Enabling Self-Service Analytics & ML at Transport for NSW with Databricks

Sydney Databricks Meetup - December 2020

In this business-focused Databricks Meetup, Shelby Ferson (Sr. Manager ANZ – Databricks), Sandeep Mathur (Program Manager – TfNSW) and Rodney Joyce (Practice Director – Data-Driven) discuss how Transport for NSW (TfNSW) leveraged Databricks to enable Self-Service Analytics and Machine Learning on the TfNSW Operational Data Lake.

We look at an end to end delivery of an Operational Data Lake implementation from the technology, business requirement, and the implementation point of view.

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In addition to Operational Reporting, the main objective was to ingest, store and process huge amounts of operational public transport data on NSW to empower innovation across Transport departments as non-technical analysts, business users, and data scientists now have a scalable, secure platform to test transport-related hypotheses and mine public transport data for additional insights.

On top of this, external users and the public can now download historical GTFS public transport data for use within their own services and products.

The solution uses services such as:

  • Databricks Delta
  • Databricks SQL Analytics
  • Databricks Spark
  • Microsoft Synapse
  • Azure Machine Learning Service
  • Power Platform
“TfNSW needed a solution to capture real-time data for every vehicle in motion across the state. This solution just gives us that so that we mine nuggets from this data at a later date. We now have an ability to self-service without waiting for someone else to curate data.”
Sandeep Mathur Photo
Sandeep Mathur Program Manager, Transport for NSW
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