VR Group is moving its infrastructure to AWS to gain cost effectiveness, elasticity, and faster development. It started implementations in 2016 and its core infrastructure was ready in 2017. VR Group is now focusing on building an analytics project using data from various sources to power dashboards, personalized recommendations, and predictive analytics. It is using AWS services like Snowflake, API Gateway, and Lambda along with open source tools to build its data analytics stack.

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Data Driven Public
Transport
Saku Vaittinen, VR Group | 21.3.2018

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VR Group: Moving people and other things

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VR Group’s track to AWS
• Cost effectiveness + elasticity + development speed
• First implementations 2016, core infra ready 2017 (CON206)
• Terraform
• Direct Connect
• Shift from on-prem to cloud required lots from organization
• Comprehensive mindset change with DevOps through new projects
• Lift’n’shift avoided
• Focus on architecture!

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Data on driver’s seat
• Incentive: Free competition on local railway passenger traffic by
2021 and on long-haul by 2024
• From Excel to Data science
• Learning from data

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Analytics project
• Started in January 2018
• 6 members, everyone new
• Lots of legacy burden
• Almost production-ready
• Infrastructure, Data platform, Data model
• First data sources
• Power BI dashboards and reports
• Recommendation API for new ticket system
• Data science work has been started

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OPERATIVE INTELLIGENCE CUSTOMER INTELLIGENCE
RESOURCE
PLANNING
PERSONALIZED CONTENT
PRODUCT, PRICING
STRATEGY
CUSTOMER SERVICE
ROBOTIZATION
CORPORATE
TRAVEL
CUSTOMER
VALUE & CHURN
SHIFT PLANNING
OTHER OPERATION
OPTIMIZATION
LOYALTY
PROGRAM
DEVELOPMENT
The Problem

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Diverse data ecosystem
Data sources
• DBs: on-premise, cloud
• Apps: mobile, web, ticket machines
• Systems: seat reservation, train planning, ERP
• Communications: email, surveys, social media
Data users
• Dashboards & analytics applications
• Personalized content
• Operative intelligence
• Customer service

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Data analytics stack
Cloud
• AWS
Open source tools
• Apache NiFi, Jupyter, DbSchema, Python …
Business analytics applications
• Power BI
Warehouse
• Snowflake

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AWS Architecture
Connectivity
• VPC Privatelink
• Direct in-account connection to Snowflake in same region
Data pipeline
• Data Migration Service
• Daily batches, Continuous migration by CDC files
Analytics API
• ECS, Lambda, API GW, ElastiCache

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Snowflake characteristics
• AWS-native
• Pay-per-use pricing model
• Separated storage & compute
• Virtual Data Warehouses
• Variable patterns both in data import & usage
• Batch, stream
• BI users, Data science, API

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Next hot things
• Raw data from ALL sources
• Machine Learning for e.g.:
• Time series forecast algorithms
• Ticket pricing
• Work shift lists
• Train configuration planning
• Immediate reaction to changes