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    Sap net weaver bw 7.3 powered by hana.3 Sap net weaver bw 7.3 powered by hana.3 Presentation Transcript

    • SAP NetWeaver BW Powered by SAP HANA and Future Roadmap Lothar Henkes April, 201
    • 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. © 2012 SAP AG. All rights reserved. 2
    • Agenda Introduction SAP NetWeaver BW‟s use of In Memory technology  SAP NetWeaver BW powered by SAP HANA  Customer results What‟s new with NetWeaver BW 7.3, SP8 Outlook- SAP NetWeaver BW 7.3 SP 9 and further Roadmap
    • Agenda Introduction SAP NetWeaver BW‟s use of In Memory technology  SAP NetWeaver BW powered by SAP HANA  Customer results What‟s new with NetWeaver BW 7.3, SP8 Outlook- SAP NetWeaver BW 7.3 SP 9 and further Roadmap
    • Unlock The Power of Your Data Across The Enterprise Enterprise Data Warehousing – the single point of truth  Enterprise Data Warehousing - why – Consolidate the data across the enterprise to get a consistent and agreed view on your data  "Having data is a waste of time when you can't agree on an interpretation." – Combine SAP and other sources together – Standardized data models on corporate information – Supporting decision making on all organizational levels  EDWs require a Database plus an EDW application  EDW with SAP NetWeaver BW a flexible and scalable EDW application – Highly integrated tools for modeling, monitoring and managing the EDW – Open for SAP and non-SAP systems – Agile data modeling using BW workspaces – Runs on top of HANA and other RDBMS – Easy consumption of HANA Data Mart scenarios via virtualized data access  EDW with custom built application – High development and maintenance efforts – Variety of tools with lacking integration © 2012 SAP AG. All rights reserved. 5
    • SAP NetWeaver BW Adoption Productive SAP NetWeaver BW Systems – Constant Growth Stable Product, Large installed Base, Constant Growth  Adoption of SAP NetWeaver BW constantly growing  Unaffected by economic downturn in 2009  ~ 14.000 customers referring to >17.000 productive systems Usage 16.000 15.000 14.000 13.000 12.000 Q1 13 Q4 12 Q3 12 Q2 12 Q1 12 Q4 11 Q3 11 Q2 11 Q1 11 Q4 10 Q3 10 Q2 10 Q1 10 Q4 09 Q3 09 Q2 09 © 2012 SAP AG. All rights reserved. 17.000 Q1 09  Vast majority: Central DWH, harmonizing many source systems  Minority: One-to-One to an ERP  Embedded into mission critical business processes 18.000 6
    • Agenda Introduction SAP NetWeaver BW‟s use of In Memory technology  SAP NetWeaver BW powered by SAP HANA  Customer results What‟s new with NetWeaver BW 7.3, SP8 Outlook- SAP NetWeaver BW 7.3 SP 9 and further Roadmap
    • Customer Value of BW Powered by SAP HANA Speed and Accelerated performance  Excellent query performance for improved decision making  Performance boost for Data Load processes for decreased data latency  Accelerated In-Memory planning capabilities for faster planning scenarios New Business Insights  Self-Service BI – Data modeling with BW Workspaces  Flexible combine EDW with HANA-native data for better insights and decision making Streamline Landscape and simplify data management  Non-disruptive DB Migration with SAP standard tools and services  Data persistency layers are cut out and admin efforts reduced: No aggregates, indexes, rollups, statistics  Simplified data modeling and remodeling © 2012 SAP AG. All rights reserved. 8
    • SAP NetWeaver BW7.3 Powered by SAP HANA How Does BW 7.3 Running on HANA Differ from BW Running on xDB? BW Upgrade BW BW 7.0x SAP NetWeaver BW 7.x on xDB  Standard 7.3 Migration DataStore Objects  Data Base server and SAP NetWeaver BWA  Standard InfoCubes  BW Integrated Planning  HANA Data Marts running side-by-side BW SAP NetWeaver BW 7.3 on HANA  SAP HANA-optimized DataStore Objects     SAP HANA In-Memory platform SAP HANA-optimized InfoCubes In-Memory planning engine Consumption of HANA artifacts created via HANA studio  BW staging from HANA Migration without reimplementation - no disruption of existing scenarios 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. © 2012 SAP AG. All rights reserved. 9
    • DataStore Objects in SAP NetWeaver BW 7.3 Overview and Challenge DataStore Objects are fundamental building blocks for a Data Warehouse architecture  They are used to create consistent delta information from various sources  Reporting can be done on a detailed level  In today‟s RDBMS-based implementation, the activation and querying operations are extremely performance-critical Delta upload Query Change Log Active Data Table Activation Activation Queue Parallel Upload © 2012 SAP AG. All rights reserved. 10
    • DataStore Objects in SAP NetWeaver BW 7.3 Creation of Consistent Delta Information Current architecture  Activation algorithm calculates the changes of each record and creates heavy load on the DBMS  Delta calculation performed on the application server, too complex to push it down to the DBMS as SQL/Stored Procedure  Roundtrips to application server needed for delta calculation Active Data Table Change Log Calculate Delta Lookup Update Data Packages Sorted Full Table Scan Activation Queue © 2012 SAP AG. All rights reserved. 11
    • SAP HANA-Optimized DataStore Objects Accelerated Data Loads SAP HANA-optimized DSOs  Delta calculation completely integrated in HANA User interface User interface  Using in-memory optimized data structures for Layer Layer Presentation Presentation faster access SAP NW BW SAP NW BW  No roundtrips to application server needed  Process of SID generation highly optimized for DSO Objects Application Application DSO Objects HANA Optimized DSOs  low impact on staging Layer Layer performance Activation SAP NW BW SAP NW BW  Speeding up data staging to DSOs by factor 5-10  Avoids storage of redundant data Activation Database Database Layer Layer  After the upgrade to BW on HANA all DSOs Data remain unchanged SAP HANA Data xDB  Tool support for converting standard DSOs into SAP HANA-optimized DSOs No changes of Data Flows required 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. © 2012 SAP AG. All rights reserved. 12
    • SAP HANA-Optimized InfoCubes Faster Data Loads and Easier Modeling MD Traditional InfoCubes tailored to a relational DB consist of 2 Fact Tables and the according Dimension tables MD D Facts SAP HANA-optimized InfoCubes represent “flat” structures without Dimension tables and E tables*: FE D  Up to 5 times faster data loads  Creation of DIM Ids no longer required     Simplified Data modeling Faster remodeling of structural changes After the upgrade to BW7.3, SP5 all InfoCubes remain unchanged Tool support for converting standard InfoCubes MD MD Conversion/New MD MD F  Preliminary lab result: 250 Million records in 4 minutes Facts No changes of processes, MultiProvider, Queries required MD MD *Tables for compressed data 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. © 2012 SAP AG. All rights reserved. 13
    • Query Performance Query Performance at Least as Good as with BWA Query acceleration on BW InfoCubes  Leverage column store and In-Memory Calculation Engine for query acceleration  No replication – fast query access directly on primary data persistence  Indexes on InfoCubes and InfoObjects no longer required  No Rollups, Change runs SAP NW BW Query on DSO(w/o SIDs), BW InfoSet Query on InfoCube, DSO(with SIDs), MultiProvider, Masterdata AnalyticIndex, CompositeProvider SAP HANA Query acceleration on BW DataStore Objects SQL Engine Calc Engine  Leverage column store and In-Memory Calculation Aggregation Engine on In-Memory data Engine for query acceleration  SID generation during DSO activation to be enabled  Result: Same kind of excellent query performance on DSOs  Process of SID generation highly optimized for HANA Optimized DSOs  low impact on staging performance No changes of processes, MultiProvider, Queries required 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. © 2012 SAP AG. All rights reserved. 14
    • Query Performance Are InfoCubes Still Required ? Query Info Cubes required for  Non-disruptive approach when migrating to BW on HANA  Non-cumulative Key Figures  Complex business logic(report specific)  BW Integrated Planning  External write-interface(RSDRI) Aggregates or BWA index Rollup InfoCube InfoCube DTP Conclusion  There are scenarios where the InfoCube layer becomes obsolete  Less materialized data and simplification  Decision to be made scenario by scenario: Business and Performance needs DTP Conversion Conversion DataStore Object BW 7.x on RDBMS DataStore Object BW on SAP HANA DataStore Object (w SID generation) BW on SAP HANA InfoCube can be removed when used for query performance only © 2012 SAP AG. All rights reserved. 15
    • SAP NetWeaver BW – SAP HANA Interoperability Consumption of SAP HANA data models in BW TransientProvider based on HANA Model  For ad hoc scenarios  Generated not modeled, no InfoObjects required SAP BW on HANA Query Query Composite Provider  Full BEx Query support  Can be included in a CompositeProvider to combine with other BW InfoProviders Query InfoCube TransientProvider Virtual Provider VirtualProvider based on HANA Model  For a flexible integration of HANA data with BW managed metadata (e.g. lifecycle) HANA Analytical View  Security handled by BW  Full BEx Query support  Can be included to Composite- and MultiProvider to combine with other BW InfoProviders © 2012 SAP AG. All rights reserved. SAP BW Schema SAP HANA Schema(s) SAP HANA 16
    • BW In-Memory Planning Accelerated Planning Functions Traditional Planning runs planning functions in the App. Server In-memory Planning runs all planning functions in the SAP HANA platform  Performance boost for planning capabilities like:  Aggregation, Disaggregation  Conversions, Revaluation  Copy, Delete, Set value, Repost, FOX  Performance boost for plan/actual analysis User interface Layer Presentation SAP NW BW User interface Layer Presentation SAP NW BW Application Layer Orchestration Calculation SAP NW BW Data Orchestration SAP NW BW Database Layer Database Layer xDB Application Layer SAP HANA Calculation Data No changes of planning models, planning processes, MultiProvider, Queries required 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. © 2012 SAP AG. All rights reserved. 17
    • SAP NetWeaver BW – Post-Copy Automation (BW PCA) Easier and faster migration to SAP NetWeaver BW on SAP HANA Automated migration of SAP NetWeaver BW on RDBMS to SAP NetWeaver BW on SAP HANA using BW PCA  Optimized downtime for SAP NetWeaver BW source system  Automated delta-queue cloning/synchronization – Enables easy operation of original + new system in parallel  Automated clean up of target system  Automated post-copy configuration © 2012 SAP AG. All rights reserved. 19
    • Agenda Introduction SAP NetWeaver BW‟s use of In Memory technology  SAP NetWeaver BW powered by SAP HANA  Customer results What‟s new with NetWeaver BW 7.3, SP8 Outlook- SAP NetWeaver BW 7.3 SP 9 and further Roadmap
    • SAP NetWeaver BW on SAP HANA Asian Paints Project scope and business scenario Asian Paints, the biggest paint manufacturing company in India with very strong brand equity is seen as the early adopter of cutting edge IT solutions, and is widely regarded in the SAP customer community as very prolific user of SAP BI. In late 2011, Asian Paints decided to implement HANA for their growing analytical needs for the large volume ERP and BW implementations, and with the help of SAP Consulting implemented SAP HANA running under SAP BW in only 3 weeks. Performance and benefits  Data volume compression savings of 6:1 moving from BW/Oracle to BW/HANA  Significant improvement in the query performance: average query performance improvement was15 x with maximum improvement 266x times.  Certain queries, for analyzing orders and billing that could not run in the past on BW-Oracle, are returning the results with an impressive query response time of 15-20 secs with BW-HANA.  Data load time reduced by an average of 95% with some of the delta data load completing in 2 minutes in BW-HANA, paving the way for near real-time data extraction and analysis, compared to more than 35-40 minutes it used to take in BW-Oracle. © 2012 SAP AG. All rights reserved. 22
    • SAP NetWeaver BW on SAP HANA Merit Energy Project scope and business scenario Merit Energy is one of the largest privately held oil and gas companies in the world. They purchased BW on HANA in Q4 2011 and implemented a BW on HANA pilot system that is running next to their production BW on Oracle system. They are feeding delta loads from their production ECC environment to both BW on Oracle and BW on HANA. Performance and benefits DSO Activation Biggest DSO is for FI-GL line items with nearly 1 billion rows of active data. From ECC, loaded 1 million+ rows of delta data into the FI-GL DSO. On Oracle, the activation took 588 seconds. With BW on HANA, the activation took 14 seconds! 42x Improvement. Data Loading Loading 14 million rows of data into an InfoCube for Lease Operating System data took 37 minutes in the Oracle based BW system. With BW on HANA, the In Memory Optimized InfoCube with no Dimension ID lookups took only 12 minutes for a 3x improvement. Infocube Compression The Lease Operating System InfoCube was 64.66 GB. With BW on HANA the same cube was only 19.32 GB for 3x compression. Customer Quote © 2012 SAP AG. All rights reserved. “I am able to access the lowest level of detail in our General Ledger DSO and the performance is incredible. Using the intuitive, Excel-based Analysis Office Interface I can navigate through 1 billion records in 10 seconds or less per navigation.” 23
    • Yazaki Europe, an automotive supplier and the world‟s largest producer of wiring harnesses for cars and trucks, had been using the 3.5 version of SAP NetWeaver BW 3.5. The company was having challenges with the performance of the existing reporting system. “We chose SAP NetWeaver BW powered by SAP HANA to improve and accelerate several processes in the areas of finance and controlling, production and logistics and we have already seen major improvements. We were able to accelerate processes within finance and controlling, such as end-ofmonth closing, year-end closing and other common analysis in the area of controlling.” “Our next big step will be the acceleration of processes within the productive ERP system, among others in the area of production and logistics.” With SAP NetWeaver BW powered by SAP HANA, Yazaki Europe has been able to accelerate reporting and is now accessing reports faster out of info cubes and using creativity within reporting. In addition, they are leveraging in-memory processing for fast acceleration of raw tables, calculations and BW metadata. Salim Siddiqi, CIO Yazaki Europe © 2012 SAP AG. All rights reserved. 24
    • More time for everyone © 2012 SAP AG. All rights reserved. 25
    • Agenda Introduction SAP NetWeaver BW‟s use of In Memory technology  SAP NetWeaver BW powered by SAP HANA  Customer results What‟s new with NetWeaver BW 7.3, SP8 Outlook- SAP NetWeaver BW 7.3 SP 9 and further Roadmap
    • Overview SAP HANA-specific highlights*  SAP BW and SAP HANA Mixed Scenarios  “Non active” data concept  Conversion of Semantic Partitioned Objects (SPO)  Enhanced Partitioning for write-optimized DSOs Platform independent highlights  Enhanced Support of 3.x  7.x Dataflow Migration  File Download of BW meta data  DSO Planning *please ensure to install the latest HANA revision © 2012 SAP AG. All rights reserved. 27
    • SAP NetWeaver BW – SAP HANA Mixed Scenarios: Combining the strengths of both worlds BW: Native HANA: - SAP Extractors - Data Services - SLT - SAP Extractors (DXC) - Data Services - SLT BW InfoProvider consumption in HANA HANA model consumption in BW BW data provisioning for HANA HANA data staged into BW © 2012 SAP AG. All rights reserved. 28
    • SAP NetWeaver BW – SAP HANA Interoperability Recap - Consumption of SAP HANA data models in BW before SP8 TransientProvider based on HANA Model  For ad hoc scenarios  Generated not modeled, no InfoObjects required SAP BW on HANA Query Query Composite Provider  Full BEx Query support  Can be included in a CompositeProvider to combine with other BW InfoProviders Query InfoCube TransientProvider Virtual Provider VirtualProvider based on HANA Model  For a flexible integration of HANA data with BW managed metadata (e.g. lifecycle) HANA Analytical View  Security handled by BW  Full BEx Query support  Can be included to Composite- and MultiProvider to combine with other BW InfoProviders © 2012 SAP AG. All rights reserved. SAP BW Schema SAP HANA Schema(s) SAP HANA 29
    • SAP NetWeaver BW – SAP HANA Interoperability Direct loading of HANA data into BW Optimized SAP HANA Data Load into SAP NetWeaver BW  Complementary to DB Connect  Based on new source system type: Operational Data Provider (ODP) with the context „HANA‟ SAP BW on HANA Data Transfer Process InfoCube MasterData DSO ODP source system, context ‚Hana„  Enable direct loading from HANA Views via DTP into BW managed schema  Data persistency in PSA optional  Mass data loading SAP HANA Analytic / Calculation / Attribute View SAP BW Schema SAP HANA Schema(s) SAP HANA © 2012 SAP AG. All rights reserved. 30
    • SAP NetWeaver BW – SAP HANA Interoperability Easy consumption of HANA master data in BW queries SAP NetWeaver BW Virtual Master Data SAP BW on HANA Query  HANA attribute view exposed as master data in BW InfoProvider  No data staging of SAP HANA master data required  Prior to SP 8 information in master data tables stored in SAP HANA schema could only be consumed in BW query via staging into BW Virtual Info Object Virtual Info Object Virtual Info Object Master Data Read Class SAP HANA Attribute View SAP BW Schema SAP HANA Schema(s) SAP HANA © 2012 SAP AG. All rights reserved. 31
    • SAP NetWeaver BW – SAP HANA Interoperability Flexible BW Data Provisioning for SAP HANA applications Open Hub Destination with new target SAP HANA DB:  Support data transfer from BW InfoProviders, DataSources and Queries into tables residing in SAP HANA for further usage by HANA applications. SAP BW on HANA Query InfoCube MasterData DSO Open Hub Service DataSource  Default is 1:1 mapping, individual transformation can be modeled  Delta extraction supported for InfoProviders and DataSource SAP HANA SAP HANA table  Query snapshots via QueryProvider are possible  Supported also for BW on classic RDBMS SAP BW Schema BW Schema SAP HANA HANA Schema Schema(s) SAP HANA © 2012 SAP AG. All rights reserved. 32
    • SAP NetWeaver BW – SAP HANA Interoperability Easy BW InfoProvider Consumption Generate Analytic View for BW InfoProvider via HANA Modeler Explorer  Generated Analytic View contains basic BW Metadata  Generated Analytic View can be consumed via SAP HANA interfaces and used in further SAP HANA models  This enables SAP BO Explorer on BW data SAP BW on HANA Master Info Cube consumes DSO Data SAP HANA Studio generates BW Schema SAP BW Schema HANA Analytic View SAP HANA Schema(s) SAP HANA SAP HANA © 2012 SAP AG. All rights reserved. 33
    • “Non Active” Data Concept * Providing lower TCO by optimized RAM sizing Tables/partitions in SAP HANA can be marked as “non active”  Such tables/partitions are … Data Stores (RAM) – loaded into RAM only when accessed – loaded into RAM in case of read access (column-wise) and processed as usual (same speed and functionality) – loaded to RAM for merge process (if new data was written and delta reaches limit) – displaced from RAM with highest priority in case of RAM shortage (but only then) or when actively a cleanup is triggered Persistence Layer  BW automatically marks all PSA tables and all write-optimized DSO tables as “not-active”, no extra maintenance or tuning is necessary * Requires HANA SPS 5 © 2012 SAP AG. All rights reserved. 34
    • Multi Temperature Data Data volume hot • warm • cold • • • • • • • Performance Data is read and/or written frequently In memory No restrictions, all features available Non-Active Data Infrequent access On disk, no need to keep in memory all the time No restrictions, all features available Concept Sporadic access Not stored in HANA DB; stored in Near-line Storage Restricted to NLS capabilities Providing lower TCO by optimized RAM management © 2012 SAP AG. All rights reserved. 35
    • “Non Active” Data Concept – SAP NetWeaver BW Reduces RAM sizing, thereby reducing TCO 32% Optimized RAM sizing • “Non active” data concept has substantial impact on SAP NetWeaver BW on SAP HANA RAM sizing • Assumption: only ~ 20% of “not active” tables are required in memory, rest resides on disk only. 28% 37% Examples of Customer Savings 3000 GB RAM Size without “non active” data consideration 2500 2000 1500 GB RAM Size with “non active” data consideration 1000 500 • ABAP Sizing Report (note 1736976) for SAP NW BW on HANA will take “not active” data into account © 2012 SAP AG. All rights reserved. 0 Customer A Customer B Customer C Software Industry Beverage Industry Automotive Industrie 36
    • Conversion of Semantic Partitioned Objects to HANA optimized Providing Investment Protection for investment made in SPOs Tool Support for Converting existing SPOs to HANA-optimized DSOs and InfoCubes  Integrated into standard conversion tool RSMIGRHANADB to ensure consistency during conversion Semantic Partitioned Object (SPO) Semantic Partitioned Object (SPO)  Conversion can be re-started at any time in case of failure SAP HANA optimized SPO RSMIGRHANADB Source1 Source2 © 2012 SAP AG. All rights reserved. Source3 Source1 Source2 Source3 37
    • Partitioning of Write-Optimized DSOs Reduces data latency for Write-Optimized DSOs Better performance of write-optimized DataStore Objects Main Indexes  Write-optimized DSO are now partitioned by request-ID  Partitioning improves merging performance significantly, especially in case the delta Index is merged for a large Write-Optimized DSO.  With partitioning only the relevant (last) partition (with changed/new records) is merged. Main Index Delta Index Request n Request 2 Request 1 Merge  In addition performance for read and delete is improved, because with partition pruning only a subset of the data is accessed. © 2012 SAP AG. All rights reserved. 38
    • Agenda Introduction SAP NetWeaver BW‟s use of In Memory technology  SAP NetWeaver BW powered by SAP HANA  Customer results What‟s new with NetWeaver BW 7.3, SP8 Outlook- SAP NetWeaver BW 7.3 SP 9 and further Roadmap
    • SAP NetWeaver BW Product Roadmap Overview – Key Themes and Capabilities SAP NW BW 7.3 on SAP HANA SAP HANA specific features  Performance boost for data loading, query response time and In-memory planning  SAP HANA-optimized InfoCubes and Data Store Objects  Simplified and faster data modeling/remodeling  “Not active” data concept  SAP NetWeaver BW and SAP HANA Mixed Scenarios (BO Explorer, BW Virtual Master Data, etc.)  Support of Semantic Partitioned Objects and enhanced partitioning for write optimized DSOs  Simplified system landscape Platform independent highlights*  Graphical data flow modeling and enhanced support of 3.x-> 7.x data flow migration  Semantic Partitioned Objects (SPO)  Rapid prototyping of Ad Hoc scenarios via BW Workspaces  File download of BW meta data  DSO planning  Tighter integration with SAP Data Services Today Upcoming planned release HANA-and platform independent  SAP NetWeaver BW Near Line Storage solution (NLS) based on Sybase IQ – Helps to reduce TCO by reducing data volume in BW – Shortens backup time frames – High speed analysis for NLS residing historical information – SAP owned solution out of one hand  SAP NetWeaver BW Post-Copy Automation Refresh – Helps to reduce operations costs and risks by automating post-copy configuration tasks – Availability planned with 2013/Q2 for all releases from BW 7.0 onwards Planned Innovations (Release SAP NW BW 7.3 SP8; SAP NW BW 7.31 SP5) Future innovations Prepare for Big Data  Data LifeCycle Management / Multi-temperature data  Bulk load capabilities  Dimensions with more than 2 billion members  Hadoop connection Additional flexibility / agility  Harmonize SAP NetWeaver BW and SAP HANA modeling  Eclipse based modeling environment in BW e.g. BW Query Designer, CompositeProvider  PSA layer becomes optional  Leverage HANA libraries in BW transformations  Support extra long texts Future Direction (Release SAP NW BW 7.3 SP9; SAP NW BW 7.31 SP7) * SAP will continue to support RDBMS platforms © 2012 SAP AG. All rights reserved. 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. 45
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