Learn best practices from Hilton Hotels Worldwide as they built an Enterprise Data Lake/Management (EDM) platform on AWS to drive insights and analytics for their business applications, including worldwide hotel booking and reservation management systems. The EDM architecture is built with Hadoop clusters running on Amazon EC2 combined with Amazon Redshift and Amazon Athena for data warehousing and ad hoc SQL analytics. This is a great opportunity to get an unfiltered customer perspective on their road to data nirvana!

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Ask an Amazon Redshift Customer
Anything
Sumanth Punyamurthula
Director ADM and Data
Quality
Hilton Worldwide
A N T 3 8 9
Prahlad Rao
Solutions Architect
Amazon Web Services
Raziul Akm Islam
ProServ Consultant
Amazon Web Services

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Agenda
Enterprise information management (EIM) at Hilton—
Background and motivation
EIM architecture & benefits
Design considerations
Looking ahead

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Data & analytics at Hilton
Vision
• Enable travel & hospitality market disruption through data & analytics innovation
Mission
• Drive Hilton’s performance with actioned, integrated insights, through market-leading,
differentiated expertise and continuous innovation
Strategies
• Create aspirational and unrivaled hospitality data & analytics team that attracts best talent
• Become a trusted strategic business partner, driving untapped incremental value
• Provide timely access to quality data and innovative solutions

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Evolution
today
Siloed
Manual
High-touch
Latent
Tactical
Reactive
Integrated
Automated
Self-service
Actionable
Strategic
Predictive
tomorrow

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Things we must get right
4. Data science capabilities
Establish an advanced, forward-looking analytics capability that identifies
untapped value
5. Action from insights with data at its core
Raise overall analytics acumen and develop a culture of fact-based
decision-making
3. Simplified reporting
Define the art of the possible, then enhance and simplify reporting with the
ability to consume more information faster
1. Fit for purpose technology
Build a technology foundation for data to be used as a highly valuable asset at all levels in
the organization
2. Trusted data
Streamline processes for creating, cleansing, gathering, and using data

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The new EIM architecture

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EIM benefits
Platform
• Combine best of open-source Hadoop
tools with Amazon Redshift data-
warehousing service to unlock powerful
data analytics capabilities
• Leverage Amazon Simple Storage
Service (Amazon S3) as central data lake
to store, consume, and process data
across data zones
• Use Amazon Redshift Spectrum to
analyze larger data sets directly from
Amazon S3
• Move from offline, batch-based to
streaming workflow to process and
analyze data faster
Business
• Enhance and simplify reporting
• Centralized data store, reporting, and
analytics capabilities for Hilton
• Retire existing legacy data marts,
warehousing environments
• Reduce overall cost and improve
manageability with a unified data
platform
• Self-service capability for data
scientists and analysts for timely access
to quality data

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EIM benefits
PHASE CUSTOMERS DATA DOMAINS REPORTING
1
• Revenue Management
• C-suite*
Incremental (3): Competitive
Intelligence, Pricing & Inventory, RM
Systems
Today
380
Standard &
ad-hoc reports
Tomorrow
40-80
Reports, dashboards,
apps
2
• Honors
• CRM
Incremental (1): Guest
60
Standard &
ad-hoc reports
10-20
Reports, dashboards,
apps
3
• Corporate & Brand Marketing
• Regional Marketing & eCommerce
• Digital
Incremental (2): Digital Products,
Marketing
160
Standard &
ad-hoc reports
30-50
Reports, dashboards,
apps
4
• Sales
• HRCC
Incremental (3): Call Center
Transactions, Guest Experience, Sales
(Accounts)
180
Standard &
ad-hoc reports
20-40
Reports, dashboards,
apps
5
• Partnerships
• Brands
Incremental (3): Guest Experience,
Owner, Product Quality
30
Standard &
ad-hoc reports
5-15
Reports, dashboards,
apps
n..
Customer Insights, Product Management,
Development, Feasibility, Owner’s Intelligence,
F&B, Strategy, Operations, Ops Finance,
Investor Relations, FP&A, Risk Management,
Treasury, HSM, HR, GBCS Finance, Legal, IT,
Corp Affairs, Accounting
Incremental (9): Accounting,
Development, Human Resources
(People), Market Intelligence, Ops
Finance, Procurement
140
Standard &
ad-hoc reports
40-70
Reports, dashboards,
apps

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Design considerations
Concurrency
• Right balance between multiple projects and domains sharing Amazon Redshift cluster
resources for user queries, dashboards, cubes, and reporting
• Addressed by multiple smaller clusters based on domains—Customer 360, revenue management, and others
• Use Amazon S3 as central data store for multiple clusters, opening up opportunities to leverage Amazon
Redshift Spectrum and Amazon Athena in the future
• Data design and maintenance plans
• Query alignment with distribution/sort key
• View approach for filtering, aggregation, and summarization of the datasets and leverage Amazon Redshift’s
MPP architecture for joins and aggregation based on the distribution and sort keys
• The above approach helps business users and analysts to write queries without having to worry about
underlying design architecture
• Data transformation/cleaning leveraged inside Enterprise Zone (Hadoop tier) before being pushed to Business
Zone (Amazon Redshift) through Amazon S3
• Vacuum and analyze predicate columns immediately after each ELT

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Design considerations
• Workload management to manage priorities and boundaries
• Leverage WLM queues to segregate and prioritize workloads
• ELT and maintenance workloads—Run daily and weekly, respectively
• Complex cubes—Dynamically allocate more memory by utilizing all slots and run nightly
• Ad-hoc queries—Uses same queue as cubes, with more user concurrency and run during the
day
• Campaign workloads—Reserve slots to allow daily campaign jobs
• WLM query rules
• Query monitoring rules for metrics-based performance boundaries to confine bad queries

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Looking ahead
• Extend the platform for additional internal teams and domains—Customer, Revenue
Management, Sales, Human Resources, Risk Management, Strategy, Operations, and
others
• Build machine learning capabilities to power data science projects that can answer
questions around “How Did It Happen?” and “What Will Happen Next?”
• Streaming workflow to process and analyze data faster in inline mode
• Leverage additional AWS services to simplify ad-hoc querying capabilities using
Amazon Athena
• Further optimize the platform for scale and performance

17. Thank you!
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Prahlad Rao
Solutions Architect
Amazon Web Services
Raziul Akm Islam
ProServ Consultant
Amazon Web Services
Sumanth Punyamurthula
Director ADM and Data
Quality
Hilton Worldwide

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