U.S. Department of Transportation builds a real-time aviation database with Databricks
The U.S. Department of Transportation used the Databricks Data + AI Platform on Azure with Delta Lake and Apache Kafka to unify FAA SWIM streaming data with on-premises Oracle and Sybase databases into a real-time commercial flight database, powering ML-driven predictions of air traffic patterns and reducing the cost of collecting and ingesting streaming data by 90% compared to other cloud solutions.
Overview
The U.S. Department of Transportation used the Databricks Data + AI Platform on Azure with Delta Lake and Apache Kafka to unify FAA SWIM streaming data with on-premises Oracle and Sybase databases into a real-time commercial flight database, powering ML-driven predictions of air traffic patterns and reducing the cost of collecting and ingesting streaming data by 90% compared to other cloud solutions.
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Inspect the highlighted sourceThe challenge
USDOT needed to unify fragmented on-premises databases (Oracle, Sybase) and public FAA SWIM streaming data into a real-time commercial flight database to comply with updated Bureau of Transportation requirements, in an industry that traditionally relied on on-premises platforms rather than the cloud, making it hard to control data quality and scale without heavy manual review.
The solution
USDOT chose Microsoft Azure and the Databricks Data + AI Platform to democratize its various data sources, including on-premises databases like Oracle and Sybase, with real-time streaming via Apache Kafka, using Delta Lake to build reliable pipelines that feed Tableau and Power BI dashboards and machine learning models predicting air traffic patterns and passenger impact.
Reported business value
USDOT reduced the cost of collecting and ingesting streaming data by 90% compared to other cloud-based solutions, and its data engineers, scientists and analysts now collaborate on one platform to deliver real-time aviation dashboards and more accurate predictions about resource needs.
Sources
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