AWS Case Study

Manchester Airport Group (MAG)


About Manchester Airport Group

The Manchester Airports Group umbrella consists of the operations of Manchester Airport, alongside London Stansted and East Midlands. MAG Property and Cargo Operations also come under the remit of our brand, representing Manchester Airport as the global gateway to the North of England and beyond. Manchester Airport has won prestigious industry recognition for customer service, and holds the title of ‘Best UK Airport’.

The Challenge

MAG already had a mature stack of BI and database products for reporting however these were being stretched to the limits and needed to be replace by a solution that would create the extensible and flexible data solution that would allow MAG to reach their ambitions. The new solution would need to enable an extended Data Warehouse, scalable and elastic compute, deal with seasonality spikes of passenger travel and allow real‐time streaming of data; empowering MAG to become a real‐time business across their entire customer journey.

Why Amazon Web Services

Manchester Airport Group wanted secure, resilient environment with repeatable build patterns and Amazon Web Service was perfect for this. “We wanted to create an architecture than can evolve over time to meets MAG’s new challenges delivering benefits early and continuously without the need for MAG to invest in a large, front‐loaded enterprise Data Warehouse programme” says Stuart Hutson Chief Technical Office (previously the Head of BI at the initiation of the project). The MAG AWS environment includes Redshift, Kinesis, S3, SQS, SNS, CloudWatch, CloudTrail, ECS, EC2, RDS, Config Service as well as other AWS Services.

The Benefits

In the first 6 months of the project MAG went from a single instance database to a scalable Data Warehouse using Redshift, daily sales rung in at store level to over 90% of all sales automatically ingested at product level from over 50 separate retailers such as Dixon , Burger King, Hugo Boss, WH Smiths etc., only being able to access reporting via a reporting tool to authorised users being able to visualise data and use data science tools such as R and Python for self‐server analytics and from no ability to create and run own SQL to sandboxes within the scalable Redshift environment.

By the end of the first year, MAG had added a real‐time streaming solution to ingest data from car park and fast-track bookings to the data warehouse using Kinesis and Lambda, and by the end of the second year had multiple real time dashboards on TV screens in their control room showing the real‐time state of the customer security queues ingesting data from dozens of sources including trains, queues, metal detector scan, body scans, x‐rays etc.

Stuart Hutson MAG CTO said “Crimson Macaw has accelerate our move to cloud for data warehouse with some great solutions, their work on real time dashboards using AI for passenger predictions in our airports has changed our operational agility and we look forward to doing more with Crimson Macaw to make even better use of our data”