CD203
How to Port an Existing Solution to SAP HANA
with the Maximum Benefit
Michael Bechauf, HANA Platform Product Management
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Agenda
HANA Overview
 What is the spectrum of applications that can be built ?
 What tools are available ?
 What is deep integration with HANA ?
Case Studies
 Airport security system
 Social media analytics
Overview of a HANA ISV porting project
 Project timeline
 Development methodology
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Introduction
What is the HANA ecosystem ?
HANA – the open development platform
Fast, extensible, multi-purpose
Previous use of HANA
 ERP Accelerator
 New business applications
 Analytics tools
Now: HANA as a real-time, in-memory database






Independent of ERP, beyond traditional business applications
Custom analytics visualizations
Custom applications can be built on open interfaces (JDBC, ODBC)
Developer support with free developer licenses
Easy starting point by HANA hosting in the Amazon cloud
Dedicated support for innovative ISV companies
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HANA Overview
Quick walkthrough of the HANA capabilities and technological foundation
HANA as a database for ISV applications
You could choose to look at HANA as
just a very fast In Memory database …
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HANA as a database for ISV applications
… but you would be giving away a key
competitive advantage
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HANA Overview
Data Modeling
SQL
R Integration
+
Column and
row store
Predictive
analytics
Math
Libraries
SQL interface on
columns & rows
T
In-memory
Compression
Multi-core/
parallelization
Text Retrieval &
Exploration
Application Server
Scripting
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SQL
Column and Row Store
DBs typically use a row-based storage; SAP HANA supports rows, but is optimized for column-order
data organization
Order
456
457
458
459
Country
France
Italy
Italy
Spain
Product
corn
wheat
corn
rice
Sales
1000
900
600
800
456
France
corn
1000
457
Italy
wheat
900
458
Italy
corn
600
459
Spain
rice
800
Row order organization
456
457
458
459
France
Italy
Italy
Spain
corn
wheat
corn
rice
Column order organization
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1000
900
600
800

Single-record access:
SELECT * FROM SalesOrders
WHERE Order = ‘457’
Single-scan aggregation:
SELECT Country, SUM(sales) FROM
SalesOrders WHERE Product=‘corn’ GROUP BY
Country
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HANA – Developer Scenarios
1. Business Analytics with HANA
Develop business analytics scenarios on top of HANA using Business Objects tools.
Analytics
a) load data
HANA
c) build analytical apps
using BOBJ tools/sdk
b) model
2. JDBC/SQL Programming HANA
Develop applications on top of HANA using JDBC and SQL
New Apps
JDBC/ODBC
d) Build custom apps using JDBC/ODBC
against HANA
a) load data
HANA
c) Use SQL Script or advanced libraries
(e.g. Predictive Analytics)
b) model
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HANA – Developer Scenarios
3. HANA Apps
Develop lightweight apps directly on HANA
Apps
a) load data
HANA
d) Build apps using
JavaScript/XS/HTML5
c) Use SQL Script or
advanced libraries (e.g.
Predictive Analytics)
b) model
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Algorithmic complexity
Lower-level languages like Java or C# are more
expressive and powerful, but run outside of HANA in
memory engine
Modeling
Key to performance breakthroughs is parallel execution
close to data
SQL Script
HANA programming environment is optimized towards
these properties
System-defined functions
 Trade-off between performance optimization, TCO
and simplicity of solution
(Business Function Library, Predictive
Analytics Library)
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Trade Offs
Increasing use of HANA by an ISV application
All logic on Application Server
Relational DB schema
(without Stored Procedures)
All logic on Application Server
HANA only used as a relational
column-store database
Some logic on Application Server
HANA modeling and scripting used
to push processing towards data
No Application Server
All processing done in a stateless
application
HTML 5
Relational diskbased row store
database
SAP HANA
Starting point
At best an intermediate step towards a real
HANA application. Validate the schema,
identify issues (e.g. constraints, triggers).
Breakthrough performance is not expected !
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Application server tier ideally becomes
only used for UI rendering.
However, high algorithmic complexity
may force break-up of business logic
between HANA and application server tier
Extremely low latency
Perfect for lightweight analytical
applications
Less useful for transactional
application that require state and
session management
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Case Studies
Airport Security System
Social Media Analytics
Airport security management
AlertEnterprise
Goal: Real-time analytics of securityrelevant events
 Analyze the potential threat from legitimate access
to security-relevant facilities
 Understand the context of an security event
 Provide alert as it happens and help respond
Results
 Processing of thousands of parallel events possible
 Simplification of overall architecture (replace rule
engine and CEP engine)
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Social Media Analytics
Next Principles
Goal: Real-time analytics of social
media events (Tweets, FB)





Currently, Java based solution
No real-time analytics
Limited in terms of data volume
Limited in granularity of analysis
Approach: Use text analytics to parse Tweets,
move logic into HANA as much as possible
Results
 Text analytics in HANA
 Rendering stays as a HTML 5 rendering
component in Java
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Value proposition for “good” ISV applications
Delivering across 5 dimensions of modern decision-processing
Deep
Complex & interactive questions
on granular data
Broad
High Speed
Big data, many
data types
Fast response-time,
interactivity
Real-time
Recent data, preferably
real-time
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Simple
No data preparation, no preaggregates, no tuning
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HANA and Open Source
Many ISV solutions right now use open source databases or infrastructure to
manage Big Data






Hadoop for storage and cluster management
Cassandra : Fault-tolerant key-value store running on a cluster
Memcached: Distributed in memory cache
MongoDB: Schemaless distributed storage of JSON documents
CouchDB: Storage of large documents
Couchbase: Commercial CouchDB including in memory caching
Undoubtedly, open source offers many advantages, but HANA has a sweet spot
 Pre-integrated solution removes the necessity to build infrastructure from open source components




Real-time analytics by means of column store design and in memory performance
Predictable processing times by means of parallel processing of complex algorithms
SQL as a language makes migration of existing systems easier
And of course – the SAP ecosystem
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A HANA Porting Project
Infrastructure, timeline, success factors
Getting started …
Utilize HANA in Amazon AWS
 Quick way to get started
 Free
 Performance degradation only for large databases and complex algorithms
Define a business scenario that makes sense for HANA
 Decide whether to do a full HANA port, or limit to certain sub-functions
 Remember that HANA OLTP performance will likely not exceed that of an
existing database
Understand the porting effort
 Algorithmic complexity
 Use of proprietary databases features (particularly stored procedures,
triggers, sequences, constraints)
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Timeline and success factors
Begin with a simple schema migration





Don’t worry about replication techniques just yet, just do a simple CSV import
Move some parts of your schema over
Familiarize yourself with HANA modeling and scripting methodology
Expose data through JDBC/ODBC and connect your application server
Test certain parts of your application functionality end-to-end with HANA sideby side
Decide on the final setup
 Migrate the whole schema and use HANA as your OLTP database, or do you
prefer a side-by-side model ?
 Trade-off between porting effort and TCO
Success Factors
 Clearly understand how to differentiate your HANA application in the market
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If HANA is not the OLTP database – set up Data Replication
SAP BusinessObjects
tools
SAP Business
Suite
Trigger Based,
Real Time
Log Based
Real Time
Extractor
Batch
Event Streams
Real Time
Data Sources
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SQL
BICS
SQL
MDX
SAP In-Memory Computing Studio
SAP BOBJ
Data Services
Sybase Replication
Server
Trading & Order
Management Systems
DB Connection
ETL, Batch
SAP BW
Non SAP
Data Sources
SAP LT
Replication
Server
Other query tools
SAP HANA Direct
Extractor
Connection**
Sybase Event
Stream Processor *
* Sybase ESP-5.1SP1, Q4-2012
** SAP ERP & BW Extractors
ODBC
SAP In-Memory Database
ECDA/ODBC
Calculation and
Planning Engine
Row & Column
Storage
Http/Https
In memory DSO
ODBC
Activation
Queue
XS Engine
SAP HANA
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Further Information
SAP Public Web
https://experiencesaphana.com/community/startups
http://developer.sap.com
SAP Education and Certification Opportunities
www.sap.com/education
Watch SAP TechEd Online
www.sapteched.com/online
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