Exploring SAP NetWeaver BW on SAP HANA in Combination With SAP BusinessObjects BI 4.x [1]
What You Need to Know About SAP NetWeaver BW Powered by SAP HANA
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Transcript of What You Need to Know About SAP NetWeaver BW Powered by SAP HANA
What You Need to Know About SAP NetWeaver BW
Powered by SAP HANA
Daniel Rutschmann Director, BW on HANA at SAP America
February 16, 2012
BI Expertwww.BIexpertOnline.com
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Agenda• Introduction• SAP NetWeaver BW powered by HANA• Brief tour of BI Expert Web site• SAP NetWeaver BW powered by HANA (cont.)• Q&A
Adoption of SAP NetWeaver BW constantly growing
More than 13.000 customers referring to more than 15.000 productive systems
Stable Product, Large installed Base, Constant Growth
Productive SAP NetWeaver BW systems – constant growth
Q3 09
Q4 09
Q1 10
Q2 10
Q3 10
Q4 10
Q1 11
Q2 11
Q3 11
12,000
12,500
13,000
13,500
14,000
14,500
15,000
15,500
16,000
13,9
10
14,2
17
14,4
50
14,6
93
14,9
52
15,2
43
15,5
33
15,6
71
15,8
32
Reliable Data Acquisition
Reliable Data Acquisition
Business Content
Business Content
Streamlined Operations
Streamlined Operations
Lifecycle Management
Lifecycle Management
Fast, sustainable implementation: Modeling Patterns Business Content
Load any data with trust and quality: Out-of-the box integration for data originating in SAP systems Integrated with SAP BusinessObjects Data Services (Data Integrator and Data Quality Management)
Efficient, streamlined data management: Management of data consistency Sophisticated Security, Authorization and Identity Handling High availability
Sophisticated lifecycle management at different levels: System Meta Data Data (Nearline storage, archiving)
Integrated, scalable Enterprise Data Warehouse (EDW) platform
Existing SAP BW Reference customers
• Missing analytical capabilities on DB level lead to massive AppServer/DBServer traffic
– DataStoreObjekt (DSO) (e.g. Activation)– Integrated Planning (e.g. Disaggregation)
• Missing capability of integrating different data mart types on a single platform
– Architected data marts vs. operational data marts vs. agile data marts
• Nature of RDBMS - tupel based data storage, indexing necessary for performance
– Read/Load Performance on the RDBMS (e.g. Extended SAP Star Schema too complex)
• Other Examples– Exception Aggregation (e.g. Distinct Count only available as BWA Calculation Engine
feature)
Typical Bottle Necks - Short Comings of current Approach
ABAP AS Next Generation
Next Generation Apps
SAP HANA
Data inmemoryRuntime
Procedurecode
Programcode
compile& deploy
Fast datatransfer
Applications – Tight coupling between Application Server and SAP HANA
ABAP AS
App
RDBMS
Today
In-Memory empowered
With large data volumes, reading information becomes a bottleneck
Next generation applications will delegate data intense operations
The runtime environment executes complex processes in memory
In memory computing returns results by pointing apps to a location in shared memory
SAP NetWeaver BWSAP NetWeaver BW
Data ModelingData Modeling
HANA
Data ManagementData Management
Data StorageData Storage
Analytical / PlanningEngine
Analytical / PlanningEngine
DBMS
SAP NetWeaver BWSAP NetWeaver BW
Data ModelingData Modeling
Analytical / PlanningEngine
Analytical / PlanningEngine
Relational Database
Data ManagementData Management
Data StorageData Storage
SAP NetWeaver BW – Fully In-Memory Enabled EDW
BWA
SAP NetWeaver BW Powered by HANASupercharge BW Applications
SAP NetWeaver BW
RDBMS
SAP ERP
RDBMS
ETL
SAP HANA
Non SAP
Data sources
Primary Database for BWFoundation for new Applications In-Memory database used as primary persistence
for BW
BW manages the analytic metadata and the EDW data provisioning processes
Detailed operational data replicated from applications is the basis for all processes
SAP HANA 1.x will be able to provide the functionality of BWA
High-performance foundation for new SAP applications
SAP Applications
SAP BusinessObjects BI
Supercharge BW with Dramatically Improved Performance
Time becomes your competitive advantage With faster reporting and more insightful analysis With faster data loading and decreased data latency
Simplified Administration and Streamlined Landscape
Efficiency becomes your competitive advantage With reduced IT workloads and administrative tasks With a simplified system landscape and less data storage
Unlock The Power of Your Data Across The Enterprise
Business flexibility becomes your competitive advantage With access to ALL your data down to the most detailed level With business users empowered to quickly get the answers they need
Preserve Your BW Investment without Disruption
Innovation without disruption becomes your competitive advantage With no disruption to your BW application but with all the benefits of a supercharged system With minimal impact to business users and administrations in terms of training
A brief tour of the BI Expert Web site
SAP NetWeaver BW 7.x on xDB Standard DataStore Objects Data Base server and SAP BWA Standard InfoCubes BW Integrated Planning HANA Data Marts running side-by-side BW
SAP NetWeaver BW 7.3 on HANA HANA as a Platform In-Memory based InfoCubes In-Memory based DataStore Objects In-Memory planning engine Consumption of HANA artifacts created via HANA
studio BW staging from HANA
Migration without reimplementation - no disruption of existing scenarios
Access Data Faster
– Improved Reporting and query performance over traditional
RDBMS
– Query acceleration on BW DataStore Objects(DSO)
– Acceleration via In-Memory column storage
– Additional acceleration via Analytic Views on top of DSO
Real-Time Operational Data Access
– Transient InfoProider dynamically generated on top of Analytic
Views during Query runtime
Reduce Latency
– Performance boost for ETL processes
– Faster delta loading via massively improved load window
Accelerated reporting performanceSpeed
Scale
Flexible
HANA
SAP NW BWQuery on
InfoCube, MasterdataAnalytic Index, Composite
Provider
Query on InfoCube, Masterdata
Analytic Index, Composite Provider
Query on DSO, BW InfoSet
Query on DSO, BW InfoSet
SAP HANA SQL Engine Calc Engine
Aggregation Engine on In-Memory data
Classic BWStar Schema
HANA OptimizedStar Schema
SAP HANA optimized InfoCubes represent “flat” structures Up to 5 times faster data loads (lab results) Faster remodeling of structural changes After the upgrade to BW on HANA all InfoCubes remain unchanged Tool support for converting standard InfoCubes (lab result: 250 Million records in 4 minutes) No changes of processes, MultiProviders, Queries required
No dimension tables No second fact tables
HANA optimized DSOs• Delta calculation completely
integrated in HANA• Using in-memory optimized data
structures for faster access• No roundtrips to application server
needed• Speeding up data staging to DSOs
by factor 5-10• Avoids storage of redundant data• After the upgrade to BW on HANA
all DSOs remain unchanged• Tool support for converting
standard DSOs into HANA optimized DSOs
• No changes of data flows required
DatabaseLayer
DatabaseLayer
User interfaceLayer
User interfaceLayer
ApplicationLayer
ApplicationLayer
Presentation
DSO Objects
Activation
Data
Presentation
DSO Objects
Activation
Data
SAP NW BW
SAP NW BW SAP NW BW
SAP NW BW
SAP HANA xDB
HANA Optimized DSOs provide faster activation times!
Delta: 0.1 M, Active: 1 M Delta: 1 M, Active: 10 M Delta: 10 M, Active: 100 M0
500
1000
1500
2000
2500
3000
3500
4000
4500
20sec5 min
75 min
3sec41sec
8 min
Activation Runtime - Lab Results
BW 7.30 - RDMBS based In-Memory optimized
Using in-memory computing technology
… one of the most time consuming staging operations – the request activation – was speed up tremendously by factor 5 - 10
... storage of redundant data was prevented
Runti
me
in s
econ
ds
Push-Down Planning Logic to SAP HANA
– Traditional Planning runs planning functions in the
App. Server
– In-memory Planning runs planning functions in the
SAP HANA platform
Performance boost for planning capabilities
– Aggregation, Disaggregation
– Conversions, Revaluation
– Copy, Delete, Set value, Repost, FOX
– Performance boost for plan/actual analysis
Non-Disruptive
– No changes of planning models, planning
processes, MultiProvider, Queries required
In-Memory enabled planning for shorter planning cycles!
DatabaseLayer
DatabaseLayer
User interfaceLayer
User interfaceLayer
ApplicationLayer
ApplicationLayer
Presentation
Orchestration
Calculation
Data
Presentation
Orchestration
Calculation
Data
SAP NW BW
SAP NW BW SAP NW BW
SAP NW BW
SAP HANA xDB
Tight integration between HANA Data Mart scenarios and SAP NetWeaver BW Providing additional flexibility by combining
ad-hoc data models from Data Marts with consolidated data in the EDW
No need to manually create/maintain Metadata for Analytic Views in SAP NetWeaver BW
Transient InfoProider dynamically generated on top of Analytic Views during Query runtime
Query: e.g. Analysis, Xcelsius, Web Intelligence (WebI)
Integration BW Analysis Authorization Concept
SAP BW can leverage real-time operational data-marts from SAP HANA!
BW Schema Non-BW data
InfoCubeTransient Info
Provider
Composite Provider
Query Query
SAP BW
SAP HANA
Reporting & Analytics Tools
SAP HANA
any schema
BW application
BICS, MDX, SQL
ReplicationServices
Data Services
SAP Extractors
Data Services
HANA
BW managed schema
BW workspace
Explorer Universes (SQL) other SQL clients
Analysis (Pioneer) BICS Universes
(WebI, CR, XC)
BICS, MDX, SQL
Planned functionality, not yet available.
Scope tbd, butwill be limited.
Feedback from Early Customers
• 100x Faster Reporting
• 10x Faster Data Loading
• 3x faster than BWA!
• 20% Reduction in Admin & Maintenance FTE• Estimated by customer. Customer plans on re-deploying FTEs to BI projects
Selected Early Results– 1. Data Mart Process Performance – Before: 3 hours a day, for populating physical Data Marts within BW– After : 3 minutes for the same data volume, Data Marts are logical views only
– 2. Change Management Performance after structural changes (e.g. add/delete fields)– Before: ALTER TABLE (triggering data re-alignment) on ORCL and rebuilding BWA index taking 7+ hours– After : ALTER TABLE is just a “drop/add column” and BWA index is not required anymore taking < 1 min
– 3. Staging / Data load Performance – Before: data is loaded to DSO, delta is calculated (activation), delta is loaded to InfoCube – taking 9:08h (4:30h + 56
min + 3:20h)– After : same process, but using HANA-optimized object types and delta calculation within HANA – taking 3:20h (2h
+ 4 min + 1:14h)
– 4. Query Performance– Before: All queries’ performance is within good level, if BWA is attached – After : Current performance is at least the same as with BWA or better (with potential for further optimization)
Ramp Up Program SAP NetWeaver BW powered by HANA
Customer Industry Region Size of POC Scenario
Retail Customers EMEA, APJ, South America 800GB – 5 TB
POS-DM, Financial Reporting, Procurement Scenarios, Materials Management, Shelf Analysis, Detailed Stock
reporting, General Ledger, Material Assortment Planning
Consumer ProductsEMEA, APJ,
North&South Americas
320 GB – 5 TB Operational Reporting on ERP data (e.g. SD, MM), CRM scenarios, Shop reporting
Utilities, Public Sector EMEA, APJ, North America 1 – 2 TB
Reporting on ERP data, Fleet and Plant Management reporting, Customer Care, Billing, HR, Energy
Consumption Analysis and planning
Oil & Gas, Chemical EMEA, APJ 1 – 5 TB Classical ERP Reporting, revenue stream and chash-flow analysis,
Automotive EMEA, APJ 160GB – 5TB Classical ERP reporting, Material Movement, Financial Reporting and planning
PLUS more Ramp Up Customers in Telecommunications, Finance, Pharma, Healthcare, HighTech
www.BIexpertOnline.com
Questions?
To “raise your hand” online for a question, click the hand icon and
we will unmute you.
Resources■BW powered by HANA Online Help– http://help.sap.com/nw73bwhana■Experience SAP HANA– www.experiencesaphana.com■SAP HANA optimized planning–
http://www.sdn.sap.com/irj/scn/weblogs?blog=/pub/wlg/27521■Support for multiple applications on SAP HANA – Note 1661202
and 1666670■SAP BW on HANA Sizing – Note 1637145
Contact informationDaniel Rutschmann • Director, BW on HANA• [email protected]
Scott Wallask• Managing Editor, BusinessObjects Expert• Email: [email protected]