This modernisation establishes a high-performance foundation capable of securely handling massive data expansion while maintaining strict regulatory compliance under the National Telematics Framework.
Key Efficiency Gains:
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Our architectural design prepares the platform to scale from processing data for a few thousand vehicles up to an estimated 90,000 heavy vehicles over the coming years.
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We integrated automated testing directly into the ingestion data pipelines using a modular framework to capture third-party errors seamlessly.
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The entire technical infrastructure rollout is automated using Terraform, streamlining resource provisioning and removing processing delays.
Overcoming Infrastructure Limits and Scaling for Machine Learning
Managing massive, externally generated datasets requires moving beyond standard analytics tools to eliminate systemic processing bottlenecks and data mastering errors. By embedding strict validation checks and modular pipelines, we help transport agencies manage complex workloads like GPS tracking and vehicle classification without losing operational velocity. This infrastructure uplift eliminates legacy performance limitations and ensures the platform is fully configured to deploy complex, future-ready machine learning and AI use cases at scale.
“Databricks resolves scalability issues, as it is designed to handle increasing data loads, supports machine learning, and enables automation and modular architecture. It is also built for collaboration, allowing us to deliver value quickly.”
Marty ConneelyPartner | Mantel
