The engagement covered the platform’s full delivery lifecycle: architecture, implementation, workload migration, testing and continuing operational support.
Rail · Data platform modernisation
Building the data platform behind fleet analytics
Porterbrook
Trigger Data built Porterbrook’s end-to-end Azure and Databricks platform, migrated its existing workloads and completed the testing and post-migration work needed for ongoing operation. The platform now processes high volumes of railway data and serves multiple analytical workloads, with continuing engineering support to maintain and evolve the environment.
Operational challenge
Porterbrook needed a common data platform capable of handling high volumes of railway data and supporting different analytical workloads. Existing workloads also needed to move into Databricks, with testing and post-migration work to establish that they were ready for ongoing use.
What Trigger Data built
We delivered an end-to-end platform using Microsoft Azure and Databricks, including high-throughput data ingestion and the migration of existing workloads. We completed post-migration activities and testing, then continued supporting the environment through platform maintenance, code debugging and technical investigation.
User workflow
Data from railway and business systems enters the platform and is processed for downstream analysis. Porterbrook can run multiple workloads on the shared environment, while its technical team works with Trigger Data to investigate issues, maintain pipelines and support changes.
Operational outcome
Porterbrook gained a high-throughput data platform that serves multiple workloads, including train-recorder data processing and fleet analytics. The completed migration established Databricks as the processing environment, with ongoing engineering support extending beyond the initial implementation.
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