autodesk technical webinar: sap hana in-memory database

Jan Teichmann, P&I HANA Product Management

November, 2013

SAP HANA Overview

© 2013 SAP AG or an SAP affiliate company. All rights reserved. 2

Disclaimer

This presentation outlines our general product direction and should not be relied on in making a

purchase decision. This presentation is not subject to your license agreement or any other agreement

with SAP. SAP has no obligation to pursue any course of business outlined in this presentation or to

develop or release any functionality mentioned in this presentation. This presentation and SAP's

strategy and possible future developments are subject to change and may be changed by SAP at any

time for any reason without notice. This document is provided without a warranty of any kind, either

express or implied, including but not limited to, the implied warranties of merchantability, fitness for a

particular purpose, or non-infringement. SAP assumes no responsibility for errors or omissions in this

document, except if such damages were caused by SAP intentionally or grossly negligent.


Storage Storage

CPU

Memory

CPU

Memory

Sub-second response, no matter how complex

Process data and application logic in parallel (MPP), using all cores in a multi-core architecture, by effectively partitioning data.

Avoid unnecessary compensation (e.g.: buffering, data duplication) during application execution by running application using the SAP HANA application services (built-in web server).

Eliminate disk I/O by keeping all data in memory using column store, and by significantly compressing data.

Access data faster using any column as index, and by accessing only relevant columns via dictionary-encoded column store.

Top 10

CPU

Memory

Bottleneck

Data Hard Disk: 10,000,000ns* / SSD: 200,000ns*

Disk Storage

Log

60ns*

L1 Cache

L2 Cache

L3 Cache

1.5ns*

4ns*

15ns*

Core 1 Core N

Any Column as Index

Parallelized Query

Query Compressed Data

Log

Copy into memory

1 Speed

2 Real-Time

3 Any Data

4 Any Source

6 Open

8 Prediction

9 Consolidation

5 Predictable Completion

10 Choice

Code

DB App

Data

(DB + App) SAP HANA

7 Simplicity


Technology trends: Amdahl’s law

Competitive DBs try to avoid HDD access, say with 99.9% success – Caching, indexes, aggregate tables, pre-fetching, hashing, compression, …

Pretty good? What is the impact of 0.1%?

10,000,000ns vs. 60ns: 150,000 times slower access!


The Bottleneck has Shifted…

Access to memory is 4 times slower than L3 cache, and 50 times slower than L1 cache…


Intel Xeon – Hyper-threaded Cores, Huge Caches

10 X

State of the art: 10 pipelined cores (20 threads per CPU), 30MB L3 cache

Hyper-threading: Sharing of one ALU between two threads; the chip handles the cycle-level task-

switching (when a thread is stalled, typically when it waits for memory)

Westmere-EX

Draw ing from: http://www.phys.uu.nl/~steen/web09/xeon.php

ALU

L3 L2

http://www.phys.uu.nl/~steen/web09/xeon.php


Chip Design – L1, L2 and L3 Level Cache – Columnar Processing

Cache aware memory organization, optimization and execution

Performance bottleneck in the past: Disk I/O

Performance bottleneck today: CPU waiting for data to be loaded from memory into cache

Minimize number of CPU cache misses and avoid CPU stalls because of memory access.

Approach: column-based storage in memory

Search operations or operations on one column can be implemented as loops on data stored in contiguous memory arrays.

High spatial locality of data and instructions, operations can be executed completely in CPU cache

without costly random memory accesses

Memory controllers to use data prefetching to further minimize the number of cache misses

Draw ing from: http://www.phys.uu.nl/~steen/web09/xeon.php

http://www.phys.uu.nl/~steen/web09/xeon.php


Advantages Of Columnar Storage

Advantage: Higher Data Compression Rates • Columnar data storage allows for highly efficient compression. Especially if the column is sorted, there are ranges of the same values in

contiguous memory, so compression methods such as run length encoding or cluster encoding can be used more effectively.

Advantage: Higher Performance for Column Operations • Search operations or operations on one column can be implemented as loops on data stored in contiguous memory arrays. • Compressed data can be loaded faster into CPU cache - performance gain (less data transport between memory and CPU cache)

exceeds the additional computing time needed for decompression • dictionary encoding, the columns are stored as sequences of bit encoded integers. That means that check for equality can be executed

on the integers

• Computing the sum of the values in a column is much faster if the column is run length encoded and many additions of the same value can be replaced by a single multiplication.

Advantage: Elimination of Additional Indexes • Storing data in columns already works like having a built-in index for each column: The column scanning speed of the in-memory column

store and the compression mechanisms – especially dictionary compression – already allow read operations with very high performance.

Advantage: Elimination of Materialized Aggregates

Advantage: Parallelization • In a column store data is already vertically partitioned. Operations on different columns can easily be processed in parallel. • In multi-node clusters, partitioning of data (“shared nothing approach”) in sections for which the calculations can be executed in parallel

leads to additional performance gains.


Run both transactional and analytical applications on one single data model.

– Database tables designed to support simultaneous high volume/speed transactional and analytical processing without compromising data consistency (ACID compliance)

Aggregate on-the-fly with no pre-materialization on key figures, including current transactions, using column store and parallel aggregation.

Traditional: OLTP and OLAP Separate

6 Hours 12:00:00 AM

OLTP + OLAP in SAP HANA

10:00:00 AM 10:00:01 AM Immediate

Current Data 24hr Old Data

Aggregate

ETL

Top 10

Real-time applications, zero latency

SAP HANA

1 Speed

2 Real-Time

3 Any Data

4 Any Source

6 Open

8 Prediction

9 Consolidation


10 Choice

7 Simplicity

6:00:00 AM


Embed sentiment fact extraction in same SQL

CREATE FULLTEXT INDEX TWEET_INDEX ON TWEET (CONTENT)

CONFIGURATION 'EXTRACTION_CORE_VOICEOFCUSTOMER'

ASYNC FLUSH EVERY 1 MINUTES

LANGUAGE DETECTION ('EN') TEXT ANALYSIS ON;

Embed geospatial in same SQL

CREATE COLUMN TABLE MYTABLE1

( ID INTEGER,

KEYFIGURE DECIMAL(10,2),

SHAPE ST_GEOMETRY

);

SELECT SHAPE.ST_AsGeoJSON() FROM MYTABLE1;

Embed fuzzy text search in same SQL

CREATE FULLTEXT INDEX i1 ON PSA_TRANSACTION( AMOUNT, TRAN_DATE, POST_DATE, DESCRIPTION, CATEGORY_TEXT ) FUZZY SEARCH INDEX ON SYNC; SELECT SCORE() AS SCR, * FROM "SYSTEM"."PSA_TRANSACTION" WHERE CONTAINS (*, 'Sarvice', fuzzy) ORDER BY SCR DESC;

Click- stream

Customer Data

Connected Vehicles

Smart Meter Point of Sale Mobile Structured Data

Geospatial Data

Text Data RFID Machine Data

Support advanced text analytics Analyze text in all columns of table and text inside binary files with advanced text analytic capabilities such as: automatically detecting 31 languages; fuzzy, linguistic, synonymous search, using SQL.

Structure unstructured data Use advanced text analytics, such as sentiment fact extraction, to structure unstructured data. Analyze streaming data from integrated ESP in combination with data in SAP HANA.

Process geospatial data

Social Network

SAP HANA

Any Data

SQL

“ At BigPoint in the Battlestar Galactica online game, we have more than 5,000 events in the game per second which we have to load in SAP HANA environment and to work on it to create an individualized game environment to create offers for them. In this co-innovation project with SAP HANA, using Real Time Offer Management at BigPoint, we hope to increase revenue by 10-30%.

Claus Wagner, Senior Vice President SAP Technology, BigPoint (video)

”

Top 10

Process any data, in any combination, instantaneously with SQL

1 Speed

2 Real-Time

3 Any Data

4 Any Source

6 Open

8 Prediction

9 Consolidation


10 Choice

7 Simplicity

https://www.youtube.com/watch?feature=player_embedded&v=N6bUkkiZHoE


SAP Sybase Event Stream Processor

• Unlimited number of input streams

• Incoming data passes through “continuous queries” in real-time

• Output is event driven

• Scalable for extreme throughput, millisecond latency

?

INPUT

STREAMS

Sensor data

Transactions

Market Events

Application

Studio

(Authoring)

Reference Data

SAP Event Stream

Processor

Database

Dashboard

Message Bus


Application

Top 10

Leverage remote database’s unique processing capabilities by pushing processing to remote database; Monitors and collects query execution data to further optimize remote query processing.

Compensate missing functionality in remote database with SAP HANA capabilities.

Accelerate application development across various processing models and data forms with common modeling and development environment.

Rapid data provisioning with data virtualization

Merge Results

SELECT from

DB(x)

SELECT from

DB(y)

SELECT from

HIVE

Application

One SQL Script

SAP HANA

Virtual Tables

Supported DBs as of SP6: HANA ,Sybase ASE, IQ Hadoop/HIVE, Teradata

Data-Type Mapping & Compensate Missing Functions

in DB

Modeling Environment



Modeling and Development Environment

1 Speed

2 Real-Time

3 Any Data

4 Any Source

6 Open

8 Prediction

9 Consolidation


10 Choice

7 Simplicity


SAP HANA Smart Data Access Data virtualization for on-premise and hybrid cloud environments

Benefits

Remote real-time query processing

Smart continuously self-tuning system

Secure access to heterogeneous data

sources

Heterogeneous data sources

SAP HANA to Hadoop (Hive)

Teradata

SAP Sybase ASE

SAP Sybase IQ

Transactions + Analytics

Teradata

Hadoop

SAP HANA

ASE

IQ

SAP HANA


SAP HANA Smart data access Differentiation

The intelligence of knowing when to delegate

query processing or pull the data into SAP

HANA for query processing, based on the

performance windows

Dynamic query recommendation

To return query results extremely fast.

Capabilities supporting fast processing

leveraging in-memory acceleration

Cost-based query optimization

Data pre-caching

In-flight transformation

Converged data processing

Data

Federation

Data

Virtualization

Smart

Data Access


“ It is only a matter of scaling the hardware – there are no other variables or unknowns. SAP HANA: Re-Thinking Information Processing for Genomic and Medical Data, Prof. Dr. Hasso Plattner, 2013

”

Top 10

Linear scalability to meet any time window

Multi-core / parallelization

No disk Partitioning Distributed computing

Scale Up Scale Out

With the power of mathematics and distributed computing, SAP HANA can predictably complete any information processing tasks, however complex, within a given time-window.

1 Speed

2 Real-Time

3 Any Data

4 Any Source

6 Open

8 Prediction

9 Consolidation


10 Choice

7 Simplicity

Sales and Distribution reports

Query 1: Single customer and material for one month

Query 2: Range of Customers and Materials for six months

Query 3: Year-over-Year trending report for Top 100 customers for five years

0.425 0.266 0.142

0.7 0.491 0.502

3.816

3.249 3.102

16 nodes(100 billion rows)

51 nodes(650 billion rows)

95 nodes(1,200 billion rows)

Extreme Linear Scalability Query processing time (in seconds)

Query 1 Query 2 Query 3

SAP HANA Performance, July 2012

SAP HANA scales better than linearly for workloads with increasing

capacity (up to 100 TB of raw data), complexity (queries with complex join constructs and significant intermediate

results run in less than two seconds), and concurrency (25-stream

throughput representing about 2,600 active users).

https://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&ved=0CEUQFjAC&url=http://www.eng.utah.edu/~cs6810/pres/11-6810-12.ppt&ei=VYKDUrqeMOyv4QSK8IDYDA&usg=AFQjCNGrYlwQB3YFsFAziL28nroz1rWofw





(only China)

XS: 128GB X X X X X X X

S: 256GB X X X X X X X

S+: 256GB X X X X X X

M: 512GB X X X X X X X X

M+: 512GB X X X X

L: 1.0TB X X X X X X

Scale Out (BW) X X X X X X X X planned

SoH: 1/2/4TB 1/2/4 1/2/4 2/4 1 1 1/2/4 2

High Availability X X X X X X X X planned

DR – Storage

Repl.: Async

DR – Storage

Repl.: Sync X X X X X

Certified HANA Hardware – June 2013*

* For most up to date list please go to the SAP Product Availability Matrix

http://service.sap.com/pam


Multi-SID on one SAP HANA hardware

“MCOS”

Multiple Components on one System, multi-SID

1 x Appliance

n x HANA DB

n x DB schema

n x Applications

E.g. DEV and QA system on one hardware. See SAP note

1681092.

„Classical“ scenario

Appliance approach for optimal performance

1 x Appliance

1 x HANA DB

1 x DB schema

1 x Application (e.g. ERP, CRM or BW)

SAP HANA

<HDB>

AS ABAP

SID: ABC

Schema ABC

AS ABAP

SID: ABC

SAP HANA SAP HANA

Schema ABC

AS ABAP

SID: XYZ

Schema XYZ

<HDB1> <HDB2>

Productive Systems Non-Productive Systems

Virtualization (on premise)

Virtualization technology separates multiple OS images each containing one HANA DB

n x Virtualized Appliances

n x HANA DB

n x DB schema

n x Applications

AS ABAP

SID: ABC

SAP HANA SAP HANA

Schema ABC

AS ABAP

SID: XYZ

Schema XYZ

<HDB> <HDB>

“MCOD”

Multiple Components on one Database

1 x Appliance

1 x HANA DB

n x DB schema

n x Applications

Prod. usage for white listed scenarios allowed, e.g. SAP ERP

together with SAP Fraud Management. See SAP notes

1661202 and 1826100.

AS ABAP SID:

ABC

Application

SID: XYZ

SAP HANA

Schema ABC

<HDB>

Schema XYZ

White-Listed Scenarios

https://service.sap.com/sap/support/notes/1681092




ODBC, JDBC

SAP HANA

Easily migrate your applications (e.g.: Java, PHP, .NET) using JDBC, ODBC and OData/JSON.

Build new web applications with any open source HTML5 / JS libraries, Server Side Java Script.

Easy to bring data into HANA.

– Import data in CSV, Excel or Binary formats. Load Geospatial files in shapefile, CSV, Binary, WKT and WKB file formats.

– Reuse current data sources with Data Virtualization.

– Replicate real-time data from multiple sources into SAP HANA for comprehensive data analysis.

Open Cloud Partner Program allows you to select the best SAP HANA cloud deployment option from several partners.

Top 10

Bring your own code to an open platform

App Services (Web Server)

DB Services

Browser / Mobile

Web JS Lib Data Viz Lib

Web App Server

http(s),OData/JSON

ODBO

Third Party &

Custom Application

HTTP(S), OData, XML/A ODBC, JDBC, ADBC, ODBO MDX, SQL

SQL Script

Any HTML5/JS Library

Stored Procedure Virtual Tables

Import

Real-time Replication

CSV, Binary, shapefile, WKT and WKB files

1 Speed

2 Real-Time

3 Any Data

4 Any Source

6 Open

8 Prediction

9 Consolidation


10 Choice

7 Simplicity


SAP HANA - Openness

SAP is committed to a Truly Open Ecosystem for

SAP HANA

• Intel partnerships for CPU optimization and Hadoop

distribution

• 11 Hardware partners with > 70 available hardware landscapes, incl. Virtualization

• Open APIs for BI (MDX, SQL), WebDevelopment

(HTTP/S), Dev Platforms (ODBC/JDBC)

• 3rd party Software certification for backup

infrastructures, integrate SAP HANA within bigger management environments, or provide Single-Sign-

On (SSO) capabilities

• Several (growing number of) Cloud Service Providers

• http://www.saphana.com/community/blogs/blog/2

013/09/24/engineering-open-appliances-for-high-performance-without-lock-in

http://www.saphana.com/community/blogs/blog/2013/09/24/engineering-open-appliances-for-high-performance-without-lock-in



















Browser / Mobile

Web App Server

DB Server

SQL

Stored Procedures

http(s)

Web JS Lib Data Viz Lib + + HTML5 /JS Libraries

Browser / Mobile

http(s), OData / JSON http(s)

OLAP Predictive Text Mining

BRM

DB Server DB-oriented Logic

Text Mining

Predictive

SQL Scripts

R Integration

Decision Tables

SAP HANA App Logic App Logic App Logic

App Logic App Logic App Logic App Logic App Logic App Logic

App Logic App Logic App Logic

Aggregate

+ + + Flexible Table:

Push-down code : Replace application logic at multiple places with reusable DB logic, written in SQL Script, consumed through OData.

Efficient execution with built-in application services : Significantly improve application performance by running applications using SAP HANA application services (built-in web server) to avoid multiple layers of buffering, to reduce data transfers, and processing logic.

Optimized and open: Built-in SAPUI5 libraries with open integration to 3rd-party libraries for both desktop and mobile user experience.

Dynamic Schema: Dynamically add up to 64,000 columns with SQL Insert or Update statements without ALTERing schema.

Top 10

+

Transformative power, simplified programming

App Services (Web Server)

Procedural App Logic

OData Java Script

Standard Table:

1 Speed

2 Real-Time

3 Any Data

4 Any Source

6 Open

8 Prediction

9 Consolidation


10 Choice

7 Simplicity


Compare HANA Web App Development To Classic Web Dev

Java + MySQL

Java + HANA

HANA XS

lib

R

R


Apps

SQL Script (Optimized Query Plan)

Unstructured

PAL R-scripts R Engine

“ The HANA platform at Cisco has been used to deliver near real-time insights to our execs, and the integration with R will allow us to combine the predictive algorithms in R with this near-real-time data from HANA. The net impact is that we will be able to take the capability which takes weeks and months to put together, and deliver just-in-time as the business is changing.

Piyush Bhargava, Distinguished Engineer IT, Cisco Systems (video)

”

Accelerate predictive analysis and scoring with in-database algorithms delivered out-of-the-box. Adapt the models frequently.

Execute R commands as part of overall query plan by transferring intermediate DB tables directly to R as vector-oriented data structures.

Predictive analytics across multiple data types and sources. (e.g.: Unstructured Text, Geospatial, Hadoop)

Top 10

“See” the future accurately in real-time

C4.5 decision tree

Weighted score tables

Regression

KNN classification

K-means ABC classification

Associate analysis: market basket

Apps

Virtual Tables

OLAP Unstructured

Predictive

Logic

R

Logic

Pre Process Pre Process Pre Process

1 Speed

2 Real-Time

3 Any Data

4 Any Source

6 Open

8 Prediction

9 Consolidation


10 Choice

7 Simplicity

Geospatial

https://www.youtube.com/watch?feature=player_embedded&v=HLg9L-2bkec


$

$

$

$

$

$

Web Application Server

Enterprise Search

Business Rule Management

Predictive Analytics

Planning

Geospatial

Data Warehouse Appliance

ETL

Event Processing

Multiple Databases

“ Pointing to Glass' Law (sourced to Roger Sessions of ObjectWatch), which states that "for every 25 percent increase in functionality of a system, there is a 100 percent increase in the complexity of that system," Gartner emphasizes the ability of an enterprise to get the most out of IT money spent.

Gartner

”

Top 10

De-layer, de-clutter. Consolidate!

Text Analytics / Mining / Unstructured Data

Development / Modeling Tools

Lif

ec

yc

le M

gm

t./

Ad

min

/M

on

ito

rin

g T

oo

ls

Simplify development, modeling and administration environments with Eclipse-based tool.

Reduce TCO by consolidating heterogeneous servers into SAP HANA servers to reduce hardware, lifecycle management, and maintenance.

Avoid hidden costs due to data quality, synchronization and latency.

Unified Development/Modeling/ Admin/Monitoring with Eclipse-based tool

SAP HANA

1 Speed

2 Real-Time

3 Any Data

4 Any Source

6 Open

8 Prediction

9 Consolidation


10 Choice

7 Simplicity

Database Cache

Data Warehouses


SAP HANA Appliance On Premise

SAP HANA One (Premium) Public Cloud

SAP HANA Enterprise Cloud Managed Private Cloud

Top 10

Choose and change deployment options any time

Limited Scale Any Scale Elastic Scale

SAP HANA

SAP HANA

Choose hardware (Intel x86 based architecture) from hardware vendors HP, IBM, Fujitsu, Cisco, Dell, NEC,

Hitachi, Huawei, and VCE as of July 2013.

Scale as required.

Real-time platform, infrastructure, and fully managed services from SAP or from our trusted

partners.

Bring your existing licenses to run all SAP HANA applications.

Mission critical, global 24x7 operations.

Start using SAP HANA right away.

Managed by Amazon Web Services (AWS), Korea Telecom, Portugal Telekom and

VM Ware.

60.5 GB instance size allowing for 30 GB of

data.

HANA One :

– 99¢ per hour. Pay as you use. Community Support.

HANA One Premium :

– USD 75,000 per year including SAP Enterprise Support.

SAP HANA

1 Speed

2 Real-Time

3 Any Data

4 Any Source

6 Open

8 Prediction

9 Consolidation


10 Choice

7 Simplicity


Definition: Public and Private Cloud and Managed Service Market View

http://www.idc.com/prodserv/FourPillars/Cloud/downloads/239772.pdf

IDC, 2013

IDC‘s Cloud Services Deployment Models



Definition: Public and Private Cloud and Managed Service Market View

* For on-premise: Software / Platform / Infrastructure


IDC, 2013

SaaS HANA Apps* HANA Enterprise Cloud** Successfactors, Ariba, SoD, ByD …

PaaS HANA

Appliance* HANA Cloud Platform

IaaS HANA One / Dev Edition

HANA Cloud Infrastructure

IDC‘s Cloud Services Deployment Models









Summary: SAP HANA In-Memory Platform Ideal platform for next-generation “Smart” applications

HTTP(S), OData, XML/A.

ODBC, JDBC, ODBO.

SQL, MDX.

Easier Consumption: Easier Development:

JavaScript, HTML 5.

Connect any programming language.

App/web services.

Decision table.

Easier Processing:

NLP, Predictive, R-Integration.

Spatial processing, ad-hoc OLAP views.

Data virtualization.

Easier Ingestion:

Replication, streaming, ETL/ELT.

Integration, data cleansing.

Personalized recommendation with machine learning,

predictive and rules

Natural language processing

Process any variety/volume (e.g.

unstructured)

Respond within predictable time windows

Key capabilities required for next-generation “Smart” applications:

SAP HANA is a high speed processing platform to enable:


What is a spatially enabled database?

The ability to store, process, manipulate, share, and

retrieve spatial data directly in the database

Allows for the ability to process spatial vector data

with spatial analytic functions:

Measurements – distance, surface, area, perimeter,

volume

Relationships – intersects, contains, within, adjacent,

touches

Operators – buffer, transform

Attributes – types, number of points

Can store and transform between various 2D/3D

coordinate systems

Vector and raster support

Complies with the ISO/IEC 13249-3 standard and

Open Geospatial Consortium (1999 SQL/MM

standard)

point line

polygon

Multi-polygon


Spatial Processing Architecture

Introducing in SAP HANA SP6:

New spatial data types (ST_POINT & ST_GEOMETRY)

Optimized data types for spatial

Extended SAP HANA SQL with spatial functions

Columnar storage of spatial data

Native spatial engine as part of Index Server

Access via SQL or Calculation Models/Views

Supports:

2D – Vector Types

Points, line-strings, polygons, compound polygons

Spatial functions

SRID (Spatial Reference ID’s)

Application development on XS with geo-content and mapping services


SAP HANA Spatial Ecosystem

Data Sources

Data Integration Tools Load tools:

• SAP Data Services

• SAP Event Stream Processor

Types & Functions:

• Point

• Linestring

• Polygon

• SRID metadata

• Spatial function library

• Clustering

• Spatial Joins

Engines:

• Indexserver

• Calc

• Spatial

• Attribute

• XS

Analytics GIS Visualization

Interfaces / Services

SQL /

Calculation Models

SAP Info Access

(HTML5)

Geo-Services:

• Geoservices

• Geocontent

odbc, jdbc, XS (InA, geoJSON, API, ODATA)

Views:

• Analytical

• Attribute

• Calculation

Geospatial Import/Export:

• Shapefile, csv, binary

• WKT / WKB Support

Data Access

SAP HANA

(OGC Compliant)

Applications

SAP Data Spatial Data Non-SAP Data Real-Time Data

Mobility

GIS


SAP HANA and Esri ArcGIS – Interoperability Vision

Mapping Services

Esri ArcGIS Server

Analytic Services Content Services

SAP HANA

Esri

QueryLayers

REST

Services

Esri ArcSDE

Geodatabase

Technology

Esri ArcGIS

Map creation, editing, and publishing

Geospatial location analytics

Geocontent and services

SAP HANA

Real-time in-memory columnar database

OGC Compliant

Spatial types and processing

Esri ArcGIS + SAP HANA

Scalable platform for real-time high-performing spatial and analytic processing

Integration of spatial and non-spatial data

and analytics to answer more questions

Lower TCO and TCD

Shapefile

Import /

Export

Internal

Spatial Data

Server

CVOM


Capabilities: SAP HANA spatial application development components include:

Location Services (on-premises or cloud), Geo-Content, Application Interfaces, Services

Allows for visualization, interaction, and exploration of spatial data in SAP HANA via maps

Supports HTML5 deployments for browser or iPad

Consumes SAP HANA models

NOT a general purpose BI or GIS tool!

Benefits: Quick development and deployment time

Low TCO & TCD and fast response times with 2-tier architecture

Components, content, and services included with SAP HANA; can also use other map svcs

SAP HANA Spatial Application Development

Quickly develop and deploy SAP HANA based spatial applications with

provided geo-content and map services via the native XS engine

SAP HANA XS

iPad/

Browser

SAP

HANA

HTML5

Application

Location

Services Maps

Geo-

coding Services

Spatial

Engine

Geo-

content


Vector spatial data types and functions

Import/export capability

BI/GIS interoperability

Geo-content and services

Geo-application development platform

Spatial Compliance

Advanced Spatial Capabilities Geodatabase and 3D Support

SAP HANA Spatial Roadmap

Full OGC compliance

3D type and function support

Raster support and processing

Support as a Geodatabase

Non-Geo visualization tool support

(Visual Enterprise)

Full integration of spatial data-types

Additional OGC features

Additional product libraries

Advanced spatial functions

Additional third-party interoperability

Application enhancements to support and

leverage spatial

Short-Term Mid-Term Long-Term This is the current state of planning and may be changed by SAP at any time.

Internal

Feedback, Q & A

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