201304 sap dwh strategic direction
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Data Ware ouse o ut ons romSAPStrategic Direction and Evolution
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Data Warehousing Solutions from SAPOverview
Solving complex and diverse challenges
Real-time analytics
Big data
Agility
Powering sophisticated information landscapes
High-performance operational analytics
Enterprise data warehousing
Providing customer choice through complementary
and inter-operable solutions
SAP HANA Appliance
Sybase IQ
SAP NetWeaver Business Warehouse
Sybase Power Designer
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Definitions
Data Mart
Large Scale Query & AnalysisFlexible Data Marts
Simplified Maintenance
Centralized EDW
Data LineageData Consolidation
Data TransformationInformation Lifecycle Mgmt
Logical/Distributed DW
ETLGovernance
Data Distribution
Data Mart= Way to store and report on information
Data Warehouse = Ability to Manage/Integrate/Harmonize/Govern Multiple Data marts Centralized EDW = Centralized Management/Orchestration of multiple data-marts in a
single environment Logical EDW = Logical Management/Orchestration across a variety of data marts and
environments
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What is a Data Mart?
A data mart is a repository of data gathered from operational data and other sources that is designed to serve a particularcommunity of knowledge workers. The data may derive from a data warehouse or operational systems directly. The emphasis ofa data mart is on meeting the specific demands of a particular group of knowledge users in terms of analysis, content,presentation, and ease-of-use. Users of a data mart can expect to have data presented in terms that are familiar.
A data mart is a collection of subject areas organized for decision support based on the needs of a given department.
There are two main categories of data marts - dependent and independent. A dependent data mart is one whose source is adata warehouse. An independent data mart is one whose source is the legacy applications environment. All dependent datamarts are fed by the same source - the data warehouse.
Main tasks of a data mart:
Providing information specific to the needs of a certain business user group or department
Providing information ready for end-user consumption
Sourced:TechTarget
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Different Analytical Needs Leverage Appropriate Data Marts to serve Business Needs
Arch itec ted Data Mar ts (Enterpr ise BI) : Consolidated and integral part of EDW supporting decision making
on corporate data
Centrally managed by IT, standardized data models on corporateinformation
Long term requirements in terms of stability and consistency
Typically time aggregated data
Operational Data Marts (Operational Analytics):
Real Time Data and timeliness centric
Reporting on large volumes of granular, transactional data
Supporting local business execution
Higher data volatility
Agil e Data Mart s (Ad-hoc Analy sis):
Independently of the highly governed centralized corporate EDWlayers
Maximum flexibility for LoBs in data modeling and integration of LoBspecific data
Support strategic decision making in LOBs
Volatile and historical data with fluid data models
Real-Time Data Marts ( Event Based Analytics) :
Reporting on Events and Streams of Information
Defining Rules and Alerts to trigger exception based analytics
Tracking and logging Events via an audit trail and managingdeviations from audit information
Mobile consumption paradigm as key value
Near-Line Data Mart ( Query-able Archive) :
Reporting on Longer Term Trends
Reporting on Rarely Used Information for Governance / AuditPurposes
Near-LineData Marts
Query-able Archive
Archived Information
OperationalData Marts
OperationalAnalytics
DB Replication
Transactional |System of Record
Real TimeData Marts
Real TimeAnalytics
Sensor | Mobile |Social
Streams | Feeds
Transactional | Analytical |Systems of Record & Engagement
ArchitectedData Marts
AgileData Marts
Enterprise BI Ad-Hoc | Self-Service BI
ETL / ELT /Replication
Logical / Centralized Enterprise Data Warehouse(as a management framework)
ETL / ELT /Replication
Federated Queries
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What is a Data Warehouse?
Bill Inmon:The Data Warehouse is a subject-oriented, integrated, time-variant, non-volatile collection of data used to supportthe strategic decision-making process for the enterprise. It is the central point of data integration for business intelligence and isthe source of data for the data marts, delivering a common view of enterprise data.
Ralph Kimballs: A data warehouse is a copy of transaction data specifically structured for query and analysis. It is theconglomerate of all data marts within the enterprise. Information is always stored in the dimensional model.
TDWI: Data warehousing incorporates data stores and conceptual, logical, and physical models to support business goals andend-user information needs. A data warehouse (DW) is the foundation for a successful BI program.
Main tasks of a Data Warehouse:
Integrating data from various data sources to common semantics given by the Data Warehouse data model
Harmonizing data values
Establishing a single version of truth as a source for data marts, delivering a common view of enterprise data
Providing a single, comprehensive source of current and historical information
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Logical EDW versus Centralized EDW
Logical/Distributed DW Centralized EDW
SAP Sources(Structured+Unstructured)
Non-SAP Sources(Structured+Unstructured)
Data-Marts Data-Marts
Central Data Hub
Data-Marts Data-Marts
Data Visuali zation Data Visual ization
SAP Sources(Structured+Unstructured)
Non-SAP Sources(Structured+Unstructured)
Central EDW
Data-Marts
Data Visualization
Data-Marts
Harmonized Data
Logical Reporting Data (Cubes)
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Enterprise Data Warehousing Will Continue To Be a StrategicInvestment
Exponential Growth and IncreasingVelocity of Data
Increased Complexity of System andInformation Landscapes
Dynamic Business Requirements
Greater and More DiverseConsumption Patterns
Market Consolidation AcrossIndustries Drives M&A Activity
Focus on Internal and ExternalStakeholder Transparancy
EDW Challenges and Drivers
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SAP NetWeaver Business WarehouseAn integrated Enterprise Data Warehouse management application
Including capabilities for
Pre-defined Entity Relationship Models based on
SAP Business Suite
Agile Data modeling with
BW workspaces Non-materialized consumption of HANA datamodels in BW
Layered Scalable Archit ecture (LSA)
ETL process modeling
Real time data acquisition
Scheduling
Administrat ion l ifecycle management
High performance OLAP processing
both SAP and non-SAP sources
BW LSA ++
HANA
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A petabyte-scale elastic-compute analytics-serverthatsupports highly parallel query processing and data loading, andtunable workload management,
Supports commodity hardware and patenteddatacompression and indexing for very low Total Cost of Ownership
(TCO) Capabilities include ability to scale out with near linear
performance; native MapReduce / Hadoop integration; isolationof user communities for straightforward SLA management andsecurity; best in class in-database analytics andpredictivelibraries; advanced text search; FIPS level security.
An expanded eco-system for the support of third-partyapplications for information lifecycle management, businessintelligence and data integration, predictive analytics andsystem/data administration.
Sybase IQA proven enterprise-class columnar database IQ
Sybase IQ 15 Engine
Communications &
Security
MultiplexGridManagement
Adm
inistrationFramework
Column Indexing Sub-system
LoadingEngine
Storage Area Network
Query Engine
In-DatabaseAnalytics
Text Search
Web Enabled AnalyticsInformationLifecycleManagement
Column Storage Processor
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SAP HANAA platform for a new class of real-time analytics and applications
Real-time analytics (OLAP)
SAP Business Suite Third-party systems
SAP HANA
Real-time replication
servicesData services
Real-time apps (OLTP)
In-memory database
Planning and Calculation
EngineR & Hadoop integration
Predictive Analysis &
Business Function Libraries
Information Composer &
Modeling StudioText Search
Application Services (e.g.
HTML 5 Server)
3rd PartyBI Client
SAP NetWeaver (On Premise / Cloud )
Custom
Apps
SAP Business
Suite
SAP Business
Warehouse
SAP Big Data
App li cat ion sSAP Analytics
SAP
Mobile
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SAP HANA + Sybase IQStrategic Direction
Extend SAP HANAs processing enginewith Sybase IQ optimizer and indexinginnovations
Extend Sybase IQ Hadoop andMapReduce capabilities into SAPHANA
Next generation near-line SMARTSTORE solution for BW/SAP HANA
Customer Value
Market leading internal and externalpredictive libraries
Enterprise-wide information accesssupporting massive concurrentworkloads
Customer Value
Integration of Hadoop data andMapReduce queries with SAPHANA for Breadth AND DepthAnalysis
Customer Value
Cost effectively store Petabyte-sizeddata sets
Integrate Optimize Synthesize
Managing all OLAP and OLTP applications with close to zero application-based data redundancy by switching operations seamlesslybetween hot blades to manage 100% up-time with zero-disruption from data loads and applying fixes and new developmentsallocating data across all available storage media by hot and cold data requirements and everything in between
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SAP Data Visualization(SAP BusinessObjects BI)
Data Storage & Processing(SAP HANA Platform + Sybase IQ)
Data-Marts(Sybase PowerDesigner, HANA Model Studio, SAP BW)
SAP DeliveredData-Mart
Content(RapidMarts + RDS)
SAP Delivered EDW(SAP BW)
Custom Build EDW(Sybase PowerDesigner +
HANA Model Studio)
PartnerDelivered Data-
Mart Content(RapidMarts)
Custom BuildData-Mart
Content
SAP Enterpri se Information Management(SAP Data Services, Sybase RepServer, Sybase ESP, SLT, MDM, Informatio n Steward)
SAP Sources(Structured+Unstructured)
Non-SAP Sources(Structured+Unstructured)
Data Mart Context EDW Context
SAP Delivered Content for
BizSuite
Present
Manage Integrate& Harmonize
Store& Process
Ingest
Build
The SAP Data Management Landscape
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Sybase IQ EDW
SAP Enterprise Data WarehousingVision
Centralized EDW(In-Memory with Temperate data)
X-Consumption of BW and HANA data models NLS - Near-Line StoragePlanned in the near future Keep Current Data In-Memory Keep Aged Data on Disk
Custom Build DW Massive Scale
Petabyte scale storageand processing of data
Sybase PowerDesignerMeta Data and datamodeling
Centralized EDW - In-Memory Packaged Data Warehouse
framework Leverage Business Content for
SAP (master data, transactiondata, semantics)
Consumption of SAP HANAData Mart models
SAP BW on HANA
SAP HANA DB
SAP BW on HANA + HANA Data Mart
Sybase IQSybase IQ
SAP BW integrated with
SAP Business SuiteCustom Build Enterprise Data Warehouses
SAP Real-Time Data Platf orm (HANA+IQ)
Today
Future
ETL/Replication
Data Marts
SAP is evolving the Data Warehouse uniting SAP BW, Sybase IQ and SAP HANA on the SAP Real-Time Data Platform
SAP HANA Data Mart
Custom Build Data marts HANA optimized for real-time
use cases Instantaneous reporting on hot
dataPlanned in the near future Consumption of BW models Sybase PowerDesigner
SAP ETL/Replicatio nSAP BW ETL
SAP HANA DB SAP HANA DB
SAP BW ETL SAP ETL/Replication
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Next generation SAP Real-time Data Platform
3rd PartyBI Client
SAP NetWeaver (On Premise / Cloud)
Custom
Apps
SAP Business
Suite
SAP Business
Warehouse
SAP Big Data
App li cat ionsSAP Analytics
SAP
Mobile
Open Developer APIs and Protocols
Comm
on
Landscape
Management
SAP Smart Data Services Platform
SAP HANA Platform
SAP Real-time Data Platfo rm
SAP Sybase ASE
Common
Modeling
SybasePowerDesigner
HADOOP
3rd
PartyDB
MPP
Scale-Out SAP Sybase SQLA
SAP Sybase ESP
SAP Sybase IQ
SAP Sybase
Replication Server
SAP Data
Services SAP MDG, MDM
SAP innovation without customer disruption
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SAPs Data Platform Supports Storing Both High Value Data As WellAs High Volume Data
SAP is a company of choice: Buy or Build! SAP BW is an integratedEnterprise Data Warehouse
management application that SAP recommends due to its tight
integration with the SAP application family (master data,harmonization, data movement, etc) For customers who want to define their own warehousing
methodology, SAP will also optimize our tooling and platform forbuilding custom warehouses
Choosing the Analytic Database
SAP BW or Custom Bui ld Warehouses?
HANA for High Value or Hot Data
Sybase IQ for Higher Volume characteristics(price/performance of in-memory and disk)
HANA+IQ for providing temperate data (tiering storage andprocessing)ataValue
Data Volume Hadoop
ASE
HANA
IQ
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Planned Innovations Future DirectionToday
SAP NetWeaver BWStrategy Overview Key Themes and Capabilities
Upcoming planned release
HANA-specific features
SAP BW and SAP HANA MixedScenarios
Not active data concept
Support of Semantic Partitioned
Objects (SPO)
Enhanced Partitioning for writeoptimized DSOs
Support for SAP BusinessObjectsExplorer
Platform independent hi ghlights
Enhanced Support of 3.x ->7.xDataflow Migration
File Download of BW meta data
DSO Planning
Future innovations
Add it ional Flexib il ity
BW/Non-BW mixed EDW environments -Managing the logical EDW
Open Operational Data Store layer
Big Data/Hadoop connector
Lower TCO with sim plif ied data modeling
Uniform modeling concepts with eclipse basedUIs
Highly reduced number of InfoProvider types foreasier data modeling
Enhanced performance and scalability
Further reduce data provisioning times
HANA optimized transformations
Data aging strategies
Conversion t ools
(Release SP 8 Q4 2012)GA since April 10th
* SAP will continue to support RDBMSplatforms
Thispresentationoutlinesourgeneralproductdirectionandshouldnotbereliedoninmakingapurchasedecision.ThispresentationisnotsubjecttoyourlicenseagreementoranyotheragreementwithSAP. SAP hasnoobligationtopursueanycourseofbusinessoutlinedinthispresentationortodeveloporreleaseanyfunctionalitymentionedinthispresentation.ThispresentationandSAP'sstrategyandpossiblefuturedevelopmentsaresubjecttochangeandmaybechanged byS AP atany timeforanyreason withoutnotice.Thisdocumentis providedwithouta warrantyofanykind, eitherexpressor implied,includingbutnotlimitedto, theimpliedwarrantiesof merchantability,fitnessfora particularpurpose,or non-infringement.SAP assumesnoresponsibilityforerrors oromissions inthis document,exceptif suchdamageswerec ausedbyS AP intentionallyor grosslynegligent.
SAP NW BW 7.30 on HANA*
HANA specific features
Performance boost for data loading,query response time and planning
HANA-optimized InfoCubes and DataStore Objects (DSO)
Simplified and faster datamodeling/remodeling
In-memory planning Support of native HANA models Simplified system landscapePlatform independent highlights
Graphical data flow modeling Semantic Partitioned Objects (SPO) Rapid prototyping of Ad Hoc Scenarios
via BW Workspaces Tighter integration with SAP Data
Services
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Thi
s i
SAP Sybase IQStrategy Overview Key Themes and Capabilities
Analyt ics Server fo r EDW and Big
Data Analytics
Shared-everything MPP with VirtualData Marts and MapReduce APIs(Native +Hadoop Federation)
Certified with SAP BusinessObjectsBIplatform and Data Services v3.x, v4.x
Analyt ics Server fo r EDW
and XLDB Analytics
Next-gen column store for XLDBanalytics w/ parallel +concurrentingestion, intelligent scale out
Near-line store/feed from/to BWand HANA
MDX API support
Optimizations/innovations w/ SAPBusinessObjectsBI platform, DataServices & Predictive Analytics
SAP BusinessObjects Services:Load Table/Client SideLoads/ELT/Upsert
SAP BusinessObjects PredictiveAnalytics: connectivity drivers
Aut onomic An alyt ics Server fo r EDW and
XLDB Analytics
Self adjusting XLDB Analytics platformincluding cloud APIs
End-to-end co-innovations / differentiationwith SAP HANA, SAP BusinessObjectsBIplatform, Data Services & Predictive Analytics
SAP BusinessObjects BI platform: FunctionLibrary support, SQL Code Optimizations
SAP BusinessObjects Data Services: Multi-node loads, insert.location
SAP BusinessObjects Predictive Analytics: In-DB Analytics
Planned Innovations Future DirectionToday(Release 15.4 Q4 2011)
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Thi
s i
SAP HANAStrategy Overview Key Themes and Capabilities
Analyt ics Plat fo rm
SAP BW on SAP HANA Additional business functions and
predictive algorithms SAP HANA Studio Modeler
enhancements
Enhanced enterprise fit andreadiness
Additional and enhanced dataprovisioning capabilities
R integration Unstructured text search Enhanced security and
authentication Hadoop Integration (DS 4.1) BPC on SAP HANA Predictive Analysis on SAP HANA Desktop visualization client for
HANA (SAP Visual Intelligence)
Expanded existing Transactional
Open Platform supp ort
Business Suite on SAP HANAreadiness
Packaged Suite Analytics
New applications on SAP HANA
Enhanced developer support
Third-party tool certification andsupport (BI - SQL, monitoring,backup and recovery, data centeroperations)
Text analytics and file filtering
Explorer support for BW on HANA
Optimized PlanningEnhancements
Odatasupport
Single sign-on with SAML
Data-at-rest encryption
Sybase Replication Server & ESPsupport
Real-Time Data Platform
New applications on SAP HANA and SAPHANA Cloud
Additional Private Cloud deploymentcapabilities for SAP HANA
Power Designer Interoperability
HANA & SAP IQ Optimization and Integration
Native integration with Hadoop
Transformed SAP Business Suite processesleveraging merged OLTP & OLAP
Additional third-party tool support (ETL, BI MDX, and more)
Further optimization & integration betweenHANA and Sybase IQ & ASE
Spatial support and integration
Planned Innovations Future DirectionToday(SPS4 Q2 2012)
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SAP Enables ChoiceWhen to choose Centralized Enterprise Data Warehouse vs Custom Build?
Enterprise Data Warehousi ng - why
Consolidate the data across the enterprise to get a consistentand 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 tooling & capabilities
SAP NetWeaver BW provi des flexiblit y and scalable EDW capabiliti es
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
Sybase IQ/SAP HANA provi de a real-time database platform to custom buil d an EDW Higher development and maintenance efforts than adopting a packaged approach with
SAP BW on HANA (today, there are a variety of tools with lacking integration)
SAP is building out a Real-Time Data Platform to Unify the Tooling to Build andOrchestrate Custom Data Warehouses
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Key Contacts:
DW Solution Management Scott Shepard [email protected] Daniel Rutschmann - [email protected] Erich Schneider [email protected] Yuvaraj Athur Raghuvir - [email protected] SAP BW Product Management
Lothar Henkes [email protected] Brian Wood [email protected]
DW Solution Marketing Dan Kearnan [email protected] Dan Lahl [email protected]
Sybase IQ Product Management J oydeep Das [email protected]
HANA Product Management Michael Eacrett [email protected] Ingo Brenckmann [email protected]