Autodesk Technical Webinar: SAP HANA in-memory database

SAP HANA Overview
Jan Teichmann, P&I HANA Product Management
November, 2013

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.

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

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Speed

Real-Time

Any Data

Any Source

Predictable
Completion

Open

Simplicity

Prediction

Consolidation

Choice

Sub-second response, no matter how complex
Core 1

Core N

1.5ns*
L1 Cache

4ns*
L2 Cache

CPU

CPU

15ns*

CPU

L3 Cache
60ns*

Memory
Bottleneck

Memory

Query Compressed Data

Copy into memory

Log
Data

Memory

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

Disk Storage

DB

Code
Storage

App

Log

Any Column
as Index

Parallelized Query

Storage

SAP HANA
(DB + App)
Data

 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.


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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!


6

The Bottleneck has Shifted…
 Access to memory is 4 times slower than L3 cache, and 50 times slower than L1 cache…


7

Intel Xeon – Hyper-threaded Cores, Huge Caches

L3

L2

Westmere-EX

ALU

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 taskswitching (when a thread is stalled, typically when it waits for memory)
Draw ing from: http://www.phys.uu.nl/~steen/web09/xeon.php


8

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


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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.

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Prediction

Consolidation

Choice

Real-time applications, zero latency

OLTP + OLAP in
SAP HANA

Traditional: OLTP and OLAP
Separate
24hr Old Data

ETL

SAP HANA

Current Data

Aggregate

12:00:00 AM

6 Hours

6:00:00 AM

10:00:00 AM

Immediate

10:00:01 AM

 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.


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Predictable
Completion

Open

Simplicity

Prediction

Consolidation

Choice

Process any data, in any combination, instantaneously with SQL
Embed sentiment fact extraction in same SQL

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.

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

Structure unstructured data
Use advanced text analytics, such as sentiment fact extraction,
to structure unstructured data.

CREATE COLUMN TABLE MYTABLE1
(
ID INTEGER,
KEYFIGURE DECIMAL(10,2),
SHAPE ST_GEOMETRY
); Embed fuzzy text search in same SQL

Analyze streaming data from integrated ESP in combination
with data in SAP HANA.

SELECT SHAPE.ST_AsGeoJSON() FROM MYTABLE1;

SQL

CREATE FULLTEXT INDEX i1 ON PSA_TRANSACTION( AMOUNT,
TRAN_DATE, POST_DATE, DESCRIPTION, CATEGORY_TEXT )
FUZZY SEARCH INDEX ON SYNC;

Process geospatial data

SAP
HANA

SELECT SCORE() AS SCR, * FROM
"SYSTEM"."PSA_TRANSACTION" WHERE CONTAINS (*,
'Sarvice', fuzzy) ORDER BY SCR DESC;

Any Data

Customer
Data

RFID

Smart Meter

Mobile

Point of Sale

Geospatial
Data

Machine
Data

Connected
Vehicles

Structured
Data

Clickstream

Social
Network

Text Data

“ ”

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)

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SAP Sybase Event Stream Processor
INPUT
STREAMS

Studio
(Authoring)

Sensor data

Transactions

SAP Event Stream
Processor

?

Dashboard
Application

Message Bus
Market Events
Database
Reference Data

•

Unlimited number of input streams
• Incoming data passes through “continuous queries” in real-time
• Output is event driven
• Scalable for extreme throughput, millisecond latency

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Open

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Prediction

Consolidation

Choice

Rapid data provisioning with data virtualization
Modeling and
Development Environment

Application
Data-Type Mapping & Compensate Missing Functions
in DB

SAP HANA
Merge Results

Application
SELECT from
DB(x)

SELECT from
DB(y)

One SQL Script

SELECT from
HIVE

Virtual Tables

Modeling
Environment

Modeling
Environment

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

 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.


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

Transactions + Analytics

 Remote real-time query processing
 Smart continuously self-tuning system
 Secure access to heterogeneous data
sources

SAP HANA

Heterogeneous data sources

IQ

Teradata






SAP HANA to Hadoop (Hive)
Teradata
SAP Sybase ASE
SAP Sybase IQ

ASE

Hadoop
SAP HANA

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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

Data
Federation

Data
Virtualization

Smart
Data Access

 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


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Consolidation

Choice

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

Scale Up

Extreme Linear Scalability

Scale Out

Query processing time (in seconds)
3.816

3.249

0.425

No disk

Distributed
computing

Multi-core /
parallelization

Partitioning

0.7

0.266

16 nodes
(100 billion rows)

0.491

51 nodes
(650 billion rows)
Query 1

Query 2

3.102

0.142

0.502

95 nodes
(1,200 billion rows)
Query 3

Sales and Distribution reports
Query 1: Single customer and material for one month
Query 2: Range of Customers and Materials for six months

“ ”

SAP HANA Performance, July 2012

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

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


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).
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Certified HANA Hardware – June 2013*

(only China)

XS: 128GB

X

X

S: 256GB

X

X

X

S+: 256GB

X

X

X

M: 512GB

X

X

X

M+: 512GB

X

X

X

X

L: 1.0TB

X

X

X

X

X

Scale Out (BW)

X

X

X

X

X

X

X

1/2/4

1/2/4

2/4

1

1

1/2/4

2

X

X

X

X

X

X

X

X

X

X

X

SoH: 1/2/4TB
High Availability

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X
X

planned

X

planned

DR – Storage
Repl.: Async
DR – Storage
Repl.: Sync

X

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

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Multi-SID on one SAP HANA hardware
Productive Systems

White-Listed Scenarios

Non-Productive Systems

„Classical“ scenario

“MCOD”

“MCOS”

Virtualization (on premise)

Appliance approach for
optimal performance

Multiple Components on one
Database

Multiple Components on one
System, multi-SID

 1 x Appliance

 1 x Appliance

 1 x Appliance

Virtualization technology separates
multiple OS images each containing
one HANA DB

 1 x HANA DB

 1 x HANA DB

 n x HANA DB

 n x Virtualized Appliances

 1 x DB schema

 n x DB schema

 n x DB schema

 n x HANA DB

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

 n x Applications


 n x DB schema

Prod. usage for white listed
scenarios allowed, e.g. SAP ERP
together with SAP Fraud
Management. See SAP notes
AS ABAP SID:
Application
1661202 and 1826100.
ABC
SID: XYZ

E.g. DEV and QA system on one
hardware. See SAP note
1681092.

AS ABAP
SID: ABC

SAP HANA
<HDB>
Schema ABC

SAP HANA

<HDB>
Schema ABC
Schema XYZ


AS ABAP
SID: ABC

SAP HANA
<HDB1>
Schema ABC

AS ABAP
SID: XYZ

SAP HANA
<HDB2>
Schema XYZ


AS ABAP
SID: ABC

AS ABAP
SID: XYZ

SAP HANA
<HDB>
Schema ABC

SAP HANA
<HDB>
Schema XYZ

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Prediction

Consolidation

Choice

Bring your own code to an open platform
Browser / Mobile

Web JS Lib

Third Party
&
Custom Application

Data Viz Lib

http(s),OData/JSON

Web App Server

ODBO

ODBC, JDBC

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

 Easy to bring data into HANA.

– Reuse current data sources with Data
Virtualization.

Any HTML5/JS Library

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

DB Services
Stored Procedure

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

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

SAP HANA
App Services
(Web Server)

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

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

Virtual Tables

SQL Script

Real-time Replication

Import

CSV, Binary, shapefile,
WKT and WKB files


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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-SignOn (SSO) capabilities

•

Several (growing number of) Cloud Service Providers

•

http://www.saphana.com/community/blogs/blog/2
013/09/24/engineering-open-appliances-for-highperformance-without-lock-in


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Consolidation

Choice

Transformative power, simplified programming
Browser / Mobile

Browser / Mobile

+

+

Web JS Lib

HTML5 /JS
Libraries

Data Viz Lib
http(s)

Web App Server

http(s), OData / JSON

App Logic
App Logic
App Logic

App Logic
App Logic

App Logic

SAP HANA

BRM

App Services
(Web Server)

SQL
App Logic
App Logic
App Logic

Text Mining

http(s)

App Logic
App Logic
App Logic

DB Server

Predictive

Java Script

DB Server

OLAP

Text Mining

Stored
Procedures

Predictive

Aggregate

Standard Table:

+

OData

Procedural App Logic
DB-oriented Logic
R Integration
Decision Tables

SQL Scripts

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.

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Compare HANA Web App Development To Classic Web Dev

Java + MySQL

lib

Java + HANA
R

HANA XS
R

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Open

Simplicity

Prediction

Consolidation

Choice

“See” the future accurately in real-time
Apps

Apps

KNN classification

Logic
Predictive

SQL Script

Logic
R

(Optimized Query Plan)

R Engine
Pre Process

Pre Process

Pre Process

R-scripts

PAL

Unstructured
Geospatial

K-means

Associate analysis:
market basket

ABC classification

Weighted score
tables

Regression

Virtual Tables
C4.5 decision tree

OLAP

Unstructured

 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)

“ ”

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-intime as the business is changing.
Piyush Bhargava, Distinguished Engineer IT, Cisco Systems (video)

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Open

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Prediction

Consolidation

Choice

De-layer, de-clutter. Consolidate!

Lifecycle Mgmt./Admin/Monitoring Tools

Development / Modeling Tools

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

Event Processing

Enterprise Search
$
Business Rule Management

$

$

Data Warehouse Appliance

Planning

SAP
HANA

$ Data Warehouses

Text Analytics / Mining / Unstructured Data

$

Predictive Analytics
Geospatial

$

ETL

Web Application Server

Multiple Databases

Database Cache

 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.

“ ”

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


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Predictable
Completion

Open

Simplicity

Prediction

Consolidation

Choice

Choose and change deployment options any time
Limited Scale

Any Scale

SAP
HANA

SAP
HANA

SAP HANA One (Premium)
Public Cloud
 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.

SAP HANA Appliance
On Premise
 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.

Elastic Scale

SAP
HANA

SAP HANA Enterprise Cloud
Managed Private Cloud
 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.

 HANA One Premium :
– USD 75,000 per year including SAP Enterprise
Support.


26

Definition: Public and Private Cloud and Managed Service
Market View
IDC‘s Cloud Services Deployment Models

IDC, 2013

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

27

Definition: Public and Private Cloud and Managed Service
Market View
IDC‘s Cloud Services Deployment Models

SaaS
PaaS

HANA Apps*

HANA Enterprise Cloud**

HANA
Appliance*

IaaS

Successfactors, Ariba, SoD, ByD …
HANA Cloud Platform
HANA One / Dev Edition
HANA Cloud Infrastructure

IDC, 2013
* For on-premise: Software / Platform / Infrastructure
http://www.idc.com/prodserv/FourPillars/Cloud/downloads/239772.pdf

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

Personalized recommendation
with machine learning,
predictive and rules

Natural language
processing

Process any
variety/volume (e.g.
unstructured)

Respond within
predictable time windows

SAP HANA is a high speed processing platform to enable:
Easier
Processing:

Easier
Ingestion:

Easier Consumption:

Easier Development:

 NLP, Predictive,
R-Integration.
 Spatial processing, ad-hoc
OLAP views.
 Data virtualization.

 Replication,
streaming, ETL/ELT.
 Integration, data
cleansing.

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

 JavaScript, HTML 5.
 Connect any programming
language.
 App/web services.
 Decision table.


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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:

Multi-polygon
point

line
polygon

 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)

31

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


32

SAP HANA Spatial Ecosystem
Analytics

Visualization

Applications

Interfaces / Services

SAP Info Access
(HTML5)

Mobility

odbc, jdbc, XS (InA, geoJSON, API, ODATA)
SQL /
Calculation Models

Data Access
Types & Functions:
• Point
• Linestring
• Polygon
• SRID metadata
• Spatial function library

SAP HANA
(OGC Compliant)

Data Integration Tools

GIS

Load tools:
• SAP Data Services
• SAP Event Stream Processor

• Clustering
• Spatial Joins

Engines:
• Indexserver
• Calc
• Spatial
• Attribute
• XS

Geo-Services:
• Geoservices
• Geocontent

Views:
• Analytical
• Attribute
• Calculation

Geospatial Import/Export:
• Shapefile, csv, binary
• WKT / WKB Support

Data Sources
SAP Data

Non-SAP Data

Spatial Data

Real-Time Data

GIS

33

SAP HANA and Esri ArcGIS – Interoperability Vision
Esri ArcGIS
 Map creation, editing, and publishing

Esri ArcGIS Server

 Geospatial location analytics

Mapping Services

Analytic Services

Content Services

 Geocontent and services

Esri
QueryLayers

Spatial Data
Server

REST
Services

SAP HANA
 Real-time in-memory columnar database
 OGC Compliant

CVOM
Shapefile

 Spatial types and processing

Esri ArcGIS + SAP HANA

Import /
Export

 Scalable platform for real-time highperforming spatial and analytic processing

Esri

 Integration of spatial and non-spatial data
and analytics to answer more questions
 Lower TCO and TCD


ArcSDE

Geodatabase
Technology

SAP HANA
Internal

34

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
Capabilities:
HTML5
Application

iPad/
Browser
SAP HANA XS
Spatial
Engine

Maps

Geocoding

Geocontent

SAP
HANA

Location
Services Services


 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

35

SAP HANA Spatial Roadmap

Advanced Spatial Capabilities

Geodatabase and 3D Support

Spatial Compliance
 Full OGC compliance
 Full integration of spatial data-types
 Vector spatial data types and functions

 Additional OGC features

 Import/export capability

 Additional product libraries

 BI/GIS interoperability

 Advanced spatial functions

 Geo-content and services

 Additional third-party interoperability

 Geo-application development platform

 3D type and function support

 Application enhancements to support and
leverage spatial

Short-Term

Mid-Term

 Raster support and processing
 Support as a Geodatabase
 Non-Geo visualization tool support
(Visual Enterprise)

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


Internal

36

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