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Understanding the SAP
HANA Difference Amit Satoor, SAP Data Management
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© 2013 SAP AG. All rights reserved. 3
The future holds many transformational opportunities Capitalize on the new technology frontier
Retail: From transactions to
1:1 engaging relationships
Manufacturing: From mass
production to
custom 3-D printing
Healthcare: From generic
treatments to personalized
medicine
© 2013 SAP AG. All rights reserved. 4
• Database & data processing
engines
• Application Server
• Integration Services
• Development, Deployment and
Administration
SAP HANA Difference Enabling real-time computing design patterns across entire software architecture
OLTP + OLAP
in Columnar database
SIMPLIFIED
Application
Processing
OPTIMIZED
End-to-end
Data Processing
CONVERGED
Text Image
Spatial/GIS Transactions Sensors
Prescriptive Predictive
Sentiment Intelligence
Machine Learning
Operational Analytics
SAP HANA (Main Memory)
SAP HANA (Main Memory)
SAP HANA (Main Memory)
Application Layer
In-Memory
Database layer
Libraries
© 2013 SAP AG. All rights reserved. 5
Uncover value
Create breakthroughs
Experience simplicity
INNOVATIONS PREVIOUSLY UNFEASIBLE
• Real-time genome analysis
• Instantaneous fraud detection
• Predictive maintenance
• Optimize procurement, manufacturing, transportation
• Real-time MRP with instant re-planning
SIMPLICITY PREVIOUSLY UNACHIEVABLE
• Transactions and analysis in one system
• Efficiently analyze structured and unstructured data
• Fewer systems needed
• Hardware cost savings
• Less DBA involvement needed
SAP HANA In-Memory
Transaction & Analysis
directly In-Memory
VALUES PREVIOUSLY UNATTAINABLE
• Iterative period end closing
• Cash forecasts/management
• Real-time offer calculation
• In-moment sales forecast
• Self-service apps with instantaneous response
• Interactive POS data analysis
Building next generation apps with SAP HANA John Appleby
@applebyj
Global Head of SAP HANA
What is SAP HANA?
What is SAP HANA?
• SAP HANA is a re-imagined platform for business applications Designed from the ground up
Not limited by 30 years of database legacy
Designed for modern multi-core computers
• SAP HANA includes the whole application platform in-memory Database Services
Text Analysis and Search
Event Processing
Predictive, Graph and Spatial Engines
Integration/Web Services
• SAP HANA is Enterprise Ready High Availability, Disaster Recovery, Backup/Restore, ACID Compliant
Security Compliant (e.g. HIPAA)
Repository, User and Version Management
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The structure of future applications • We believe that future applications will span domains, in real-time
9 Reference Data
Internet of Things Transactional Data
Customer
Employee
Invoice Sales Order
Product
Suppliers
Social/News
Challenges of a traditional RDBMS
Oracle Stack
11
Microsoft Stack
12
IBM Stack
13
SAP HANA
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Real-Time Applications
Being able to transact in real-time…
• Consuming transactional data
• Tested at up to 250k transactions/sec in a bank
• Stored only once No Indexes
No Aggregates
No Materialized Views
No Duplication or ETL
• Dramatic reduction in data footprint Up to 20x for redesigned apps
Normally 5x for re-platformed app
• Reduced data footprint = simplicity Dramatic reduction in cost to build and maintain
5-20x less developer effort
16
… and report in real-time
• SAP HANA Information Views built on base data 2bn scans/sec/core, 16m aggregations/sec/core
40% more with Intel Ivy Bridge, 50% more cores
750m aggregations/sec with 1 40-core system
• Most CPU time spent in Data Mart is on ETL Aggregates are not required in SAP HANA
Instead, CPU time spent calculating what is needed
17
Consuming Reference Data
Public reference data is everywhere
• Most governments have an active data program
• Many public and private organizations have the same
• If you need it… it’s probably available
• Most reference sources are free of charge
19
NOAA Temperature and Rain data
• NOAA NCDC data is 140m measurements per annum
• 4GB/year stored in SAP HANA – stored only once
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We create re-usable information views
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Good performance
• Even aggregating all our weather data, 2.4bn rows
• 1-2 seconds
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Performance improves as we filter
• Performance always improves as we filter
• This model can be joined into other models in SAP HANA system
• Or consumed from another SAP HANA system via Smart Data Access
23
Consuming Social & Sensor Data
Social and Sensor data is everywhere
• Almost everything has a sensor
• Most sensors have an API
• Most APIs are publicly accessible
• Usually OAuth and OData compliant
• Easily integrated into SAP HANA
25
Consuming Twitter/News with SAP HANA
• Using python it is straightforward to integrate APIs into SAP HANA
• Specific keywords (products, companies, people) can be tagged
• Sentiment analysis possible
(see next section)
http://scn.sap.com/community/developer-center/hana/blog/2013/09/02/predicting-my-next-twitter-follower-with-sap-hana-pal
26
Text & Sentiment Analysis
Consuming Text
• Storage and analysis of Text data straightforward
• Either in PDF/Text form in a large database object (up to 2GB)
• Or consumed from social/news feeds
• Both Search and Sentiment is possible from one text index
• Text indexes are built asynchronously
28
Building a Text Index in SAP HANA
• One simple command:
• Physically creates a table $TA_VOICE
• 1m rows, just 50mb
29
Consuming Text Indexes
• Text Analysis is very powerful Language
Sentiment
Token (Keyword)
Type e.g. Sentiment, Weapon, Emoticon
• Queried like any other DB table
• Joined into an Information Model
30
Text Indexes into Information Views
• Now we can consume our Text Index into an Information View
• Now it is part of our calculation model which we can consume externally
31
Simple Info Access (SInA)
• Note we can also consume text indexes into JavaScript
• Allows for Google-style searching
32
Predictive Analysis Library
SAP HANA Predictive Analysis Library
• PAL can be used to write predictives in-line with applications
• Providing the most popular predictive algorithms
• Performance is typically excellent (1-5 seconds) even on big datasets
34
SAP HANA Predictive - Integration
• We can use SAP HANA Information Models to run PAL algorithms against real-time data
• In this example we do association analysis between customer and merchant
35
SAP HANA Web Services (XS)
SAP HANA XS
• Provides a lightweight web server
• Server-Side JavaScript or OData
• Scalable and Enterprise-Class
• Repository with versions and users
37
D3 JavaScript Libraries
• Easily consumed into SAP HANA XS
• Connect to SAP HANA XS OData Services or Server Side JavaScript
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SAP UI5
• Installed on your SAP HANA Appliance
• Provides the ability to build rich UI applications out the box
39
SAP HANA UIS
• SAP HANA UIS provides the ability to build widgets and pages very quickly
• Very useful for Analytics apps, which are easy to build in SAP HANA
40
SAP River
SAP River development language
• Included with SAP HANA SPS7
• Rapid, descriptive language
• Combined with SAP HANA Views
• OData Compatible
• SAP HANA XS for development
• Build apps in days, not months
42
Example Applications
Retail Customer Analytics
• Built on real-time POS data
• Aggregated on the fly based on inputs
44
Retail Customer Analytics
• Use of D3 JavaScript Libraries
45
Influencer Analysis
• Built in SAP River and Lumira in 1 day
46
Influencer Analysis
• Consumes both structured and unstructured data in one model
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