SAP High-Performance Analytic
Appliance 1.0 (SAP HANA)
A First Look At The System Architecture
Marc Bernard
SAP Technology...
© 2011 SAP AG. All rights reserved. / Page 2
Disclaimer
This presentation outlines our general product direction and shoul...
© 2011 SAP AG. All rights reserved. / Page 3
Agenda
1. Architecture Overview
2. Row Store
3. Column Store
4. Persistency L...
© 2011 SAP AG. All rights reserved. / Page 4
ERP
Architecture Overview
In-Memory Computing Engine and Surroundings
ERP DB
...
© 2011 SAP AG. All rights reserved. / Page 5
ERP
Architecture Overview
The Engine
LogERP DB
Clients (planned, e.g.) SBOP E...
© 2011 SAP AG. All rights reserved. / Page 6
ERP
Architecture Overview
Loading Data into SAP HANA
ERP DB
In-Memory Computi...
© 2011 SAP AG. All rights reserved. / Page 7
ERP
Architecture Overview
Data Modeling
ERP DB
In-Memory Computing Engine
Req...
© 2011 SAP AG. All rights reserved. / Page 8
Clients (planned, e.g.)
ERP
Architecture Overview
Reporting
ERP DB
In-Memory ...
© 2011 SAP AG. All rights reserved. / Page 9
ERP
Architecture Overview
Administration
ERP DB
In-Memory Computing Engine
Re...
© 2011 SAP AG. All rights reserved. / Page 10
DB Server
SAP High-Performance Analytic Appliance 1.0
SAP HANA
JDBC ODBC ODB...
© 2011 SAP AG. All rights reserved. / Page 11
Request Processing and Execution Control
Conceptual View
Standard SQL
Proces...
© 2011 SAP AG. All rights reserved. / Page 12
Calc Engine for Dummies
The easiest way to think of Calculation Models is to...
© 2011 SAP AG. All rights reserved. / Page 13
Calc Engine for Dummies
Example
© 2011 SAP AG. All rights reserved. / Page 14
Agenda
1. Architecture Overview
2. Row Store
3. Column Store
4. Persistency ...
© 2011 SAP AG. All rights reserved. / Page 15
In-Memory Computing Engine
High Level Architecture
Row Store
One of the
rela...
© 2011 SAP AG. All rights reserved. / Page 16
Row Store Architecture
Row Store Block Diagram
Row Store Block Diagram
Trans...
© 2011 SAP AG. All rights reserved. / Page 17
Row Store Architecture
Highlights
Write Operations
Mainly go into “Transacti...
© 2011 SAP AG. All rights reserved. / Page 18
Indexes for Row Store Tables
Primary Index / Row ID / Index Persistence
Each...
© 2011 SAP AG. All rights reserved. / Page 19
Agenda
1. Architecture Overview
2. Row Store
3. Column Store
4. Persistency ...
© 2011 SAP AG. All rights reserved. / Page 20
In-Memory Computing Engine
High Level Architecture
Column Store
One of the r...
© 2011 SAP AG. All rights reserved. / Page 21
Column Store Architecture
Column Store Block Diagram
Column Store Block Diag...
© 2011 SAP AG. All rights reserved. / Page 22
Column Store
Highlights
Storage Separation (Main & Delta)
Enables high compr...
© 2011 SAP AG. All rights reserved. / Page 23
Column Store
Delta Management
Delta Merge Operation
Purpose
To move changes ...
© 2011 SAP AG. All rights reserved. / Page 24
Agenda
1. Architecture Overview
2. Row Store
3. Column Store
4. Persistency ...
© 2011 SAP AG. All rights reserved. / Page 25
Persistence Layer
Purpose and Scope
Why Does An In-memory Database Need A Pe...
© 2011 SAP AG. All rights reserved. / Page 26
Persistence Layer
System Restart and Population of In-memory Stores
Actions ...
© 2011 SAP AG. All rights reserved. / Page 27
Agenda
1. Architecture Overview
2. Row Store
3. Column Store
4. Persistency ...
© 2011 SAP AG. All rights reserved. / Page 28
Row Store vs. Column Store
When to Use Which Store
Modeling Only Possible Fo...
© 2011 SAP AG. All rights reserved. / Page 29
SAP In-Memory Computing Studio
Look and Feel
Navigator
View
Quick Launch
Vie...
© 2011 SAP AG. All rights reserved. / Page 30
SAP In-Memory Computing Studio
Features
Information Modeler Features
Modelin...
© 2011 SAP AG. All rights reserved. / Page 31
Modeling Process Flow
Import Source
System
metadata
• Physical tables
are cr...
© 2011 SAP AG. All rights reserved. / Page 32
SAP In-Memory Computing Studio
Terminology
Information Modeler Terminology
D...
© 2011 SAP AG. All rights reserved. / Page 33
SAP In-Memory Computing Studio
Navigator View - Default Catalog
HANA Instanc...
© 2011 SAP AG. All rights reserved. / Page 34
SAP In-Memory Computing Studio
Navigator View - Information Models
Informati...
© 2011 SAP AG. All rights reserved. / Page 35
Attribute Views
Attribute View
What is an Attribute View?
Attributes add con...
© 2011 SAP AG. All rights reserved. / Page 36
Analytical View
Analytical View
An Analytical View can be regarded as a “cub...
© 2011 SAP AG. All rights reserved. / Page 37
Analytical View
Analytical View: Data Preview
There are three main views one...
© 2011 SAP AG. All rights reserved. / Page 38
Calculation View (Scripting)
Calculation View
Define Table Output Structure
...
© 2011 SAP AG. All rights reserved. / Page 39
SAP In-Memory Computing Studio
Pre-Delivered Administration Console
Navigato...
© 2011 SAP AG. All rights reserved. / Page 40
Agenda
1. Architecture Overview
2. Row Store
3. Column Store
4. Persistency ...
© 2011 SAP AG. All rights reserved. / Page 41
Thank you!
© 2011 SAP AG. All rights reserved. / Page 42
Further Information on
SAP HANA and In-Memory Technologies
In-Memory Computi...
© 2011 SAP AG. All rights reserved. / Page 43
No part of this publication may be reproduced or transmitted in any form or ...
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it is a new in memory software which has been a boon to the mnc.it is a type of database based on in memory computingit has not only solved the problem of big data but also store unstructured data as well.

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Sap%20 high performance%20analytic%20application%201.0%20%e2%80%93%20a%20first%20look%20at%20the%20system%20architecture%20-%20webinar%20presentation

  1. 1. SAP High-Performance Analytic Appliance 1.0 (SAP HANA) A First Look At The System Architecture Marc Bernard SAP Technology Regional Implementation Group February 2011
  2. 2. © 2011 SAP AG. All rights reserved. / Page 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.
  3. 3. © 2011 SAP AG. All rights reserved. / Page 3 Agenda 1. Architecture Overview 2. Row Store 3. Column Store 4. Persistency Layer 5. Modeling 6. Q&A
  4. 4. © 2011 SAP AG. All rights reserved. / Page 4 ERP Architecture Overview In-Memory Computing Engine and Surroundings ERP DB In-Memory Computing Engine Clients (planned, e.g.) BI4 Explorer Dashboard Design SAP BI4 universes (WebI,...) Request Processing / Execution Control MS Excel BI4 Analysis SQL Parser MDX SQL Script Calc Engine Transaction Manager Session Management Relational Engines Row Store Column Store Persistence LayerPage Management Logger Disk Storage Log VolumesData Volumes Authorization Manager Metadata Manager In-Memory Computing Studio Administration Modeling Load Controller Replication Agent Replication Server SAP Business Objects BI4 Data Services Designer SBO BI4 servers ( program for client) SBO BI4 Information Design Tool Other Source Systems SAP NetWeaver BW 3rd Party Data Services
  5. 5. © 2011 SAP AG. All rights reserved. / Page 5 ERP Architecture Overview The Engine LogERP DB Clients (planned, e.g.) SBOP Explorer 4.0 Xcelsius SAP BI universes (WebI,...) MS Excel SBOP Analysis IMC Studio Administration Modeling Load Controller Replication Agent Business Objects Enterprise Data Services Designer SBO server programs for clients SBO Information Design Tool Other Source Systems SAP NetWeaver BW 3rd Party Data Services In-Memory Computing Engine Request Processing / Execution Control SQL Parser MDX SQL Script Calc Engine Transaction Manager Session Management Relational Engines Row Store Column Store Persistence LayerPage Management Logger Disk Storage Log VolumesData Volumes Authorization Manager Metadata Manager Replication Server
  6. 6. © 2011 SAP AG. All rights reserved. / Page 6 ERP Architecture Overview Loading Data into SAP HANA ERP DB In-Memory Computing Engine Request Processing / Execution Control SQL Parser MDX SQL Script Calc Engine Transaction Manager Session Management Relational Engines Row Store Column Store Persistence LayerPage Management Logger Disk Storage Log VolumesData Volumes Authorization Manager Metadata Manager In-Memory Computing Studio Administration Modeling Load Controller Replication Agent Replication Server Business Objects Enterprise Data Services Designer SBO BI4 servers ( program for client) SBO Information Design Tool Other Source Systems SAP NetWeaver BW 3rd Party Data Services Clients (planned, e.g.) BI4 Explorer Dashboard Design SAP BI4 universes (WebI,...) MS Excel BI4 Analysis
  7. 7. © 2011 SAP AG. All rights reserved. / Page 7 ERP Architecture Overview Data Modeling ERP DB In-Memory Computing Engine Request Processing / Execution Control SQL Parser MDX SQL Script Calc Engine Transaction Manager Session Management Relational Engines Row Store Column Store Persistence LayerPage Management Logger Disk Storage Log VolumesData Volumes Authorization Manager Metadata Manager In-Memory Computing Studio Administration Modeling Load Controller Replication Agent Replication Server Business Objects Enterprise Data Services Designer SBO BI4 servers ( program for client) SBO Information Design Tool Other Source Systems SAP NetWeaver BW 3rd Party Data Services Clients (planned, e.g.) BI4 Explorer Dashboard Design SAP BI4 universes (WebI,...) MS Excel BI4 Analysis
  8. 8. © 2011 SAP AG. All rights reserved. / Page 8 Clients (planned, e.g.) ERP Architecture Overview Reporting ERP DB In-Memory Computing Engine Request Processing / Execution Control SQL Parser MDX SQL Script Calc Engine Transaction Manager Session Management Relational Engines Row Store Column Store Persistence LayerPage Management Logger Disk Storage Log VolumesData Volumes Authorization Manager Metadata Manager In-Memory Computing Studio Administration Modeling Load Controller Replication Agent Replication Server Business Objects Enterprise Data Services Designer SBO BI4 servers ( program for client) SBO Information Design Tool Other Source Systems SAP NetWeaver BW 3rd Party Data Services BI4 Explorer Dashboard Design SAP BI4 universes (WebI,...) MS Excel BI4 Analysis
  9. 9. © 2011 SAP AG. All rights reserved. / Page 9 ERP Architecture Overview Administration ERP DB In-Memory Computing Engine Request Processing / Execution Control SQL Parser MDX SQL Script Calc Engine Transaction Manager Session Management Relational Engines Row Store Column Store Persistence LayerPage Management Logger Disk Storage Log VolumesData Volumes Authorization Manager Metadata Manager In-Memory Computing Studio Administration Modeling Load Controller Replication Agent Replication Server Business Objects Enterprise Data Services Designer SBO BI4 servers ( program for client) SBO Information Design Tool Other Source Systems SAP NetWeaver BW 3rd Party Data Services Clients (planned, e.g.) BI4 Explorer Dashboard Design SAP BI4 universes (WebI,...) MS Excel BI4 Analysis
  10. 10. © 2011 SAP AG. All rights reserved. / Page 10 DB Server SAP High-Performance Analytic Appliance 1.0 SAP HANA JDBC ODBC ODBO SQL DBC SAP In-Memory Computing Engine Replication Server SAP In-Memory Computing Studio SAP Business Application Replication Agent SAP BusinessObjects Data Services 4.0 Any source SAP BusinessObjects BI 4.0 Repository SAP BusinessObjects BI clients SQL MDX BICS Authentication Contentmgmt sync Admin&model load (optional) (optional) (optional) (existing)
  11. 11. © 2011 SAP AG. All rights reserved. / Page 11 Request Processing and Execution Control Conceptual View Standard SQL Processed directly by DB engine SQL Script, MDX and planning engine interface Domain-specific programming languages or models Converted into calculation models Calc Engine Create logical execution plan for calculation models Execute user defined functions Relational Engine DB optimizer produces physical executing plan Access to row and column store
  12. 12. © 2011 SAP AG. All rights reserved. / Page 12 Calc Engine for Dummies The easiest way to think of Calculation Models is to see them as dataflow graphs, where the modeler can define data sources as inputs and different operations (join, aggregation, projection,…) on top of them for data manipulations. The Calculation Engine will break up a model, for example some SQL Script, into operations that can be processed in parallel (rule based model optimizer). Then these operations will be passed to the database optimizer which will determine the best plan for accessing row or column stores (algebraic transformations and cost based optimizations based on database statistics).
  13. 13. © 2011 SAP AG. All rights reserved. / Page 13 Calc Engine for Dummies Example
  14. 14. © 2011 SAP AG. All rights reserved. / Page 14 Agenda 1. Architecture Overview 2. Row Store 3. Column Store 4. Persistency Layer 5. Modeling 6. Q&A
  15. 15. © 2011 SAP AG. All rights reserved. / Page 15 In-Memory Computing Engine High Level Architecture Row Store One of the relational engines Interfaced from calculation / execution layer Pure in-memory store Persistence managed in persistence layer SAP in-memory computing engine HANA
  16. 16. © 2011 SAP AG. All rights reserved. / Page 16 Row Store Architecture Row Store Block Diagram Row Store Block Diagram Transactional Version Memory Contains temporary versions Needed for Multi-Version Concurrency Control (MVCC) Segments Contain the actual data (content of row-store tables) in pages Page Manager Memory allocation Keeping track of free/used pages Version Memory Consolidation Think ‘garbage collector for MVCC’ Persistence Layer Invoked in write operations (log) And in performing savepoints checkpoint writer
  17. 17. © 2011 SAP AG. All rights reserved. / Page 17 Row Store Architecture Highlights Write Operations Mainly go into “Transactional Version Memory” “INSERT” also writes to Persisted Segment Read Operations Write Operations Transactional Version Memory Main Memory Persisted Segment Data that may be seen by all active transactions Recent versions of changed records Version Memory Consolidation Version Consolidation Moves “visible version” from Transaction Version Memory into Persisted Segment (based on Commit ID) Clears “outdated” record versions from Transactional Version Memory Memory Handling Row store tables are linked list of memory pages Pages are grouped in segments Page size: 16 KB Persisted Segment Contains data that may be seen by any ongoing transaction Data that has been committed before any active transaction was started)
  18. 18. © 2011 SAP AG. All rights reserved. / Page 18 Indexes for Row Store Tables Primary Index / Row ID / Index Persistence Each row-store table has a primary index Primary index maps ROW ID primary key of table ROW ID: a number specifying for each record its memory segment and page How to find the memory page for a table record? A structure called “ROW ID” contains the segment and the page for the record The page can then be searched for the records based on primary key ROW ID is part of the primary index of the table Secondary indexes can be created if needed Persistence of indexes in row store Indexes in row store only exist in memory No persistence of index data Index definition stored with table metadata Indexes filled on-the-fly when system loads tables into memory on system start-up
  19. 19. © 2011 SAP AG. All rights reserved. / Page 19 Agenda 1. Architecture Overview 2. Row Store 3. Column Store 4. Persistency Layer 5. Modeling 6. Q&A
  20. 20. © 2011 SAP AG. All rights reserved. / Page 20 In-Memory Computing Engine High Level Architecture Column Store One of the relational engines Interfaced from calculation / execution layer Pure in-memory store Persistence managed in persistence layer Optimized for high performance of read operation Good performance of write operations Efficient data compression SAP in-memory computing engine HANA
  21. 21. © 2011 SAP AG. All rights reserved. / Page 21 Column Store Architecture Column Store Block Diagram Column Store Block Diagram Optimizer and Executor Handles queries and execution plan Main and Delta Storage Compressed data for fast read Delta data for fast write Asynchronous delta merge Consistent View Manager Transaction Manager Persistence Layer
  22. 22. © 2011 SAP AG. All rights reserved. / Page 22 Column Store Highlights Storage Separation (Main & Delta) Enables high compression and high write performance at the same time Delta Merge Operation See next slide Read Operations Write Operations Main Main Memory Delta Write optimized Compressed and Read optimized Read Operations Always have to read from both main & delta storages and merge the results. Engine uses multi version concurrency control (MVCC) to ensure consistent read operations. Data Compression in Main Storage Compression by creating dictionary and applying further compression methods Speed up Data load into CPU cache Equality check Search The compression is computed during delta merge operation. Write Operations Only in delta storage because write optimized. The update is performed by inserting a new entry into the delta storage.
  23. 23. © 2011 SAP AG. All rights reserved. / Page 23 Column Store Delta Management Delta Merge Operation Purpose To move changes in delta storage into the compressed and read optimized main storage Characteristics Happens asynchronously Even during merge operation the columnar table will be still available for read and write operations To fulfil this requirement, a second delta and main storage are used internally Read Operations Write Operations Main Before Merge Delta Read Operations Write Operations Main New After Merge Delta New Read Operations Write Operations Main During Merge Main New Delta New Delta Merge Operations
  24. 24. © 2011 SAP AG. All rights reserved. / Page 24 Agenda 1. Architecture Overview 2. Row Store 3. Column Store 4. Persistency Layer 5. Modeling 6. Q&A
  25. 25. © 2011 SAP AG. All rights reserved. / Page 25 Persistence Layer Purpose and Scope Why Does An In-memory Database Need A Persistence Layer? Main Memory is volatile. What happens upon… Database restart? Power outage? ... Data needs to be stored in a non-volatile way Backup and restore SAP in-memory computing engine offers one persistence layer which is used by row store and column store Regular “savepoints” full persisted image of DB at time of savepoint Logs capturing all DB transactions since last savepoint (redo logs and undo logs written) restore DB from latest savepoint onwards Ability to create "snapshots" used for backups
  26. 26. © 2011 SAP AG. All rights reserved. / Page 26 Persistence Layer System Restart and Population of In-memory Stores Actions During System Restart Last savepoint must be restored plus… Undo logs must be read for uncommitted transactions saved with last savepoint Redo logs for committed transactions since last savepoint Complete content of row store is loaded into memory Column store tables may be marked for preload or not Only tables marked for preload are loaded into memory during startup If table is marked for loading on demand, the restore procedure is invoked on first access
  27. 27. © 2011 SAP AG. All rights reserved. / Page 27 Agenda 1. Architecture Overview 2. Row Store 3. Column Store 4. Persistency Layer 5. Modeling 6. Q&A
  28. 28. © 2011 SAP AG. All rights reserved. / Page 28 Row Store vs. Column Store When to Use Which Store Modeling Only Possible For Column Tables This answers the frequently asked question: "Where should I put a table – row store or column store?" Information Modeler only works with column tables Replication server creates tables in column store per default Data Services creates tables in column store per default SQL to create column table: "CREATE COLUMN TABLE ..." Store can be changed with "ALTER TABLE …" System Tables Are Created Where They Fit Best Administrative tables in row store: Schema SYS caches, administrative tables of engine Tables from statistics server Administrative tables in column store: Schema _SYS_BI metadata of created views + master data for MDX Schema _SYS_BIC some generated tables for MDX Schema _SYS_REPO e.g. lists of active/modified versions of models
  29. 29. © 2011 SAP AG. All rights reserved. / Page 29 SAP In-Memory Computing Studio Look and Feel Navigator View Quick Launch View Properties View
  30. 30. © 2011 SAP AG. All rights reserved. / Page 30 SAP In-Memory Computing Studio Features Information Modeler Features Modeling No materialized aggregates Database views Choice to publish and consume at 4 levels of modeling Attribute View, Analytic View, Analytic View enhanced with Attribute View, Calculation View Data Preview Physical tables Information Models Import/Export Models Data Source schemas (metadata) – mass and selective load Landscapes Data Provisioning for SAP Business Applications (both initial load and replication) Analytic Privileges / Security
  31. 31. © 2011 SAP AG. All rights reserved. / Page 31 Modeling Process Flow Import Source System metadata • Physical tables are created dynamically (1:1 schema definition of source system tables) Provision Data • Physical tables are loaded with content. Create Information Models • Database Views are created • Attribute Views • Analytic Views • Calculation Views Deploy • Column views are created and activated Consume • Consume with choice of client tools • BICS, SQL, MDX
  32. 32. © 2011 SAP AG. All rights reserved. / Page 32 SAP In-Memory Computing Studio Terminology Information Modeler Terminology Data Attributes – descriptive data (known as Characteristics SAP BW terminology) Measures – data that can be quantified and calculated (known as key figures in SAP BW) Views Attribute Views – i.e. dimensions Analytic Views – i.e. cubes Calculation Views – similar to virtual provider with services concept in BW Hierarchies Leveled – based on multiple attributes Parent-child hierarchy Analytic Privilege – security object
  33. 33. © 2011 SAP AG. All rights reserved. / Page 33 SAP In-Memory Computing Studio Navigator View - Default Catalog HANA Instance (<USER>) HANA Server Name and Instance Number User Database schema Schema Content: Column Views, Functions, Tables, Views
  34. 34. © 2011 SAP AG. All rights reserved. / Page 34 SAP In-Memory Computing Studio Navigator View - Information Models Information Models organized in Packages Attribute Views, Analytic Views, Calculation Views, Analytic Privileges organised in folders
  35. 35. © 2011 SAP AG. All rights reserved. / Page 35 Attribute Views Attribute View What is an Attribute View? Attributes add context to data. Attributes are modeled using Attribute Views. Can be regarded as Master Data tables Can be linked to fact tables in Analytical Views A measure e.g. weight can be defined as an attribute. Table Joins and Properties Join Types leftOuter, rightOuter, fullOuter, textTable Cardinality 1:1 N:1 1:N Language Column
  36. 36. © 2011 SAP AG. All rights reserved. / Page 36 Analytical View Analytical View An Analytical View can be regarded as a “cube”. Analytical Views does not store any data. The data is stored in column store or table view based on the Analytical View Structure. Attribute and Measures Can create Attribute Filters Must have at least one Attribute Must have at least one Measure Can create Restricted Measures Can create Calculated Measures Can rename Attribute and Measures on the property tab
  37. 37. © 2011 SAP AG. All rights reserved. / Page 37 Analytical View Analytical View: Data Preview There are three main views one can select from when previewing data. Raw Data – table format of data Distinct Values – graphical and text format identifying unique values Analysis – select fields (attributes and measures) to display in graphical format.
  38. 38. © 2011 SAP AG. All rights reserved. / Page 38 Calculation View (Scripting) Calculation View Define Table Output Structure Write SQL Statement. Ensure that the selected fields corresponds to previously defined Output table structure of the function. Example : SQL_A = SELECT MATNR, KUNNR, …. FROM <COPA_ACTUAL_ANALYTICAL VIEW 1> SQL_P = SELECT MATTNR_KUNNR, … FROM <COPA_PROJECTED_ANALYTICAL VIEW 2> TABLE_OUTPUT_STRUCTURE = SELECT * FROM <SQL_A> UNION SELECT * FROM <SQL_P>;
  39. 39. © 2011 SAP AG. All rights reserved. / Page 39 SAP In-Memory Computing Studio Pre-Delivered Administration Console Navigator View Properties View Administration View
  40. 40. © 2011 SAP AG. All rights reserved. / Page 40 Agenda 1. Architecture Overview 2. Row Store 3. Column Store 4. Persistency Layer 5. Modeling 6. Q&A
  41. 41. © 2011 SAP AG. All rights reserved. / Page 41 Thank you!
  42. 42. © 2011 SAP AG. All rights reserved. / Page 42 Further Information on SAP HANA and In-Memory Technologies In-Memory Computing http://www.sap.com/platform/in-memory-computing Real-Real Time Business with HANA http://www.youtube.com/watch?v=uUqtUw-m7mQ SAP Community Network Topic Page http://www.sdn.sap.com/irj/sdn/in-memory SAP Community Forum http://forums.sdn.sap.com/forum.jspa?forumID=491 The SAP NetWeaver BW – SAP HANA Relationship http://www.sdn.sap.com/irj/scn/weblogs?blog=/pub/wlg/21575 SAP HANA Ramp-Up Knowledge Transfer (login required) http://service.sap.com/rkt-hana SAP HANA Documentation (login required during ramp-up) https://cw.sdn.sap.com/cw/community/docupedia/hana
  43. 43. © 2011 SAP AG. All rights reserved. / Page 43 No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP AG. The information contained herein may be changed without prior notice. Some software products marketed by SAP AG and its distributors contain proprietary software components of other software vendors. Microsoft, Windows, Excel, Outlook, and PowerPoint are registered trademarks of Microsoft Corporation. IBM, DB2, DB2 Universal Database, System i, System i5, System p, System p5, System x, System z, System z10, System z9, z10, z9, iSeries, pSeries, xSeries, zSeries, eServer, z/VM, z/OS, i5/OS, S/390, OS/390, OS/400, AS/400, S/390 Parallel Enterprise Server, PowerVM, Power Architecture, POWER6+, POWER6, POWER5+, POWER5,POWER, OpenPower, PowerPC, BatchPipes, BladeCenter, System Storage, GPFS, HACMP, RETAIN, DB2 Connect, RACF, Redbooks, OS/2, Parallel Sysplex, MVS/ESA, AIX, Intelligent Miner, WebSphere, Netfinity, Tivoli and Informix are trademarks or registered trademarks of IBM Corporation. Linux is the registered trademark of Linus Torvalds in the U.S. and other countries. Adobe, the Adobe logo, Acrobat, PostScript, and Reader are either trademarks or registered trademarks of Adobe Systems Incorporated in the United States and/or other countries. Oracle is a registered trademark of Oracle Corporation. UNIX, X/Open, OSF/1, and Motif are registered trademarks of the Open Group. Citrix, ICA, Program Neighborhood, MetaFrame, WinFrame, VideoFrame, and MultiWinare trademarks or registered trademarks of Citrix Systems, Inc. HTML, XML, XHTML and W3C are trademarks or registered trademarks of W3C®, World Wide Web Consortium, Massachusetts Institute of Technology. Java is a registered trademark of Sun Microsystems, Inc. JavaScript is a registered trademark of Sun Microsystems, Inc., used under license for technology invented and implemented by Netscape. SAP, R/3, SAP NetWeaver, Duet, PartnerEdge, ByDesign, Clear Enterprise, SAP BusinessObjects Explorer and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP AG in Germany and other countries. Business Objects and the Business Objects logo, BusinessObjects, Crystal Reports, Crystal Decisions, Web Intelligence, Xcelsius, and other Business Objects products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP France in the United States and in other countries. All other product and service names mentioned are the trademarks of their respective companies. Data contained in this document serves informational purposes only. National product specifications may vary. The information in this document is proprietary to SAP. No part of this document may be reproduced, copied, or transmitted in any form or for any purpose without the express prior written permission of SAP AG. This document is a preliminary version and not subject to your license agreement or any other agreement with SAP. This document contains only intended strategies, developments, and functionalities of the SAP® product and is not intended to be binding upon SAP to any particular course of business, product strategy, and/or development. Please note that this document is subject to change and may be changed by SAP at any time without notice. SAP assumes no responsibility for errors or omissions in this document. SAP does not warrant the accuracy or completeness of the information, text, graphics, links, or other items contained within this material. 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 shall have no liability for damages of any kind including without limitation direct, special, indirect, or consequential damages that may result from the use of these materials. This limitation shall not apply in cases of intent or gross negligence. The statutory liability for personal injury and defective products is not affected. SAP has no control over the information that you may access through the use of hot links contained in these materials and does not endorse your use of third-party Web pages nor provide any warranty whatsoever relating to third-party Web pages. © 2011 SAP AG. All Rights Reserved

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