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

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Microsoft Stack

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IBM Stack

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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

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… 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

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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

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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

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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

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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

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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

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Building a Text Index in SAP HANA

• One simple command:

• Physically creates a table $TA_VOICE

• 1m rows, just 50mb

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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

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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

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Simple Info Access (SInA)

• Note we can also consume text indexes into JavaScript

• Allows for Google-style searching

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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

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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

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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

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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

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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

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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

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Example Applications

Retail Customer Analytics

• Built on real-time POS data

• Aggregated on the fly based on inputs

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Retail Customer Analytics

• Use of D3 JavaScript Libraries

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Influencer Analysis

• Built in SAP River and Lumira in 1 day

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Influencer Analysis

• Consumes both structured and unstructured data in one model

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Questions?

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John Appleby

John.appleby@bluefinsolutions.com

@applebyj

bluefinsolutions.com/johnappleby