Contenu connexe
Similaire à Autodesk Technical Webinar: SAP HANA in-memory database (20)
Plus de SAP PartnerEdge program for Application Development (20)
Autodesk Technical Webinar: SAP HANA in-memory database
- 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.
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
2
- 3. SAP HANA In-Memory Platform
Platform for next-generation “smart” applications
Developers
Data Scientists
Applications
& Tools
Business Users
Executives
Consumers
Industry | LoB | Consumer | Analytics | Social | Cloud | Mobile
Application Services
Application Server | UI Integration Services | Web Server
Processing Engine
Event Processing | Planning | Calculation | Predictive Analytics
Database Services
Transactions | Analytics | Partitioning
Compression | Availability | Encryption
Rules | Text Mining | Search | Application Function Libraries | Geospatial
Integration Services
Unified Administration |
Security Services
Development | Connectivity |
Lifecycle Management
Services
SAP HANA PLATFORM
Mobile | XaaS | High-volume Replication | Real-time Replication | Hadoop
SAP HANA is a completely re-imagined platform that transforms transactions, analytics, predictive, sentiment and spatial processing so
that businesses can operate in real time.
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
3
- 4. SAP HANA and Real-Time Data Platform
Architecture Overview
Data Scientists
Applications
&T
ools
Business Users
Executives
Consumers
Industry | LoB | Consumer | Analytics | Social | Cloud | Mobile
SAP
Replication
Technology
Application Services
Application Server | UI Integration Services | Web Server
Unified Administration |
Security Services
Development | Connectivity |
Lifecycle Management
Services
SAP HANA PLATFORM
Processing Engine
Planning | Calculation | Predictive Analytics
Database Services
Transactions | Analytics | Partitioning
Compression | Availability | Encryption
Rules | Text Mining | Search | Application Function Libraries
Integration Services
Mobile | Federation | High-volume Replication | Real-time Replication | Hadoop
SAP Data
Services
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
SAP ESP
SAP SQLA
SAP ASE
SAP IQ
Real-time Data Platform
Transact | Analyze | Deliver
Developers
4
- 5. Top 10
1
2
3
4
5
6
7
8
9
10
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.
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
5
- 6. 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!
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
6
- 7. The Bottleneck has Shifted…
Access to memory is 4 times slower than L3 cache, and 50 times slower than L1 cache…
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
7
- 8. 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
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
8
- 9. 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
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
9
- 10. 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.
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
10
- 11. Top 10
1
2
3
4
5
6
7
8
9
10
Speed
Real-Time
Any Data
Any Source
Predictable
Completion
Open
Simplicity
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.
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
11
- 12. Top 10
1
2
3
4
5
6
7
8
9
10
Speed
Real-Time
Any Data
Any Source
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)
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
12
- 13. 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
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
13
- 14. Top 10
1
2
3
4
5
6
7
8
9
10
Speed
Real-Time
Any Data
Any Source
Predictable
Completion
Open
Simplicity
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.
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
14
- 15. 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
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
15
- 16. 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
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
16
- 17. Top 10
1
2
3
4
5
6
7
8
9
10
Speed
Real-Time
Any Data
Any Source
Predictable
Completion
Open
Simplicity
Prediction
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
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
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).
17
- 18. 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
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
18
- 19. 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 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
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
AS ABAP
SID: ABC
SAP HANA
<HDB1>
Schema ABC
AS ABAP
SID: XYZ
SAP HANA
<HDB2>
Schema XYZ
n x Applications
AS ABAP
SID: ABC
AS ABAP
SID: XYZ
SAP HANA
<HDB>
Schema ABC
SAP HANA
<HDB>
Schema XYZ
19
- 20. Top 10
1
2
3
4
5
6
7
8
9
10
Speed
Real-Time
Any Data
Any Source
Predictable
Completion
Open
Simplicity
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
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
20
- 21. 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
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
21
- 22. Top 10
1
2
3
4
5
6
7
8
9
10
Speed
Real-Time
Any Data
Any Source
Predictable
Completion
Open
Simplicity
Prediction
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.
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
22
- 23. Compare HANA Web App Development To Classic Web Dev
Java + MySQL
lib
Java + HANA
R
HANA XS
R
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
23
- 24. Top 10
1
2
3
4
5
6
7
8
9
10
Speed
Real-Time
Any Data
Any Source
Predictable
Completion
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)
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
24
- 25. Top 10
1
2
3
4
5
6
7
8
9
10
Speed
Real-Time
Any Data
Any Source
Predictable
Completion
Open
Simplicity
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
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
25
- 26. Top 10
1
2
3
4
5
6
7
8
9
10
Speed
Real-Time
Any Data
Any Source
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.
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
26
- 27. 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
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
27
- 28. 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
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
28
- 29. 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.
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
29
- 31. 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)
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
31
- 32. 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
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
32
- 33. 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
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
Non-SAP Data
Spatial Data
Real-Time Data
GIS
33
- 34. 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
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
ArcSDE
Geodatabase
Technology
SAP HANA
Internal
34
- 35. 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
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
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
- 36. 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.
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
Internal
36