DfT moves ticketing data application to Google Cloud
Department’s LENNON software is migrated to off-site environment
The Department for Transport has moved a major application for processing ticketing data from its own datacentres to a Google Cloud environment.
The rail industry uses the Latest Earnings Networked Nationally Overnight (LENNON) application to process data including ticket sales and financial information from rail franchises. The DfT runs its own in-house version “containing a subset of the data for our own analysis”.
The program was “a huge system” taking up in excess of 100 terabytes of space in the department’s datacentres, according to a blog from Luke Radford, the DfT’s head of CIO advisory. Heavy usage also resulted in “slow query times”, he added.
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During “a wider transformation” programme, the DfT worked with Google and its delivery partner, Manchester-based IT firm Cloud Technology Solutions, to explore the possible options for closing down its datacentres and moving data and services – including LENNON – to the cloud.
“Backups and maintenance on GCP (Google Cloud Platform) should be frictionless,” said Radford. “By comparison, the current application requires frequent manual intervention from the colleagues using it. Moving it to GCP will free up time and resources that could be better used elsewhere.”
LENNON is the first DfT service to be moved into a Google environment, and work is ongoing, but the department is already seeing the benefits, Radford said. Queries that previously took several hours can now be processed in “less than 20 seconds”, while multiple queries can also be processed concurrently without harming the program’s performance.
Chief architect Mark Lyons said: “The transformation of LENNON within GCP is a really exciting first step towards moving and transforming our on-premise services into Google Cloud Platform within the next six months. This has been a fantastic example of collaboration between ourselves, Google, their partners CTS and our Rail colleagues, and it has given us a design pattern that can be reused for other data processing and analysis requirements across the wider department.”
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