Saurabh Thukral│ Product Manager, SAP BI
SAP Advanced AnalyticsDemocratizing the use of Predictive Analytics
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 2Customer
2
HOW DO YOU ACCELERATE YOUR GROWTH?HOW DO YOU ACCELERATE YOUR GROWTH?
CLOUDCLOUD MOBILEMOBILE
An emerging middle class growing to 5B
Data doubling every
18 months
More mobile devices
than people
1 billion people on Facebook
15 billion web-enabled
devices in 2013
THINGSTHINGSDATADATA
In a world of accelerated change…
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 3Customer
Analytics Maturity
Sense & Respond Predict & Act
RawData
CleanedData
Standard Reports
Ad Hoc Reports &
OLAP
Generic Predictive Analytics
Predictive Modeling
Optimization
What happened?
Why did it happen?
What will happen?
What is the best that could happen?
Com
petit
ive A
dva
nta
ge
Analytics Maturity
The key is unlocking data to move decision making from sense & respond to predict & act
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 4Customer
What is Predictive Analytics?
Predictive Analytics is quantitative analysis to support predictions. Examples include: forecasting of product sales, costs, metrics; analyzing customer churn; credit scoring, identifying cross sell / up sell
opportunities, measuring market campaign response; anomaly detection and fraud detection etc.
It comprises primarily of Statistical Analysis and Data Mining, but can also include methods and techniques from Operations Research.
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 5Customer
ChallengesChallenges
Forecasting
KeyInfluencers
Trends
Anomalies
Relationships
How do historical sales, costs, key performance metrics, and so on, translate to future performance? How do predicted results compare with goals?
What are the main influencers of customer satisfaction, customer churn, employee turnover, and so on, that impact success?
What are the trends: historical / emerging, sudden step changes, unusual numeric values that impact the business?
What are the correlations in the data? What are the
cross-sell and up-sell opportunities?
What anomalies might exist and conversely
what groupings or clusters might exist for
specific analysis?
Where Predictive Analytics is used
Imagine the Business Potential…Predictive Use Cases – Industry & LoB
• Customer Churn / Retention
• Cross-Sell / Upsell• Campaign
Management
•Lifetime Value•Pricing Optimization•Product Launch Success•Brand Sentiment & Sales Analytics•Cross/Up Sell
•Product Launch Success•Brand Sentiment & Sales Analytics
•Regional Forecasting•Brand Sentiment & Sales Analytics
•Next Best Activity•Cross Sell/Upsell•Churn ReductionCustomer SegmentationBrand Sentiment & Sales Analytics
•Brand Sentiment & Sales Analytics
• Credit Risk• Fraud Management
& Prevention
•Credit Scoring•Fraud Management & Prevention•Optimizing Product Quality
•Credit Scoring•Compliance•Retail Outlier•Fraud Management & Prevention•Optimizing Product Quality
•Credit Scoring•Compliance•Fraud Management & Prevention•Optimizing Product Quality
•Credit Scoring•Underwriting•Default/bankruptcy risk•Tax Fraud•Credit Card Fraud•Insurance Fraud
•Predictive Asset Maintenance•Fraud Management & Prevention•Optimizing Product Quality
•Anomaly detection•Usage forecasting•Customer Segmentation
•KPI Forecasting•Anomaly detection•Usage forecasting•Store Segmentation•In-store Workforce Optimization•Size and Zone Optimization•Market Share Prediction
•KPI Forecasting•Anomaly detection•Usage forecasting
•KPI Forecasting•Anomaly detection•Usage forecasting
•KPI Forecasting•Anomaly detection•Usage forecasting
•KPI Forecasting•Anomaly detection•Usage forecasting•Variable Margin Analysis•Yield Management•Equipment Effectiveness•Labor Utilization
•Out of Stock Prediction•Demand Forecasting•Inventory and Logistics Planning
•Out of Stock Prediction•Inventory and Logistics Planning
•Out of Stock Prediction•Inventory and Logistics Planning
•Predictive Commodity Management•Improving Demand Planning and Inventory Management
Retail CPG Financial Services ManufacturingTelecom E-Business
Customer /Marketing
Fraud/Risk
Operations
SupplyChain
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 7Customer
Acquire
RetainCross & Up-Sell
Your Customer
Optimize every customer interaction
Challenging to detect meaningful signals in big data
Severe analytics skills shortage 50-60% shortfall for
experienced data analystsDun & Bradstreet and McKinsey Global Institute analysis
86% of organizations that
used predictive realized a competitive advantage
Sense and respond are no longer enough
Difficult to apply predictive algorithms to anticipate business trends
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 9Customer
Challenges and Inefficiencies
Analysts: Talent Shortage
Fragmented Point Solutions
Usability Shortcomings
Lack of VisualizationModel ProliferationHigh Latency
Operational Datastore
Sensors Mobile ArchivesSocial & Text
Order Processing Operational Reporting
RT Risk & Fraud Trend Analysis Sentiment Analytics
Predictive Analytics
PatternRecognition
Spatial Processing
Analyze
Data Stores
Integrate/Load
Staging
Collect
Clean-DataQuality
Transact
Report
Explore
Communicate
Monitor
Predict
Planning
0
1
DataWarehouse
Geo-Spatial
Cache Cache Cache Cache CacheCache
Business & IT: Segregated Organization Structure
Lack of Decision Support
Lack of Data Governance
Complex Slow Costly
Advanced Analytics – SAP Vision
Operationalize predictive & optimization models across the enterprise
Reduce Decision Latency with Advanced Analytics
Bringing Predictive Analytics to a broad spectrum of users
Embed Smart Agile Analytics into Decision Processes to Deliver Business Impact
Easy Fast Efficient
SAP Solutions for the Entire Spectrum of Users
Business Users & LOBDataScientist
Business Analysts
Level Of Skill Set - Analytics
Low HighNo
97% 3% >0.1%Embedded AnalyticsIndustry & Business Process Analytics
CustomAnalytics
SAP Lumira SAP InfiniteInsight (KXEN) SAP Predictive Analysis SAP PAL R Integration
SAP ADVANCED ANALYTICS
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 12Customer
The Forrester Wave™Big Data Predictive Analytics Solutions, Q3 2014
A leader in predictive
“SAP is a Leader in predictive analytics due to a strong architecture and strategy.”
Comprehensive and holistic approach to “Big Data”
“SAP also differentiates by putting its SAP HANA in-memory appliance at the center of its offering, including an in-database predictive analytics library (PAL), and offering a modeling tool in SAP Predictive Analysis.”
The Forrester Wave™ is copyrighted by Forrester Research, Inc. Forrester and Forrester Wave™ are trademarks of Forrester Research, Inc. The Forrester Wave™ is a graphical representation of Forrester's call on a market and is plotted using a detailed spreadsheet with exposed scores, weightings, and comments. Forrester does not endorse any vendor, product, or service depicted in the Forrester Wave. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change.”
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 13Customer
SAP Brings BI and Advanced Analytics Together
SAPLumira Cloud
Explorer MobileExplorer
Web
SAPLumira
Search
Explore
Share
Acquire
Transform
Visualize
Design
Model
Build
Govern
Enterprise Information
Assets
Local
View
SAPPredictive AnalysisKXEN
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 14Customer
SAP Predictive Analysis …
... Complete data discovery, visualization, and predictive analytics solution
... Integrated with SAP Lumira for data acquisition, data manipulation and visualization capabilities
… is one integrated solution for advanced data analysis and interactive data visualizations
… identifies trends, insights and discovers hidden patterns in the data
SAP Predictive AnalysisData Discovery Rich Visualizations Predictive Analytics
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 15Customer
SAP Predictive Analysis Self Service for Business Analysts and Data Scientists
Provide Business Analysts with sophisticated algorithms to take the next step in understanding their business and modeling outcomes
• Perform statistical analysis on your data to understand trends and detect outliers in your business
• Build models and apply to scenarios to forecast potential future outcomes
• Breadth of connectivity to access almost any data
• Optimized for SAP HANA to support huge data volumes and in-memory processing
OVERVIEW
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SAP Predictive Analysis Visualize, discover, and share hidden insights
TODAY
Library of advanced visualizations within the modeling tool
Share insights via Predictive Modeling Mark-up Language (PMML) and with other BI clients
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SAP Predictive Analysis – Algorithm sources
HANA Predictive Analysis Library (PAL)
algorithms
Analysis is done within HANA (no movement of data) and controlled by SAP PA
Open Source ‘R’ integration algorithms
Data is brought to SAP PA and analysis is performed in the client
SAP PA Native algorithms
Data is brought to SAP PA and analysis is performed in the client
HANA ‘R’ integration algorithms
Analysis is done in R server attached to HANA and controlled by SAP PA
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 18Customer
PAL - Algorithms supported in HANA 1.0 SPS7
Y
X Z
Association Analysis
Apriori
Apriori Lite
Cluster Analysis
ABC Classification
DBSCAN
K-Means
Kohonen Self Organized Maps
Agglomerate Hierarchical Clustering
Affinity Propagation clustering
Classification Analysis
C4.5 Decision Tree Analysis
CHAID Decision Tree Analysis
K Nearest Neighbour
Multiple Linear Regression
Polynomial Regression
Exponential Regression
Bi-Variate Geometric Regression
Bi-Variate Logarithmic Regression
Logistic Regression
Naïve Bayes
Support Vector Machines
Time Series Analysis Single Exponential Smoothing
Double Exponential Smoothing
Triple Exponential Smoothing
Outlier Detection Inter-Quartile Range Test (Tukey’s Test)
Variance Test
Anomaly Detection
Link Prediction Common Neighbours; Jaccard’s Coefficient; Adamic/Adar;
Katzβ
Data Preparation Sampling
Binning
Scaling
Convert Categorical to Binary
Other Weighted Scores Table
Statistical Functions – Univariate and Mutlivariate
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 19Customer
Customizing SAP Predictive AnalysisAdd your custom R script as a component
Predictive Analysis provides an interface for users to add a new R component using a wizard
• Type the R script or import it from a file
• Add parameters required for the script (including model saving option)
• Use the visualizations available in R as part of the script
New R component can be created both in HANA online and non-HANA online scenarios
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 20Customer
What is R?
R is a software environment for statistical computing and graphics Open Source, programming language plus a run-time environment
Over 3,500 add-on packages; ability to write your own functions
Widely used for a variety of statistical methods: linear and non-linear models, statistical tests, time series analyses, classification and clustering, predictive, etc.
More algorithms and packages than SAS + SPSS + Statistica
Who is using it? Growing number of data analysts in industry, government, consulting, and academia
Cross-industry use: high-tech, retail, manufacturing, CPG, financial services , banking, telecom, etc.
Why are they using it? Free, comprehensive, and many learn it at college/university
Offers rich library of statistical and graphical packages
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 21Customer
R Integration for SAP HANA Functionality Overview
The R integration for SAP HANA enables the use of the R open source environment in the context of the HANA in-memory database
Establishes a communication channel between HANA and R for fast data exchange
Embed R script within SQL script and submit entire query to the HANA database.
As the plan execution reaches R-node, a separate R runtime is invoked using Rserve and input tables of R node passed to R process using improved data transfer mechanism.
SAP PA
SAP HANA R Server
SQL R script
R script
Demo
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 23Customer
Options to use SAP Predictive Analysis
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 24Customer
SAP Predictive Analysis – High Level Architecture
HTML5 Client
Predictive Analysis Backend
Prepare Room
Lumira Backend
Visualize Room
Share Room
Compose Room
Predict Room
Designer
PA specific Visualization
In Database Engine
Offline Engine
PA Online components
PA offline components
Offline R Engine
HANASybase IQ
PAL Scripts
R ScriptsR Server
HTTP
JDBC
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 25Customer
Predictive Analytics in SAP Applications
Customer Engagement Intelligence
Shopper Insight
Sentiment Intelligence
Situational Awareness
Net Margin Analysis
Condition Based Maintenance
Demand Signal Management
Fraud Management
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 26Customer
Empower the Modern Marketer with SAP Customer Engagement Intelligence
SAP Solution Elements Business Benefits
Better understanding of customer behavior by leveraging Big Data as an asset
Improve ROI of campaigns by targeting the right audience
Champion the delivery of a personalized consumer experience across channels
Real-time reporting on campaign success
Marketing optimization to drive revenue and margin
SAP Predictive Analysis to optimize marketing campaigns
SAP Audience Discovery & Targeting turning analytical insight into action for campaign management powered by SAP HANA
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 27Customer
SAP Predictive Analysis 1.17Feature Description
Integration with SAP Lumira
• Data acquisition from a variety of data sources – SAP HANA (offline and online), universes, RDBMS, SAP ERP, XLS, CSV
• Data preparation, manipulation and visualization
Simplified UI for predictive analysis
• Operates in HANA and non-HANA scenarios
• HANA 1.0 SPS7 PAL support
• HANA R support – 5 algorithms out of the box
• Offline R support – 13 algorithms out of the box
Predictive specific visualizations
• Automatically created predictive algorithm specific visualizations
– Time series chart, regression chart, cluster viewer, decision tree and tag cloud
Customization support
• Custom R script support in HANA and non-HANA
– Ability to run any R algorithms
– Build your own visualization using D3 charts.
Enhance model consumption
• Advance Modeling, Partition data and choose the best model.
• Export PAL models and Analysis as SQL procedure
• Export and import models as PMML and proprietary format (SPAR)
Saving & Sharing predictive data and visualizations
• Save data in lums and automatic recreation of predictive specific algorithms visualizations on opening of .lums
• Sharing of predictive specific datasets and visualizations Story board.
Multi language support
• English, French, German, Japanese, Simplified Chinese, Russian, Portuguese, Spanish
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 28Customer
TODAY Planned Innovations Future DirectionTODAY Planned Innovations Future Direction
SAP Predictive AnalysisProduct road map overview - key themes and capabilities
Integration with SAP Lumira
Data acquisition and manipulation
Save user created visualization
Share predictive data
Simplified HTML5 UI for predictive analysis
HANA and non-HANA scenarios
HANA PAL & HANA R support
Open source R support
Predictive specific visualizations
Customization support
Custom R script support
Enhance model consumption
PAL models , Analysis as SQL procedure
Export & import models as PMML/SPAR
Train Test Validate,
Infinite Insight Integration
Classification and Regression Analysis
Clustering Analysis
Saving predictive data & visualizations
Core Predictive/ Advance Analysis Feature Enhancement.
Model Comparison
Schedule Analysis
Algorithm Improvement, HANA PAL, Offline Algorithm
Model Visualization Improvements
SAP Infinite Insight Integration
Infinite Insight Modular Algorithm.
Generation of Model in SAP HANA SQL
Predictive Consumption / Scoring
Exporting HANA R Model as Stored Procedure.
Consuming PA Models in Cloud/Server
Core Predictive/ Advance Analysis Feature Enhancement.
Auto Modeling
Advance Modeling,
Large Data volume Visualization
Algorithm Improvements
SAP Infinite Insight Integration
(SAP Predictive Analysis becomes single, go-forward solution)
Support IFL Algorithms on HANA.
Integration with Modeler, Explorer, Factory, SNA
Infinite Insight Recommendation Integration
Consumption of Infinite Insight Artifacts.
Predictive Consumption / Scoring
PA Models in Lumira stories Cloud / Server
Offline Model Export as DB SQL
Offline R Models as Stored procedure.
This is the current state of planning and may be changed by SAP at any time.(Release 1.0.17)
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 29Customer
Skills shortage
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SAP InfiniteInsight Overview
BuildModel
ScheduleRefresh
PrepareData
DeployModel
ADS Creation
Data Manip & Prep
Text Analytics
Link Analysis
Viral Marketing
Influencers
Regression
Classification
Segmentation
Forecasting
Products
Targeted Ads
Website Content
In-Database
(Optimized SQL for
Teradata, SAP Hana, etc.)
Inline
(C++, PMML, Java, SAS,
etc.)
Refresh ADS
Retrain Model
Apply Scores
Notify on Exception
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 31Customer
Modeler
Build your models
Access
Easily Integrate
InfiniteInsightScorer
Deploy your scores
InfiniteInsightFactory
Improve your models
Explorer
Prepare your data
SAP InfiniteInsight
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 32Customer
Improve Insight Extend Reach Boost ROIConnect
Access to both data and metadata (name of columns, storage, indices and more)
Connectivity to major database platforms
Access to major proprietary file formats (SAS, SPSS, Excel, etc.)
Easily integrate into your existing data investments
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 33Customer
Reusable Reduces Human Error Self-ServicePrepare
Create 1000’s of derived attributes
Define metadata once
Select time-stamped population
Builds analytic dataset automatically
Analytical data sets with clicks not code
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 34Customer
Easy to Use Time to Market More ModelsBuild
Fully automated modeling process
• Regression
• Classification
• Segmentation
• Time series forecasting
• Association rules
Identify key variables
Executive and operational reports
Predictive power in days not months
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 35Customer
Put scores into action
One-click deployment of scores into production
In-database scoring (SQL)
Interface with business apps via scoring equations in
• Java
• PMML
• SAS
Non-Intrusive Time to Value RepeatableDeploy
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 36Customer
Refresh analytic data sets and models automatically
Deploy scores to production
Alert on data and model deviations
No Programming Scale Manage By ExceptionImprove
Every model at peak performance
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 37Customer
Improve Insight Extend Reach Boost ROI
Social
Use social variables for enhanced prediction
Identify communities amongst your customers
Find influencers to make your campaigns viral
Improve insight with social networks
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 38Customer
Adaptive Big Data Plug & Play
Recommend
Addresses any type of business questions
Make Product recommendations, targeting digital content
Social recommendations (e.g. friends) and targeted ads.
Personalize the recommendations
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 39Customer
Why SAP advanced analytics
Real-time in-memory predictive &next generation visualization & modeling
Empower the Business
Extend the BI competency to advanced analytics
Embed predictive into LoB and industry solutions
Lend expertise
Bridge skills gap
In-memory processing
No data latencies
Big Data ready
In-time Actionable Insights
Reduced TCO
Streamline data management, data prep, model building and model scoring on database
Within the context of your Industry & LOB scenario
Q&A
© 2014 SAP AG or an SAP affiliate company. All rights reserved.
Thank you!
Venkatesh Vaidyantahan, [email protected] Kumar KN, [email protected] Thukral, [email protected]
30-day Trial of SAP PA here: http://www.saphana.com/docs/DOC-3527
Community, Demo & Use cases: http://www.saphana.com/community/learn/solutions/predictive-analysis
SCN: http://scn.sap.com/community/predictive-analysis
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 42Customer
© 2014 SAP AG or an SAP affiliate company. All rights reserved.
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National product specifications may vary.
These materials are provided by SAP AG or an SAP affiliate company for informational purposes only, without representation or warranty of any kind, and SAP AG or its affiliated companies shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP AG or SAP affiliate company products and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty.
In particular, SAP AG or its affiliated companies have no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation, and SAP AG’s or its affiliated companies’ strategy and possible future developments, products, and/or platform directions and functionality are all subject to change and may be changed by SAP AG or its affiliated companies at any time for any reason without notice. The information in this document is not a commitment, promise, or legal obligation to deliver any material, code, or functionality. All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions.
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Real-time business with the SAP HANA platform for Big Data
Anil Kumar Damara
Director - Cognilytics
Agenda
Why Big Data
Big Data Potential
The power of Hadoop integrated with SAP HANA
Smart Data Access
SLT Replication
SAP Business Suite
Example for Real-Time Value powered by SAP HANA
Why Big Data
12 TB of Tweets in a Day
80%Of world’s datais unstructured
30 billion pieces of content shared on Facebook every
month
Expected Data in 2020 would be 35 ZB
5 Million Trade events per second
The Human mind processes about one PB in a sec , So 50
PB can store everything in min
4.7 billion searches on
Google per day
5 Billion people tweet,text,call and browse on mobile
phones daily
Walmart handles 1 Million transaction per hour
Big Data Characteristics
Build Vs Buy
HUMAN DRIVEN
WEB LOGS
DOCUMENTS
SOCIAL
• Data Landscape evolution
MACHINE DRIVEN
SATELLITE IMAGES
BIO-INFORMATICS
M2M LOG FILES
SENSORS
VIDEO
AUDIO
BUSINESS DRIVEN
OLTP
I.T. MUST MANAGE, GOVERN AND ANALYZE MORE DATA WITH MORE COMPLEX RELATIONSHIPS … IN REAL TIME … AT SCALE
ALL DATA TYPES
1X 10X 100X
BIG DATA TODAY
BIG DATA TOMORROW
SAP HANA – In-Memory Computing
Operational
Warehouses
Marts
Dimensional
Semantic
Information
Oracle DB2 SQL Other
BW TeraData Netezza
Mart Mart Mart
OLAP OLAP
IQ
Universe
?Queries Ad-HocDashboard
ETL
DATA QUALITY
Applications
Reports
OLAP
Mart Mart Mart
OLAP
Mart
HANA
Oracle/DB2/SQL/Other
BW/Netezza/Teradata/IQ
HANA
- 7 - SIT
te
mp
late
.pp
txClient logo
Client logo
How it is done
Any Device
Any AppsAny App ServerAny Apps
Any App ServerSAP Business Suite
and BW ABAP App Server
SAP Business Suite and BW ABAP App Server
JSONROpen
ConnectivityMDXSQL
Other AppsLocationsReal-timeHADOOPMachineUnstructuredTransaction
SAP HANA Platform
SQL, SQLScript, JavaScriptSQL, SQLScript, JavaScript
Integration ServicesIntegration Services
SpatialSpatial
Business Function Library
Business Function Library
SearchSearch Text MiningText Mining
Predictive Analysis Library
Predictive Analysis Library
DatabaseServicesDatabaseServices
Stored Procedure & Data Models
Stored Procedure & Data Models
Planning EnginePlanning Engine Rules EngineRules Engine
Application & UI Services
Application & UI Services
More than just a database
Next generation - SAP Real-time Data Platform
SAP Analytics
SAP Business
Suite
SAP Big Data Applications3rd Party
BI Clients
SAP Mobile
On Premise / Cloud
Custom Apps
Open Developer API’s and Protocols
Com
mon Landsc
ape
Managem
ent
SAP Enterprise Information Management
SAP Sybase Replication Server
SAP Data Services
SAP HANA Platform
SAP MDG and MDM
SAP Real-time Data Platform
SAP Sybase IQ SAP Sybase ASE
SAP Sybase SQLA
SAP Sybase ESP
Com
mon M
odelin
gS
ybase P
ow
erD
esi
gner
MP
P
Sca
le-O
ut
SAP NW BW
The power of Hadoop integrated with SAP HANA
With SAP in-memory computing platform, SAP HANA, you’ll have the ability to run big
data analytics on 80 terabytes of data, integrate with Hadoop, search text content,
harness the power of real-time predictive analytics, and more.
Exploit unstructured data such as text, documents, Web, and social media content
Deliver predictive insight with in-database data mining
Leverage open source R analytic processing
SAP HANA integration with Hadoop: enabling customers to move data between Hive
and Hadoop's Distributed File System and SAP HANA or SAP Sybase IQ server, which
will work to provide products that make use of HANA and Hadoop.
With SAP in-memory computing platform, SAP HANA, you’ll have the ability to run big
data analytics on 80 terabytes of data, integrate with Hadoop, search text content,
harness the power of real-time predictive analytics, and more.
Exploit unstructured data such as text, documents, Web, and social media content
Deliver predictive insight with in-database data mining
Leverage open source R analytic processing
SAP HANA integration with Hadoop: enabling customers to move data between Hive
and Hadoop's Distributed File System and SAP HANA or SAP Sybase IQ server, which
will work to provide products that make use of HANA and Hadoop.
The power of Hadoop integrated with SAP HANA …
As we understood so far that Hadoop can store very huge amount of data. It is well
suited for storing unstructured data, is good for manipulating very large files and is
tolerant to hardware and software failures.
But the main challenge with Hadoop is getting information out of this huge data in real
time.
As we understood so far that Hadoop can store very huge amount of data. It is well
suited for storing unstructured data, is good for manipulating very large files and is
tolerant to hardware and software failures.
But the main challenge with Hadoop is getting information out of this huge data in real
time.
1. Use SAP Data Services to extract:
Core entities (who, what, when, where, etc.)
Domains (voice of customer, public sector, enterprise, etc.)
Sentiment analysis (strong positive, weak positive, neutral, weak negative, strong negative)
2. Perform transformations
Map text into pre-defined structures
Cleanse, match, de-duplicate data
3. Load results quickly into EDW
Map text to structure
The power of Hadoop integrated with SAP HANA …Processing Text to extract relevant data from Hadoop
The power of Hadoop integrated with SAP HANA …
SAP Data Services: Simple GUI build and run ETL processSAP Data Services: Simple GUI build and run ETL process
Processing Text to extract relevant data from Hadoop
The power of Hadoop integrated with SAP HANA …
Parameter SAP HANA HADOOP
Data Storage Hot Data (high-value, often used data, in-memory)
Cold Data (- persist information for archival and retrieval in new ways, - don't want to structure in advance: Weblogs)
Maturity Incredible maturity of HANA's SQL and OLAP engines
Need to improve
Aggregation Speed Fast. Different ways of aggregationavailable
7x times faster
Simplicity of operation and storing data
Batch Jobs processing Very Efficient but high cost Very Efficient and Cost effective
Parallel Processing Very good parallelization on large system and near-linear scalability
Not Available
Software Stack SAP Proprietary Open Source
The power of Hadoop integrated with SAP HANA …
Explore Product Performance in Real-timeExplore Product Performance in Real-time
The power of Hadoop integrated with SAP HANA …
Sessionization
Big Data TypesBig Data Types
Reviews and Social MediaReviews and Social Media
The power of Hadoop integrated with SAP HANA …
BO Explorer for Big Data Exploration
Smart Data Access
Smart Data Access in SAP HANA which is a Virtualization Technique.
Smart Data Access is a technology which enables remote data access as if they are local tables in HANA without copying data into SAP HANA.
Data required from other sources will remain in virtual tables. Virtual tables will point to remote tables in different data sources.
It will enable real time access to data regardless of its location and at same time, it will not effect SAP HANA database. Customers can then write SQL queries in SAP HANA, which could operate on virtual table.
The HANA query processor optimizes these queries, and executes the relevant part of the query in the target database, returns the results of the query to HANA, and completes the operation.
Smart Data Access in SAP HANA which is a Virtualization Technique.
Smart Data Access is a technology which enables remote data access as if they are local tables in HANA without copying data into SAP HANA.
Data required from other sources will remain in virtual tables. Virtual tables will point to remote tables in different data sources.
It will enable real time access to data regardless of its location and at same time, it will not effect SAP HANA database. Customers can then write SQL queries in SAP HANA, which could operate on virtual table.
The HANA query processor optimizes these queries, and executes the relevant part of the query in the target database, returns the results of the query to HANA, and completes the operation.
2 Parts:
1) Initiating the Replication
2) Monitoring the Load and Replication
2 Parts:
1) Initiating the Replication
2) Monitoring the Load and Replication
SLT Replication – Real-Time
How to Replicate Data from SAP System to HANA using SLT How to Replicate Data from SAP System to HANA using SLT
Source SLT HANA
SAP Business Suite
Powered by SAP HANA
SAP ERP SAP CRM SAP SCM SAP SRM
SAP HANA PLATFORM
Smarter Business
InnovationsUnlock new growth opportunities
before your competitors do
Faster Business
ProcessesDrive your business at the speed
of market
Smarter Business
InteractionsEmpower people to decide and
act in the business moment
SAP HANA Text Analysis using Twitter
Twitter API
Tweets into SAP HANA
system
Run Text Analysis in SAP
HANA
Twitter API
Tweets into SAP HANA
system
Run Text Analysis in SAP
HANA
SAP BW powered with HANA
Technologies Used
SAP BusinessObjects Analysis, edition for OLAP
SAP BusinessObjects Design Studio
SAP BusinessObjects BI
SAP NetWeaver BW
SAP HANA
In case of data replication:
SAP LT
Technologies Used
SAP BusinessObjects Analysis, edition for OLAP
SAP BusinessObjects Design Studio
SAP BusinessObjects BI
SAP NetWeaver BW
SAP HANA
In case of data replication:
SAP LT
SAP Real Time Data Platform
BW CompositeProvider
BW InfoProvider
Real-time replication from SAP or 3rd party systems
inserts / updates from an HANA application
One-click conversion
Data to Decisions
o How the signals are being analyzed in real-time using SAP HANA In-memory computing
o In Marketplaces, observe what is going right and what is going wrong e.g: Consumer Selling
o How the signals are being analyzed in real-time using SAP HANA In-memory computing
o In Marketplaces, observe what is going right and what is going wrong e.g: Consumer Selling
Real-Time Value from Marketplaces
SAP HANA + SLT = Real-time Intelligence on streaming and operational DataRetail Industry – predictive buyer & seller behavior analysis
Business Challenges
Increase conversion rates from free buyer and seller
Increase the average revenue per buyer player
Decrease churn – keep paying players playing longer
Technical Challenges
Leverage real-time data processing in SAP HANA and classification algorithms with R integration for SAP HANA to deliver personalized context-relevant offers to players
Analyze vast amounts of historical and transactional data to forecast buyer and seller behavior patterns
Benefits
Real-time insights
Per seller profitability analysis and increased understanding of seller and buyer behavior
Increase data volume and processing capabilities to communicate personalized messages to players
At one of is our strategic retail client, we have successfully implemented signal detection system leveraging SAP HANA as the in-memory to transform its marketplace, optimizing the buyer and seller experience. And simplified operations and transformed business.
Senior Managing Director – Cognilytics
“ ”
5,000 Signals per second loaded onto SAP HANA (not possible before)
10%–30%increase in revenue per year
Interactivedata analysis leading to improved design thinking and marketplace planning
References
In-memory Computing with HANA
http://www.sap.com/pc/tech/in-memory-computing-hana/software/platform/database.html
BIG DATA Platform Capabilities & Benefits
http://scn.sap.com/community/hana-in-memory/blog/2013/11/12/big-data-platform-capabilities-benefits
A Big Data Platform for Real-Time Business: How Customers Use SAP HANA
http://events.sap.com/sapphirenow/en/session/2289
SAP HANA Tutorial
http://saphanatutorial.com/sap-hana-and-hadoop/
In-memory Computing with HANA
http://www.sap.com/pc/tech/in-memory-computing-hana/software/platform/database.html
BIG DATA Platform Capabilities & Benefits
http://scn.sap.com/community/hana-in-memory/blog/2013/11/12/big-data-platform-capabilities-benefits
A Big Data Platform for Real-Time Business: How Customers Use SAP HANA
http://events.sap.com/sapphirenow/en/session/2289
SAP HANA Tutorial
http://saphanatutorial.com/sap-hana-and-hadoop/
Summary & Key Takeaways
Real-time computing is the key.
Immediate and direct access to the latest data in real time.
Unstructured data - Hadoop.
OLTP and OLAP combining into one System – SAP HANA
Smart Data Access and SLT replication.
Real-time Actionable Insights
25
Questions
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HANA Modeler FeaturesWhat’s New?
Raghavendra Rao
SAP Labs
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 2Public
SAP HANA SPS 08 – Feature Overview
Modeling Enhancements
• Enhanced SAP HANA Modeling capabilities
Variable/Input Parameter mapping to external views for value help
Data types specifications - allow decimal/float type without specifying length and scale
Currency conversion - support to specify the target data type also for base measures / configurable currency columns
Sorting support within of parent/child hierarchies
Set default schema mapping at package level
Mass-copy to allow sub-packages to be selected
Show productive system alert for edit/update/delete activities
Introducing performance analysis capabilities (partitioned tables, number of rows)
Support for Unicode characters in view/column/… names
UI and usability enhancements (new tree map control in union view, join icons, …)
BW connections enhancements
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 3Public
SAP HANA SPS 08 - Modeling Enhancements
Value Help Views – Variable and Input Parameter Mapping Support
Parameter passing to external views for value help
Variables and Input Parameter can be mapped to variables and input parameters from external views
– Allows filtering and customizing value help lists from external views
– Supported with Analytic- and Calculation Views (Graphical and Script)
Manage Mapping dialog
– Directly enabled in variable/input parameter creation dialog
– Supports mapping source differentiation via “Select Type”
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 4Public
SAP HANA SPS 08 - Modeling Enhancements
Numeric data type handling enhancements
Relaxed FLOAT and DECIMAL data type specifications
DECIMAL data types can be specified without mandating length and scale
– Internally treated as floating-point decimal with varying length and scale,length is derived from values at runtime
FLOAT data type can be specified without mandating length
– Internally treated as 64-bit double data type
Relaxed data type specification supported with
– calculated columns dialog,
– input parameters for direct and static type, variables
– union-node constants
– procedures and script-based calc views
Can be left unspecified
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 5Public
SAP HANA SPS 08 - Modeling Enhancements
Naming conventions enhancements
Support for Unicode characters in names
Unicode characters can now be used in view names, column names, input parameters, variables, hierarchies, calculation view node-names, etc. …
“Field Name Preferences” allows control the use of unicode characters
List of restricted characters (which are not allowed)
– \ / : * ? “ < > |. ; ‘ $ % , ! # + and space
– For restricted measures and hierarchies use of slashes(/) as beginning characters is allowed
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 6Public
SAP HANA SPS 08 - Modeling Enhancements
Currency conversion enhancements
Data Type for measures with currency conversions
Data Type and precision for the conversion values can be specified independent from the input data
– The inherited data type and precision may have too generic precision definition resulting in rounding errors after the conversion.
– This allows to specify sufficient precision during conversion
– Only numerical data types with decimal places are allowed
– Supported for base measures, not with calculated measures
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 7Public
SAP HANA SPS 08 - Modeling Enhancements
Hierarchy enhancements
Ordering in Parent/Child Hierarchies
Order By columns can be specified
– Previously parent-child hierarchies were ordered according to the leaf nodes in the child column and from that the natural ordering of their ancestors follows accordingly.
– Now, on the advanced tab of the parent child hierarchy dialog we allow specifying sort attributes with the sort direction.
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 8Public
SAP HANA SPS 08 - Modeling Enhancements
Introducing performance analysis capabilities
Performance Analysis Mode in Modeling Environment
Introduction of performance analysis hints and indicators inside the HANA Model Editor
– Manually switched on or defaulted switched on
– Hints and indicators about table partitioning and number of rows (threshold as preference)
Scenario indicators for partitioned tables (icon)and exceeded row thresholds
Switching on performance analysis mode
View details pane: indication about partitioning type by icon (hash, range, …)
Performance analysis: more partitioning and row count information.
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 9Public
SAP HANA SPS 08 - Modeling Enhancements
Calculation View Union Modeling Enhancements
Union Modeling Enhancements
New graphical tree-map control for unions
– Higher performance and usability when handling large structures with the union-node
– Scrolling, selecting, removing etc. are much faster
Query behavior for constant mapping columns
– A “Empty Union Behavior” flag allows to determine if queries on constant value columns shall return values, e.g. for value help queries in applications
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 10Public
SAP HANA SPS 08 - Modeling Enhancements
Managing Model Content Enhancements
Mass Copy Enhancements
Allows copying of content into sub-packages / multiple packages as target package
Package Mapping configurations can be stored independent from system tables "_SYS_BI"."M_CONTENT_MAPPING"
– Can now be stored in developer’s local eclipse workspace
– Warning issued if switched back to system tables
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 11Public
SAP HANA SPS 08 - Modeling Enhancements
Managing Schema Mapping Enhancements
Define specific default schemas
Schema content may derive from multiple / different back-end or authoring environments
In order to ease managing of schema mapping in such scenarios, package-specific schema mapping (which overrides the default schema mapping) can be maintained in _SYS_BI.M_Package_Default_Schema
– Has to be maintained manually (SQL)
– mapping with package specific default schema
Additionally, the „schema“-property of a catalog table in the modeleditors is editable (combo-box dialog)
Default Schema Mapping
Overruled by Package-specificSchema Mapping
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 12Public
SAP HANA SPS 08 - Modeling Enhancements
Model Editor User Interface Enhancements
Miscellaneous user interface enhancements
Color schema harmonization and shading selected node
Column icon use across model
Show complete editor palette
Matching join and union icons across all editors
Filter Tooltip in Details panel
– show filter expression on hover over in details pane (same as scenario panel)
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 13Public
SAP HANA SPS 08 - Modeling Enhancements
Production Alert and BW connection enhancements
Miscellaneous alert and configuration enhancements
Production System Alert
– Editing, deletion, object activation, … actions issued on systems as Production Systems (HANA system configuration) will issue a visual alter (indicator) or extra pop-up
BW connection configuration enhancements
– „SAProuter String“ for BW-Models input connection
– Java connection encryption betweenHANA Studio and BW back-end(requires client crpyto libs installed)
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 14Public
SAP HANA SPS 08 - Modeling Enhancements
Modeling Productivity – Error Handling/HANA AnswersIntegration
Extended Error Handling with SAP HANA Answers (answers.saphana.com)
In extension to documentation and help, SAP HANA Answers.com will be introduced to SAP HANA Studio as crawl source of information
E.g. adds information from SCN and others
Displays embedded in HANA Studio or outside
Integrated with HANA Studio views (job log, …),editors, wizards. Called via key from selected textor feature.
Independent feature to install
Questions
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 16Public
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.
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 17Public
How to find SAP HANA documentation on this topic?
SAP HANA Platform documentation
What’s New – Release Notes
Modeling– SAP HANA Modeling Guide
Development– SAP HANA Developer Guide
References – SAP HANA SQL Reference
• In addition to this learning material, you find SAP HANA documentation on SAP Help Portal knowledge center athttp://help.sap.com/hana_platform.
• The knowledge center is structured according to the product lifecycle: installation > security > administration > modeling > development.
So you can find e.g. the SAP HANA Modeling Guide in the modeling section and so forth …
© 2014 SAP AG or an SAP affiliate company. All rights reserved.
Thank you
Contact information
B Raghavendra RaoAssociate [email protected]
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 19Public
© 2014 SAP AG or an SAP affiliate company. All rights reserved.
No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP AG or an SAP affiliate company.
SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP AG (or an SAP affiliate company) in Germany and other countries. Please see http://global12.sap.com/corporate-en/legal/copyright/index.epx for additional trademark information and notices.
Some software products marketed by SAP AG and its distributors contain proprietary software components of other software vendors.
National product specifications may vary.
These materials are provided by SAP AG or an SAP affiliate company for informational purposes only, without representation or warranty of any kind, and SAP AG or its affiliated companies shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP AG or SAP affiliate company products and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty.
In particular, SAP AG or its affiliated companies have no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation, and SAP AG’s or its affiliated companies’ strategy and possible future developments, products, and/or platform directions and functionality are all subject to change and may be changed by SAP AG or its affiliated companies at any time for any reason without notice. The information in this document is not a commitment, promise, or legal obligation to deliver any material, code, or functionality. All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions.
Please complete this session evaluation
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© 2014 SAP AG or an SAP affiliate company. All rights reserved. 2
Summary
• Enables employees to quickly find and contact the best expert and talent inside the organization.
• Searches skills and talent data wherever it is – integrating with any system – even with LinkedIn
• Extends the SuccessFactors BizX Suite
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 3
Design
• Built on Hana Cloud Platform.
• Flexible Framework supporting multiple Skill DB sources (Interface -Based)
• Ranked result list based on level of expertise, Proximity with Employee
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 4
Architecture
HANA Cloud Platform
Initial Load
Periodic Delta
Updates
HANA DB
Search Module
SAP UI5 Front end
Wikipedia / GeoName
Org Data
Skill Data
Interfaces
1. Load Initial Data to get Org and Skill
master data
2. Run WhoCanHelpMe
App
3. Allow Access to LinkedIn
Profile
4. Put Search criteria and
ENTER
Skill Data
Other Sources 1
Skill Data
Other Sources nLinkedIn Id is
linked with SAP ID
Org Data File in Fixed Format
Manual Initial Load
Implementation
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 5
Tight Coupling as Extension with SuccessFactors
Data
• Use of employee’s organizational data
• Use of employee’s skill and talent data
User interface
• Single Sign on from SuccessFactors Home page
• Quick access of the extension from the homepage
• And vice versa direct access from the extension to SF Employee Central organizational data
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 6
SFAPI
• Compoundemployee
• background_specialassign
• background_certificates
• background_courses
• background_funcexperience
• background_industryexperience
• background_languages
• background_leadexperience
• background_projectexperience
• background_techskills
SuccessFactor API’s
ODATA
• SelfReportSkillMapping
• RatedSkillMapping
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 7
Home page
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 8
Performance Analysis
• Powered by two alternative search engines:
• HANA Specific
• Non-HANA
• Two options to prioritize search
• AND: where the search engine finds profiles matching all the supplied search terms
• OR: where the search engine finds profiles matching any of the supplied search terms
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 9
Analysis AND vs OR
0
1000
2000
3000
4000
5000
6000
7000
8000
932 30832 329832 628832 1017532
Res
po
ns
e t
ime
in
mse
c
Total number of records in DB
OR Search Engine
JPA DB
HANA DB
JPA Total Time
HANA Total Time
0500
100015002000250030003500400045005000
932 30832 329832 628832 1017532
Res
po
ns
e t
ime
in
mse
c
Total number of records in DB
AND Search Engine
JPA DB Time
HANA DB Time
JPA Total Time
HANA Total Time
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 10
Analysis HANA vs Non-HANA(JPA)
0
500
1000
1500
2000
2500
3000
3500
74 32 141 57469
169593
217
1176
266
1213
373
1021
422
1513
1156
15101591
2203
234328
7
28
7
28
7
287
28
7
Res
po
ns
e t
ime
in
mse
c
Total number of records in DB
HANA code pushdown
Selected Records
HANA Total Time
HANA DB Time
0
2000
4000
6000
8000
10000
12000
14000
826 471 412 482 1658146523772561
585044322086
722 1401 693
26421588
34222615
7540
4174
28
728
7
287
287
28
7
res
po
ns
e t
ime
in
mse
c
Total number of records in DB
JPA Query
Selected Records
JPA Total Time
JPA DB Time
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 11
http://marketplace.saphana.comcom
Try it out today!Find more information on
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 12
Summary
• One Stop Skill Searching Point
• Combined with Professional Network Search
• Filling the gap for SuccessFactors BixZ Suite
• Build on HANA Cloud Platform
• Performance optimized with code push down to SAP HANA DB
• Interface Based Architecture
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 13
Key Takeaway
• SIMPLIFY experts search
• Intuitive USER EXPERIENCE using SAPUI5
• EXTENDING SAP Cloud Product Portfolio
© 2014 SAP AG or an SAP affiliate company. All rights reserved.
Thank you
Shibaji Chandra , Vijay Singh RajputSAP Global DeliveryGurgaon
SAP HANA Cloud PortalVikrant Raj
Deloitte
Agenda
Platform as a Service(PaaS)
SAP HANA Cloud Portal
Product portfolio for SAP NetWeaver Portal
Administration & Authoring
Development Process
On-Premise Integration
Future Steps
Platform as a Service(PaaS)
PaaS delivers cloud-based application development tools, in addition to
services for testing, deploying, collaborating on, hosting, and
maintaining applications.
It enables customers and partners to rapidly build, deploy, and manage cloud-
based enterprise applications that complement and extend your SAP or non-
SAP solutions, either on-premise or on-demand.
SAP HANA Cloud Platform,
Platform-as-a-Service
offering from SAP, is an in-
memory cloud platform
based on open standards.
Product portfolio for SAP NetWeaver Portal
SAP HANA Cloud portal
Cloud-based solution for easy
creation and management of attractive
business sites designed for mobile
consumption out of the box.
A true portal Platform-as-a-Service
product.
SAP NetWeaver 7.3 Portal Proven, secure, mobile-ready enterprise portal platform enabling users to centrally access enterprise assets Enterprise Workspaces 1.1 SAP add-on solution empowering end users with focus on usability and mobile consumption to increase productivity for both individuals and teams SAP Portal content / site management by OpenTextSAP add-on solutions for enhanced content, document and web site management optimized for SAP NetWeaver Portal
SAP HANA Cloud Portal
Portal Platform as a Service (pPaas)
based lean portal, mashing and extending
on premise and cloud scenarios
• Enable lines of business to quickly and easily
create attractive and business-driven sites
• Arm IT departments with an easy-to-
administer, lean portal platform to
efficiently extend on-premises and cloud
scenarios with minimal investments
Key Elements
• Lean Portal – Quickly up and running!
• Create your own business site in minutes.
• Simple drag & drop interface
• Hosted by SAP public cloud
• Runs on top of SAP HANA Cloud
• AppStore-like experience
• Mobile first midset - designed for mobile consumption using HTML5 for
dynamic adaption to a range of devices
• Embraces industry technology standards (OpenSpcial, SAML2, CMIS)
• Enables secure and reliable integration with on-premise for leveraging
existing on-premise assets
• Fast branding and customization for multiple brands
High Level Architecture
Administration & Authoring – Creating Sites
Task Description
Create a new site Create a new site in the Site Directory. Open it for editing in the Authoring Space.
Create a page hierarchy
Define the structure of your site by adding pages and subpages.
Define page settings Define page name, page access level, and page navigation alias.
Assign a site theme Select a theme from the list of available themes, and apply it to your site.
Administration & Authoring – Working With Content
The main building blocks in SAP HANA Cloud Portal are widgets
developed using the OpenSocial standard.
• Document widget• HTML Viewer widget• Image widget• List Builder widget• Horizontal widget• Logon widget• Rich Text Editor widget• Navigation Menu• SAP Jam Feed widget• Social Networks widget• URL widget• Video Player widget
Administration & Authoring - Site Access and Permissions
Technical roles, used to manage site authoring permissions
Organization roles, used to manage access to published sites
Site Guest role, used to grant special access to individuals outside the
organization
Development Process
Developer Site Administrator Site Author
• Designs Solution
• Writes widget code
• Uses site CSS in widgets
• Deploys on SAP HANA Cloud Platform
• Adds widgets to Content Catalog(auto-discovery)
• Defines/modifies site themes
• Builds sites(create pages, places widgetson pages, defines navigation\hidden pages)
• Assign site theme• Customize widget
properties
On-Premise Integration
• Create a Gateway service using the Service Builder.
• Install and configure the SAP HANA Cloud connector.
• Define the SAP HANA Cloud destination.
• Deploy the destination to the Cloud Portal landscape.
Future Steps
• User experience and social
Expand site consumption, customization and social
enablement via rich toolsets and across any device
• Content management systems and integration
Enhanced integration capabilities across heterogeneous
environments via industry standards and protocols
• Platform
Leverage an enterprise scale cloud portal platform to
securely run your operation
• Development
Consume standards-based services to build, model and
configure portal environments from individual widgets to
complete portal sites
References
http://scn.sap.com/
https://help.hana.ondemand.com/
http://www.saphana.com/
https://store.sap.com/
Questions
Please complete this session evaluation
Thanks for attending
July , 2014
Transforming the BW landscape to HANA EDW in NW BW 7.40 SP07
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.
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 2 Internal
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.
Disclaimer
Agenda
Overview of SAP’s EDW Strategy
EDW with BW on HANA(BW + HANA)
BW + HANA + IQ
Success Stories
Agenda
Overview of SAP’s EDW Strategy
EDW with BW on HANA( BW + HANA)
BW + HANA + IQ
Success Stories
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 5 Internal
Now that Suite works on Hana, do I even need BW?“
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 6 Internal
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
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 7 Internal
Recent Challenges in EDW
Challenge Challenge
Category 2010 2011 2012
DB size (TB) A 3.2 4.8 5.5
DB growth per month
(GB) A 80 145 200
Average number of
users logged into the
system
A 150 200 400
Number of queries
per day A 4000 5000 8000+
Number of
Infoproviders (for
reporting)
B 395 1002 842
Number of DSOs B 263 292 320
A One is everything around
processing large amounts of
data, i.e. bulk loads, analytic
querying, table partitioning,
scalability, performance etc.
B Around the processes and the
data models inside the data
warehouse.
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 8 Internal
The Data Warehousing Quadrant data
volu
me
huge
modest
number of data models, sources, … modest huge
internet scale business process
(e.g. Ebay, Amazon, …) generating
huge amounts of (sensor) data
fairly modest challenges regarding
semantics, consolidation, harmoni-
zation, integration with other data
few data sources
mix of scenarios with small and
large amounts of data
many (1000s to 10000s) of data
models
many (100s to 1000s) different data
sources
data mart type of setup or
operational (OLTP) analytics
modest number of tables
modest (need for) integrations
between data models
VLDW XLDW
EDW Data Mart
more scenarios
more combinations of
scenarios
m
ore
gra
nu
lar
data
se
nso
r / b
ig d
ata
m
ore
sce
na
rios
HANA
BW
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 9 Internal
Benefits of Data warehouse
For Data consolidation
Mobility
Business Content
Planning – BPC Unified
Hot & cold data management- Big Data
Now that Suite works on Hana, do I even need BW?“
Yes ,to list out few reasons for the same
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 10 Internal
SAP’s strategic EDW solution SAP BW 7.4 on HANA
… simplify the data modeling processes
… increase the agility of the Enterprise Data Warehouse
… reduce the complexity of the EDW landscape
… combine the strengths of an SQL oriented approach with an Integrated EDW application
Only the combination of BW and HANA enables us to achieve the same
Seamless consumption of
data
Reuse BW services to manage and analyze
the data
One common modeling
environment
Process large amounts of data
faster
SAP EDW strategic Focus will be on HANA EDW (BW on HANA)
But Continue to Support RDBMS databases for existing and future releases
Agenda
Overview of SAP’s EDW Strategy
EDW with BW on HANA(BW + HANA)
BW + HANA + IQ
Success Stories
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 12 Internal
SAP BW on HANA – Smarter, simpler, more efficient Min 7.30 Sp05 >
How Does BW running on RDBMS differ from BW running on HANA ? Recommended 7.40
HANA Stack
RDBMS
Traditional Stack
SAP NetWeaver BW
Data Modeling
Planning
Data Management
OLAP Pro
ce
ss
Orc
hes
trati
on
Data Schema
&
Data
SAP BW on HANA
Data Modeling
Planning
Data
Management
OLAP Pro
ce
ss
Orc
hes
trati
on
Push Down
HANA as the Primary Database for BW and
Foundation for new Applications
Enhanced Data Modeling
Common Eclipse based Modeling Tools
BW/HANA Smart Data Access providing the logical
EDW
Easy integration of external data models with
Open ODS Layer
Further reduce data layers in BW via Operational
Data Provisioning
Push down further processing logic to HANA
BW Analytic Manager
HANA Analysis Processes
BW Transformations
PAK – Pushing down more planning semantics
Enhanced mobile enablement
Converged planning solutions
BW Content optimized for HANA
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 13 Internal
Modelling in BW on HANA
HANA
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 14 Internal
Common modeling tools SAP BW 7.4, SP5 on HANA
Common user experience via a central, unified modeling
environment
Attractive, flexible and simplified BW modeling tools
Harmonization BW and HANA modeling environments
Integration of BW and HANA models in one modeling approach
Integrated development & modeling environment across
– SAP HANA Modeler,
– BW Modeling
o New developed native Eclipse based modeling tools for
Open ODS View and New CompositeProvider
– ABAP Development Tools
– …
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 15 Internal
Modeling Tools – Maintenance CompositeProvider SAP BW 7.4, SP5 and on HANA
Common user experience via a
central, unified modeling environment
New metadata object CompositeProvider
as abstraction object of the query to the
underlying technical persistence objects
Left:
– Scenario definition (Join, Union)
Right:
– Join condition
– Mapping of Source Objects to Target
Output structure
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 16 Internal
Modeling Tools – Open ODS View SAP BW 7.4, SP5 and on HANA
Common user experience via a
central, unified modeling
environment
New metadata object • OpenODSView to integrate external
data models into BW
Left • technical Information about Source
Fields
Right • General information
• Associations to OpenODSViews or
InfoObjects
• Characteristic-specific Properties such
as Authorization relevance and
Referential Integrity
• Reporting Properties such as Display
and Query Filter behavior
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 17 Internal
Field based modeling in BW on virtual HANA Tables SAP BW 7.4, SP5 on HANA
* Pilot only
(Note 1922533)
Open ODS View offers
• Metadata object as an abstraction layer for underlying source
object
• HANA virtual tables as supported source objects via SDA
• Querying on field level
• Supported for Teradata, Sybase ASE/IQ, Hadoop
• Optimized Query execution by pushing down to HANA
Easy assignment of semantics
• Underlying object (Table, DB View, DataSource) can be tagged
as Text, Master data or Facts
• Single fields of the object can be linked to already existing
Open ODS Views or InfoObjects
Use case 1
• Access existing HANA application
• Migrate existing RDBMS model to HANA
and consume via BW
Use case 2
• Replicate data from RDBMS (e.g. external tracking system) into
DSO (with fields) leveraging BW services for delta calculation
and request management
Virtual Access BW Managed Persistence * Virtual Access
BW Query
Virtual Table
BW Query
Open ODS Layer Open ODS Layer
DSO w/ fields*
Persistent
Open ODS View
Virtual
Open ODS View
Virtual
Virtual Table
Smart Data
Access
BW on
HANA
External Sources
Table/View Table/View
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 18 Internal
Smart Data Access SAP BW 7.4, SP5 on HANA
Enhanced Business Flexibility by
providing “the logical EDW”
Data Federation in diverse EDW landscapes
• Smart data access – read access to relational and
non-relational sources via ODBC
• Enables access to remote data access just like
“local” table
• Supports data location agnostic development
• No special syntax to access heterogeneous data
sources
• BW based Analytic Services on external data
Scenario
• Make other DWHs transparent to HANA
• Non-disruptive evolution from virtual table to
persistent structure by establishing ETL without
major effort
• Consolidating / rationalizing the DWH landscape
• Consumption of HANA datamart scenarios from
second HANA database
HANA Smart Data Access Layer
Query
BW Virtualization Layer
Composite Provider, Open ODS View
Teradata
Hadoop SAP HANA
ASE
IQ
Virtual Tables HANA Tables
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 19 Internal
Automatic generation of HANA models SAP BW 7.4, SP5 on HANA
BW Schema
generates
HANA Schema
HANA
View
InfoCube
DSO
Master
data
HANA
View HANA
View
Enhanced
HANA View
Enhanced Metadata interoperability between BW and HANA
HANA Model generation
Triggered from BW InfoProvider – push
– Complements BW model import from HANA Modeler
– Analysis Authorization: Automatic sync between HANA and BW
– Object changes include HANA model impact analysis
Direct consumption of BW data via generated HANA views
– SAP Lumira, BO Explorer, SQL
Scenario
Major footprint of scenario in BW
Usage of generated view in HANA Studio to build own data
models using BW data and HANA native algorithms
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 20 Internal
High Cardinality InfoObjects SAP BW 7.4, SP5 on HANA
Enable business scenarios which require extremely
high volume of Master Data e.g. sales invoice analysis
High Cardinality InfoObjects
InfoObjects can be flagged as “High Cardinality”
– No SIDs generated
o Thus overcoming the 2 billion records limitation
– Support Attributes, Texts, Compounding, Time dependency
Can be used in Data Store Objects
Enabled for analysis and planning
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 22 Internal
Operational Data Provisioning (ODP) Technology SAP BW 7.4, SP5 on HANA
SAP ERP Extractors Operational
Data
Provisioning
HANA Views
Source BW Embedded Analytics
Target BW
SAP DataServices
SLT
Provider Subscriber /
Consumer
ODQ
Unified technology for data provisioning
and consumption
Enables extract once deploy many architectures
for sources
Unified configuration and monitoring for all
provider and subscriber types
Time stamp based recovery mechanism for all
provider types with configurable data retention
periods
Highly efficient compression enables data
compression rates up to 90% in Operational Delta
Queue (ODQ)
Quality of service: „Exactly Once in Order“ for all
providers
Intelligent parallelization options for subscribers in
high volume scenarios
*
*
*
*) New with SAP BW 7.4
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 23 Internal
Simplified trigger based table replication to BW with SLT SAP BW 7.4, SP5 on HANA
New source system type ODP-SLT
• SLT Real-Time push in Operational Delta Queue (ODQ)
• Direct Update to BW InfoProviders
Scheduled or real – time daemon
Automatic change notification for daemon
• Set up of SLT replication from SAP BW
Benefits
• Simplified data flow
• PSA no longer required
• Flexible recovery options
• Consumption of ODQ by multiple subscribers
• Reduced data latency
InfoProvider
DTP
ERP Source System
Table
Operational Delta Queue
(ODQ)
SLT
Operational Delta Queue
(ODQ)
SLT
Scheduled
scenario
SAP BW
Table
Real-Time
DTP
Real-time scenario
InfoProvider
ODP DataSource B ODP DataSource B
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 24 Internal
Open Hub Service SAP BW 7.4, SP5 on HANA
Extending the reach of Open Hub Service to
provide HANA applications with BW query and
InfoProvider data
• Export data from BW directly to tables residing in any
RDBMS supported by SAP
• Supported for Sybase ASE and IQ as well
• Delta extraction for InfoProviders and DataSource
• Query snapshots via QueryProvider are possible
BW Schema
SAP BW Schema
SAP HANA Schema(s)
SAP HANA *) Available since BW7.3
powered by SAP HANA SP8
*)
InfoCube
Query
MasterData
DSO
Any DB supported
by SAP
SAP BW
Open Hub
Service
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 25 Internal
The OLAP Compiler for HANA Intro
BW / BEX Query Designer
is the design tool for the Analytic Manager
Analytic Manager
has a wide variety of OLAP functions.
converts the query definition into a ABAP runtime object (BW
Query)
Generates calculation scenarios for those BW Query
operations which can be performed in HANA directly
Pushing down BW Analytic Manager (OLAP)
operations down to HANA provides
Excellent query performance
Additional business insights by overcoming existing ABAP
based limits – deep granular data can now be analyzed (e.g.
counters on order items level) In Memory Database
Calculation and Planning Engine
Row & Column Storage
BW / BEX Query Designer
BW Application Server
Analytic Manager
BW Query
Calc.-views /
Calc. scenarios
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 27 Internal
HANA Analysis Process SAP BW 7.4, SP5 on HANA
Enhanced analysis capabilities
Execute HANA-native functions
directly on BW InfoProvider data e.g.:
– Clustering, association algorithms,
regression analysis, anomaly
detection, weighted score, exponential
smoothing, etc.
Execute complex and data intensive
processes on HANA without loosing
the integrity and integration with the
BW environment
Materialize the result of a HANA
Analysis Process in HANA for further
processing – automated
Supporting also a scheduled batch
processing use case
Source Function Target
BW InfoProvider AFL(PAL, …), Procedure,
L-Script, R-Script BW InfoProvider
BW Process Management
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 29 Internal
Planning LOB enablement
Combine the best of three worlds to
a unique planning solution
(HANA, BPC, BW-IP)
Combines the
• …successful EPM Excel add-in
• …flexible BPC admin-UI
• …powerful BW-IP / PAK planning manager
• …super-fast HANA planning engine
Selected features
• Full PAK-model compatibility
• Business process flows (BPF)
• Work-status
• Data auditing
• Easy upload scenario
• LOB authorizations
BPC NW ‘unified’
(10.1)
BPC NW
• user experience
• collaboration
• data flexibility
BW-IP
• EDW-integration
• Built-in functions
HANA
• Unprecedented speed
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 30 Internal
BW Query & ODATA Services SAP BW 7.4, SP5 on HANA
Enable home-grown, straight forward planning
applications and embedded planning, e.g. on
mobile devices
Easy to use
• Queries flagged as ‘OData’ in the BEXQuery
Designer offer an external planning and reporting
Service interface
Standard compliant
• Fully integrated into the ODATA specification
Robust and flexible
• Stateless cell-wise data input-protocol
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 31 Internal
New Business Content optimized for BW on HANA
• New analytics combining capabilities of SAP
HANA and SAP NetWeaver BW
• Provides additional analytic solutions for
existing BW on HANA customers
• Follows the LSA++ architecture
• Provides higher level of details (line items, …)
• Implements mixed scenarios HANA Content +
BW Content
• Provides optimized transformation for HANA
• Offers more flexibility in data acquisition and
reporting
• Makes use of the consolidated InfoObjects
• Find further information in the SAP Help – BI
Content documentation and see the extended
presentations on the HANA optimized
Business Content in SCN
Agenda
Overview of SAP’s EDW Strategy
EDW with BW on HANA( BW + HANA)
BW + HANA + IQ
Success Stories
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 33 Internal
HANA DB
InfoProvider
Near-line
Storage
Acquisition
NLS Interface
BW
Access - very frequently frequently not frequently rarely
• Optimized NLS load
performance using IQ Loader
functionality
• SAP HANA and IQ share the
same columnar paradigm
• NLS data compression around
90%
• Can handle large data volumes
• Suitable for ad-hoc queries with
long history
• Minimum administrative effort
• Helps to optimize the memory
usage of HANA
Strategy & Definition BW powered by SAP HANA and Sybase IQ NLS
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 34 Internal
Big-data analytics: issues Dealing with volume, variety, velocity, costs, and skills
Big
Data
Analytics
Managing and harnessing terabytes of data
Volume
Harmonizing silos of structured and unstructured data
Variety Lack of adequate skills for nonstandard platforms and application programming
interfaces (APIs)
Skills
Keeping up with unpredictable data and query flows
Velocity
Very expensive to acquire, operate, and expand
Costs
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 35 Internal
BW powered by HANA and Sybase IQ Near-Line Storage (NLS) Architecture - Overview
BI Clients
SAP
HANA Sybase
IQ
MultiProvider Transient
Provider InfoCube/DSO
Near-Line SDK
SAP Netweaver BW 7.3x
Partner
OEM
BW NLS4IQ
SAP Native
An SAP - owned BW NLS
implementation for Sybase IQ offers a
fully integrated solution from one
provider
Main aspects:
• Deliver an ABAP-based
implementation of the BW NLS
interfaces
• Deliver a Sybase IQ DBSL ‘light’ that
covers all the needs of the above-
mentioned NLS implementation
• Sybase IQ to deliver reliable, high-
performance execution of the DBSL
driven loads and queries
• Availability since Q2/2013 SAP owned alternative to
existing NLS-Partner Solutions
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 37 Internal
… SAP HANA Extended Table HANA SP07
RT
DP
HANA
IQ
Col/Row
Table
Extended
Table
IQ Table
HANA Studio / Applications / Clients
Additional table type: Extended Table
Alternative Storage in IQ
Similar compression rates
Optimized data transfer between HANA and IQ
Data Processing can be pushed to IQ
Monitoring in HANA Studio
Joint Backup&Recovery across HANA and IQ
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 38 Internal
Extended Tables in HANA BW Use Case: Staging and Corporate Memory
RT
DP
HANA
IQ
A Table
IQ Table
BW DataSources and write-optimized
DSOs can have the property
“Extended Table”
Generated Tables are of type
“Extended”
Write and Read operations are re-
directed to IQ
All BW standard operations
supported – no changes
Only minor temporary RAM required
in HANA
DataSource
PSA Table
IQ Table
DataSource DataSource
wo-DSO wo-DSO
wo-DSO
Corporate Memory Staging Area
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 39 Internal
Extended Tables in HANA BW Use Case: Nearline Storage
RT
DP
HANA
IQ
NLS
IQ Table
BW IQ Server can be used for NLS
archive
Optimized data transfer from HANA
to IQ – no application server round
trip
DataArchivingProcess manages the
data transfer
Optimized Query access – similar to
SmartDataAccess optimization
Updates into NLS partitions possible
at any time
InfoCube /
DSO
Data Archiving Process
Trigger Control
INSERT AS SELECT
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 40 Internal
Big Data: 2.5 PB in #BWonHANA
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 41 Internal
BW 7.4 Feature Overview and Platform Availability - I
Topic Category HANA only
Extension of max. char. Value Renovation
Extra-long text Renovation
XXL-Attributes Renovation
High-Cardinality InfoObject (SID-less InfoObject) Renovation
BW Modeling Tools in Eclipse (Composite Provider, Open ODS View… ) Metadata&Modeling X
CompositeProvider Metadata&Modeling X
HANA Model Generation for BW InfoProvider Metadata&Modeling X
InfoObjects based on Calculation View Metadata&Modeling X
Inventory Keyfigures for DSO, VirtualProvider, CompositeProvider Analytic Manager X
OLAP: Calculation push-down AnalyticManager X
OLAP: Stock coverage keyfigure AnalyticManager X
OLAP: FIX operator AnalyticManager
OLAP: Multi-dimensional FAGGR AnalyticManager
OLAP: Current Member AnalyticManager
PAK enhancements AnalyticManager X
Planning on local provider in BW Workspace AnalyticManager X
Planning function push-down AnalyticManager X
Planning: ODATA & Easy Query extensions AnalyticManager
Planning: Support on HANA views for facts and master data AnalyticManager X
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 42 Internal
BW 7.4 Feature Overview and Platform Availability - II
Topic Category HANA only
Open ODS Layer – Open ODS View EDW X
Support of Smart Data Access EDW X
HANA Analysis Process EDW X
Transformation based on HAPs (In-Memory Transformations) EDW X
Field-based DataStore Objects EDW X
Bulk load capabilities EDW
Open Hub: Push data into a connected database EDW Operational Data Provisioning - PSA becomes optional – renewed integration with SAP extractors and renewed BW data mart scenario EDW
Operational Data Provisioning - ODQ for SLT EDW
Operational Data Provisioning – Dataservices Integration EDW
Data request house keeping EDW DTP for Hierarchies: extract multiple hierarchies request by request from PSA into data target EDW
Monitoring integrated in DBA cockpit for Sybase IQ NLS
Optimized Query-access to NLS data in Sybase IQ leveraging SDA NLS
Support to archive InfoProviders containing non cumulative key figures NLS
BW Workspace enhancements: Data Cleansing Misc X
Re-Modeling Toolbox Enhancements Misc X
New WebDynpro-based Masterdata Value Maintenance Misc
HANA-optimized BW Business Content Misc X
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 43 Internal
SAP NetWeaver BW on SAP HANA Get your own system today
Get your very own SAP NetWeaver BW on
SAP HANA with SAP BI 4.1 system today !
The BW 7.4 on HANA + BI 4.1 Trial
Agenda
Overview of SAP’s EDW Strategy
EDW with BW on HANA( BW + HANA)
BW + HANA + IQ
Success Stories
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 45 Internal
SAP BW Today (Feb 2014)
14500+ Customers
Vast majority: Central EDW, harmonizing many source systems
Embedded into mission critical business processes
200 New Installations/Month
3500+ BW 7.3 Customers
850+ BW on HANA Customers
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 46 Internal
Success Story..
Oil Market analysis for forwards trading: Formerly they were only able to analyze a 3 day
window of trading. It takes 11.5 hours on 22 TB Oracle instance. Each day's data has to be
duplicated to get the "performance", which is why the instance is so big. On HANA they are
able to analyze a 60 day window in only 20 minutes and that database it is only 0.5 TB due to
no data duplication and compression. The ROI was completely simple justified just on HW
alone
With BW they used to spend 80% of their time report writing and only twenty percent analyzing
data. With BW on HANA it is entirely the other way around
“With BW on HANA, we haven’t had the need to make a new info-cube in over a year and a half.”
© 2014 SAP AG or an SAP affiliate company. All rights reserved.
Thank you
Contact:
Dinesh [email protected]
Garimella Shashidhar [email protected]
SAP Labs India Pvt. Ltd.
Appendix
Unleashing Social Media Platform in Decision Making using SAP HANA
Abhinav Sharma
CSC India Pvt Ltd
Agenda
Introduction: Social Media – The power of Big Data
Challenges and Opportunities
SAP HANA – A platform for Big Data
Business Case Presentation
Summary and Key Takeaways
Why Social Data? Why Now?
Business is driven by set of challenges and set of questions to answer
Analytics is as good as the level of information, amount of information and
quality of information
Analyzing large volume of social data can be more effective and helps in
calculative decision making
No one wants to drive a car by looking into rear-view mirror.
Information must be available or readily available at the finger tips anytime
and anywhere
More and more data is being generated and now it becomes integral part
of life. ( Social Media )
Growth of Social Media – 2013
1. Mobile Phones increased from 60.3% to 818.4m in last two years
2. FB has 665m daily active users
3. Twitter is growing at 44% and monthly active users are 228m
4. Google+ growing at 33% and has 395m monthly active users
5. YouTube hours watched doubled
6. LinkedIn has active 200m users
7. In 2011, the amount of data surpassed 1.8 ZettaBytes
8. 46% of "business leaders“ planning to increase social media budgets
in 2014
The Challenge
Bring together a Large volume and Variety of Data to find New Insights
The Opportunity
Extracting Insights from an immense volume, variety and velocity of data,
in context, beyond that was previously possible
Velocity
Volume
Variety
Mobile
CRM Data
PlanningOpportunitiesTransactions
Customer
Sales Order
Things
Instant Messages
Demand
Inventory
:-)Brand
SentimentHigher NPS
360O Customer ViewLoyal Customers
Product Recommendation
More Sales
Propensity to Churn
Greater Retention
Real-time Demand/
Supply ForecastMore Efficient
Fraud Detection
Lower Risk
Risk Mitigation, Real-time
Retain Market Value
Asset TrackingIncrease
Productivity
Personalized CareLoyal
Customers
Decoding BIG DATA – Six V’s
New Approach
Business and IT identifyInformation Sources Available
IT delivers platform to exploreAvailable data and content
Business determines what questionsTo ask by exploring the data and relationships
New insights drive integration to Traditional technology
SAP HANA Platform – Big Data Approach
Different Types of Data
Data comes in many different shapes and sizes
Different Forms of DATA
Structured Data
Well-defined Content
Easily Understood
Stored in RDBMS
Unstructured Data
Not obvious structure
Process data to understand
Not suitable for RDBMS
Semi-Structured Data
Combines properties of both
Social Media falls under this category
Examples: Email, Social Media Feeds, Video feeds etc
Business Scenario
• Loading data from Twitter to SAP HANA System
• Text Analytics on Twitter data
Questions
Please complete this session evaluation
Thanks for attending
SAP Business Objects powered by SAP HANA
Presenter Name : Saurabh Raheja
Company Name : Infosys
Agenda
Overview of SAP HANA
Overview of SAP Business Objects
SAP BO BI suite 4.1 on SAP HANA
Generation of Reports
Business Scenario
In any Business Enterprise:-
1) The volume of data goes on increasing continuously
2) Speed at which data increases is high.
3) Variety of data sources are used e.g. Flat Files, RDBMS, etc.
Traditional Database :- It is used to store data on Hard disk drive(HDD).
Approach :- Query was sent to database layer, executed and data is
returned to Application Layer. All the Logics and Calculations were
performed at application layer.
As a result, there was latency in read and write operations as every
database R/W operation involves a heavy cost.
There were separate OLTP and OLAP systems.
These were the major bottlenecks of any business enterprise.
Solution to all these problems is SAP HANA.
What is SAP HANA??
• High Performance Analytical Appliance
• SAP HANA is an In Memory Database. Entire database is stored in
Main Memory e.g. RAM.
• SAP HANA is real-time data platform.
• Leverages technology through row and columnar storage, massively
parallel processing and data compressions. This allow organizations
to instantly explore and analyze very large volume of transactional
and analytical data.
• Reads data in few seconds which used to take 1 hour with traditional
databases.
• Cost effective, better compression techniques and performance due
to I/O operations
• SAP HANA also has a persistence layer e.g. Solid State Drive(SSD),
Flash Drive, etc. so that if power goes off we still have a backup of
data.
Cntd…
SAP HANA follows push down approach. No logics are processed at
Application Layer. Calculations and logics are done at the database
layer and result is returned to Application Layer.
Cntd..
SAP HANA allows us to leverage the benefits of both storage approaches
OLTP and OLAP systems.
OLTP systems(Row storage)
• High amount of write and update operations.
• Typically complete record needs to be accessed.
• Processes only one record at a time.
• Allows reading large number of attributes against single key
OLAP systems(Column Storage)
• High amount of read operations.
• Calculations are performed on a single column or few columns.
SAP HANA Architecture
SAP HANA Performance Benchmarks
The test system configuration is a 16-node cluster of IBM X5 servers with 8TB of total RAM. Each server has:
4 CPUs with 10 cores and 2 hyper-threads per core, totaling
40 cores
80 hyper-threads
512 GB of RAM
3.3 TB of disk storage
Data compression occurs during the data loading process. HANA demonstrated a greater than 20X compression rate. The 100TB SD data set was reduced to a trim 3.78TB HANA database, consuming only 236GBs of RAM on each node in the cluster .
The Reporting and Drill-down queries took 267 milliseconds to 1.041 seconds. This demonstrate HANA’s excellent ability to aggregate data
Why SAP HANA with Analytics?
• Analytics that unleash the power of collective insight
• Using analytics tools to collect massive amounts of Big Data from your
organization is one thing. Extracting meaning from that data and using
it to drive real growth is another. Business analytics from SAP can help
you unleash the power of collective insight by delivering enterprise
business intelligence, agile visualizations, and advanced predictive
analytics to all users – on any device or platform.
• Make fact-based decisions throughout your organization by relying on
our business intelligence solutions. Easily access relevant information
when and wherever you need it to better understand your business, act
quickly and confidently and ultimately achieve remarkable results.
• Provide intuitive, self-service access to business information
• Enable informed and rapid decisions based on reliable and real-time
business data
• Maximize visibility into the performance of your business network
• Simplify deployment and optimized use of IT infrastructure and
resources
SAP Business Objects
For analyzing data in HANA, SAP offers the SAP Business objects
Business Intelligence suite of products. SAP Business Objects BI platform
4.1 is a suite of front end applications.
The suite includes the following key applications:
• Crystal Reports -- Enables users to design and generate reports. SAP
Crystal Report 2013 can connect directly to tables and views in HANA
to create formatted report.
• Dashboards -- Allows users to create interactive dashboards that
contain charts and graphs for visualizing data
• Web Intelligence -- Provides a self-service environment for
creating ad-hoc queries and analysis of data.
SAP Business Objects Web Intelligence and Dashboards use
relational Universes to connect to HANA to analyze data and create
reports and visualizations. The universe can be based on views and
tables in HANA.
Crystal Reporting
• Quickly create highly formatted, pixel-perfect reports
• Connect to data sources across your organization – directly or through
a common semantic layer
• Deliver operational reporting that can help you make day-to-day
business decisions
• Give personalized reports to users in their preferred language, format,
and delivery method
Web Intelligence Reporting
• Deliver personalized business intelligence to your colleagues,
customers, and partners
• Improve productivity by giving users an intuitive tool and clearing IT
backlogs
• Improve ad hoc reporting and analytics across any data source with a
flexible framework
• Get the insights you need, when you need them, no matter where you
are
What is Universe in Business Objects?
Universe is semantic layer (middleware) between database and end users.
Universe contains Objects that map to actual SQL structures in the
database such as columns, tables, and database functions.
Objects are grouped into classes.
Objects and classes are both visible to Web Intelligence users
A schema of the tables and joins used in the database.
Web Intelligence users connect to a universe, and run queries against a database. They can do data analysis and create reports using the objects in a universe without having to know anything about, the underlying data structures in the database
What is the role of a universe?
Easy to use and understand interface for non technical Web Intelligence users to run queries against a database to create reports and perform data analysis.
As the universe designer, you use Designer to create objects that represent database structures. The objects that you create in the universe must be relevant to the end user business environment and vocabulary. Their role is to present a business focused front end to the SQL structures in the database.
SAP Business Objects Information Design Tool
• In SAP BusinessObjects 4.0, one of the major changes is the new "Information Design Tool" . It is a replacement for the old Universe Design Tool.
• Using Information Design Tool, you can build universe(UNX) that are stored in SAP Business Objects BI platform repository. The universe do not store data themselves
• Relational universes can be built directly on tables or views in HANA..
.
SAP BO BI 4.1 on SAP HANA
• Earlier the world was running SAP Business Objects on Oracle, SQL
Server, or DB2 as the application layer databases. In SAP Business
Objects BI4 SP4, SAP introduced the ability to rest those databases on
SAP HANA.
• SAP HANA can be added to existing landscape which may already
include data sources and business intelligence software.
SAP HANA Studio
• SAP HANA Studio is the front-end software delivered with HANA
• Enables administration of HANA database and modelling of data in
HANA to create views.
• One can also use Information Design Tool (IDT) included in SAP
Business Objects business Intelligence 4.0 platform, to create
universe based on HANA data.
• Using the Information modeler perspective in SAP HANA studio , one
can create analytical and calculation views in HANA based on the
data in underlying tables.
• The views are logical structure intended to facilitate analysis of
important data in the underlying table. The view do not store data
themselves.
Views in HANA
Using SAP HANA
Three major steps of using HANA :-
1) Loading data into HANA from an existing data source.
2) Modelling the data in HANA to facilitate data analysis
3) Analyzing the data in HANA using Business Intelligence tools.
For loading data into HANA there are two methods :-
1) SAP Landscape Transformation(SLT)-Used to move data from an
SAP ERP database or any SAP supported database into SAP HANA.
The data replication is done in real time, so changes in the original
data source are immediately replicated to HANA
Cntd..
Data Services Transformation- SAP BODS 4.0 can be used to move data
from any data source to SAP HANA. Provides both data transformation
and data replication functionality. Data is scheduled in batches.
Generation of Reports:-
• When setting up a universe that connects to SAP HANA, you must first
create a relational connection to the HANA database using JDBC or
ODBC drivers
• Once you have created a relational connection to SAP HANA, the next
step is to create the data foundation for the universe. When
connecting to SAP HANA, you can build your data foundation by
selecting the appropriate tables and creating joins between them, or
you can build your data foundation directly on a pre-existing analytic
or calculation view.
• Once you have built your data foundation on an SAP HANA view, you
can finish your universe by creating a business layer that specifies
the folders, dimensions, and measures that will be available to users
when they connect to the universe using one of the client tools.
Cntd..
• You can build a report directly on SAP HANA by creating a connection
using ODBC or JDBC drivers
• you will create an ODBC System Data Source Name (DSN) for SAP
HANA.
• Using the 32 bit ODBC Data Source Administrator, add a new system
DSN.
• Select the appropriate driver for SAP HANA.
• HDBODBC32 driver is automatically installed with SAP HANA client.
• Define the unique name and the server and port combination for data
source and then test the connection.
• Enter the credentials for the connection. You have successfully
connected to SAP HANA.
Cntd…
• Crystal Report can be generated by connecting directly to views or
underlying tables in SAP HANA.
• In crystal report 2013 create a blank report and choose the new ODBC
DSN as your data source.
• Select the newly created data source name.
• Note that the fields from the SAP HANA tables are available to be
added in your report.
Questions
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SAP HANA – Goal and Impact
Kumar Mayuresh
The Principal Consulting
Agenda
New Challenge of Data
SAP HANA Overview
Goal of SAP HANA DB
Impact of Modern Hardware on Database System Architecture
SAP HANA DB Tables
Memory Sizing
SAP HANA: Persistence Layer
Backup
Disaster Recovery
SAP HANA Approach to Business Needs
Application on HANA Landscape Impact
Application Based on SAP HANA Database
Delivery of SAP HANA
Q&A
New challenges with the data
Velocity
Volume Variety
Mobile
CRM Data
PlanningOpportunitiesTransactions
Customer
Sales Order
Things
Instant Messages
Demand
Inventory
:-)Brand Sentiment
Higher NPS
360O Customer View
Loyal Customers
Product Recommendation
More Sales
Propensity to Churn
Greater Retention
Real-time Demand/
Supply Forecast
More Efficient
Fraud Detection
Lower Risk
Risk Mitigation, Real-time
Retain Market Value
Asset Tracking
Increase Productivity
Personalized Care
Loyal Customers
SAP HANA Overview
Goal of SAP HANA DB
Executing Application Logic inside the Data Layer
Enabling New Types of Applications
High Performance and Scalability
Hybrid Data Management System
Compatibility and Standard DBMS Features
Support For Text Analysis, Indexing and Search
Multi-Tenancy
Impact of Modern Hardware on Database System
Architecture
The focus was on optimizing disk access, for example by minimizing number
of disk pages to be read into main memory when processing a query – But
today the performance bottleneck is now between the CPU cache and main
memory
Core
CPU
Performance bottleneck today:
CPU waiting for data to be
loaded from memory into cache
Performance bottleneck in the past: Disk I/O
Disk
CPU Cache
Main Memory
Characteristics of high performance data management system:-
In-memory database
•All relevant data must be kept in main memory, so read operations can be executed without disk I/O.Disk based index structures, for example, are not needed any more for an in-memory database. Diskstorage is still needed to make changes durable, but the required disk write operations happenasynchronously in the background.
Cache aware memory organization, optimization and
execution:
•The design must minimize the number of CPU cache misses and avoid CPU stalls because of memoryaccess. One approach for achieving this goal is using column-based storage in memory. Searchoperations or operations on one column can be implemented as loops on data stored in contiguousmemory arrays. This leads to high spatial locality of data and instructions, so the operations can beexecuted completely in the CPU cache without costly random memory accesses.
Support for parallel execution:
• In recent years CPUs did not become faster by increasing clock rates. Instead the number of processorcores was increased. Software must make use of multi-core processors by allowing parallel executionand with architectures that scale well with the number of cores. For data management systems thismeans that it must be possible to partition data in sections for which the calculations can be executedin parallel. To ensure scalability, sequential processing – for example enforced by locking – must beavoided wherever possible.
SAP HANA DB Tables : ROW Tables
Facts Interfaced from the Calculation/ Execution layer In-Memory store and persistence is managed in
the persistence layer Stores and retrieves data in rows, much like a
traditional relational database, except that the data is stored in memory
SAP HANA DB Tables : COLUMN Tables
• Facts• Interfaced from the Calculation/Execution layer• In-Memory store and persistence is managed in the
persistence layer• Optimized for read with efficient data compression
• Columnar Data Store Advantages• Optimized for reads• High data compression• Very fast data aggregation• Can be joined with row-based data
Memory Sizing
Source : SAP
Used Memory is the total amount of memorycurrently in use by SAP HANA. This is the mostprecise indicator of the amount of memory thatSAP HANA requires at any time.Resident memory is the physical memoryactually in operational use by a process• By default, Row store tables are loaded into
memory once HANA database starts• Column store tables are loaded into memory
when there is a query against that table• Column store table can be partially loaded
into the memory, that is, one or two selected columns can be loaded into memory
• Column store table can also be fully loaded into the table, depending on the query
Memory Sizing: Static Data
• Memory requirements for static data are derived from the database footprint of the
correspondingtables of the source system’s database system
• Database footprint in the source system must be determined using database-specific catalog
information (e.g., in Oracle: dba_segments; in DB2: syscat.tables)
• Database-specific scripts and more details on how to determine the database footprint can be found in
SAP Note 1514966
• Average compression factor in HANA memory = 7 : 1
• Note that this compression factor refers to uncompressed database tables and space for database
indexes is to be executed
RAMSTATIC = Source data footprint / 7 * c
Memory Sizing: Runtime Objects
• Additional memory is required for objects that are created dynamically
• When loading new data
• When executing queries
• Recommended to reserve as much memory for dynamic objects as for
static objects
RAMDynamic = RAMStatic
Total RAM is:
RAM = RAMDynamic + RAMStatic
= Source data footprint * 2 / 7 * c
Disk Sizing
Disk size for persistence layer. This does not cover backup space.
Diskpersistence = 4 * RAM
Disk size for log files/operational disk space
Disklog = 1 * RAM
Example
Source : SAP
SAP HANA: Persistence Layer
How Data from HANA Memory Is Written to Disk
Source : SAP
Backup
Source : SAP
Backups to file systemSAP Note 1651055 – Scheduling SAP HANA Database Backups in Linux
Backups to third-party backup toolsSAP Note 1730932 – Using backup tools with Backint for HANA
Disaster Recovery
Storage Replication only supports synchronous (for shorter distance); however, system replication supports asynchronous over longer distance
Disaster Recovery (contd)
Storage-based mirroring of SAP HANA disk areas controlled by storage technology
• Synchronous implementation
• Asynchronous implementation
WARM standby: DATA and LOG content is continuously transferred to secondary site under control
of SAP HANA database
• Fast switch-over times because secondary site has preloaded DATA
• Synchronous implementation
• Asynchronous implementation
HOT Standby: DATA content is only initially transferred to secondary site; afterwards, continuous
LOG transfer and LOG replay on secondary site
• LOG is provided to secondary site on transactional basis (COMMIT) controlled by SAP HANA
database (including initial DATA transfer)
• Fastest switch-over times, sec. site preloaded and rolled forward on COMMIT basis
• Synchronous implementation
• Asynchronous implementation
Disaster Recovery : Storage Replication
• Each node in Multi-Node HANA server comes with 512 GB memory• Benefits: Continuous replication of all persisted data, offers a more attractive RPO than backups• Limitation: Storage-based replication is only synchronous and limited to 100 KM. Requires a reliable high• bandwidth and low latency connection between the primary site and the secondary site.• Cost of implementation: Medium to high, depending on the exact business requirements and storage vendor
Disaster Recovery : System Replication
• Each node in Multi-Node HANA server comes with 512 GB Memory• If the DR distance is more than 100 KM, asynchronous system replication can be set up, but the RPO will be higher then synchronous
replication. Storage-based replication is synchronous and only supports DR up to 100 KM. Hence, HANA kernel-based system replication is the recommended
• option.• Benefits: Low to medium RPO and RTO depending on business requirement• Cost of implementation: Medium to high, depending on the exact business requirements
Disaster Recovery : TDI System Replication
• In this recommendation, Tailored Datacentre Integration HANA server is used. Existing certified SAN Storage can be used and only the HANA server without storage needs to be procured. Using TDI approach brings down the cost of ownership.
• Moving forward, SAP is planning to relax the network and allow users to leverage existing network infrastructure, which brings down the cost even more.
• Same 512 GB HANA can be used for both CDP (128 GB) and ERP (256 GB)• Benefits: Low to Medium RPO and RTO, depending on business requirements• Cost of implementation: Low to Medium, depending on the business requirements
SAP HANA Approach to Business Needs
• Focus: SAP ERP Acceleration – HANA used as an appliance
Data Acquisition: SAP ERP, SAP BW, and any non-SAP System.
• HANA used as a separate Appliance
• Can acquire real-time data from SAP ERP
• Can acquire data from any system as an ETL process
• Has the tools for complete lifecycle-like modelling, security, etc.
HANA (Native HANA)
• Focus: HANA as a database for painful transactional processing
• Data Acquisition: SAP ERP, SAP Business Suite
• HANA used as a database to host data from SAP Business Suite
• Can still acquire data from SAP ERP as real-time and via ETL from other sources
• ABAP can be leveraged to accelerate performance of “pain-areas
HANA (HANA Sidecar)
• Focus: HANA as a database for SAP BW, SAP BPC, SAP ERP
• Data Acquisition: SAP ERP, SAP BW, and any non-SAP system
• As a database to host SAP BW, SAP ERP …
• Acquire data from sources leveraging ETL/other processes, security, etc.
HANA (Application on HANA)
• Focus: HANA as a database for all SAP products
• Data Acquisition: SAP ERP, SAP BW, and any non-SAP system
• HANA as a database to host applications
• Acquire data from any non-SAP source
• Applications can be made leveraging HANA DB
HANA (Beyond SAP)
Application on HANA Landscape Impact
SAP HANA as Application Platform
Application Based on SAP HANA Database
Applications in the category “SAP HANA Applications” have theproperty of utilizing the SAP HANA database as their primarydatabase management system. Application logic is executedon the application server and SAP HANA database layer. TheSAP HANA database executes those parts of the applicationlogic that are data intensive and performance critical.
Architecture Overview of SAP HANA Applications
Different platforms are used for the application server layer of SAP HANA applications. For example:• The Next Generation ABAP Platform is used as the platform
for transactional and analytical applications, such as SAP Business by Design and High Performance Applications (e.g. Customer Analytics, Liquidity Risk Management).
• The SAP HANA based SAP NetWeaver Business Warehouse (BW) 7.30 SP5 is used as the platform for analytical SAP HANA applications such as Demand Signal Management.
• Some SAP HANA based Enterprise Performance management (EPM) applications use the Lean Java Server.
Delivery of SAP HANA
7 Key Points to Take Home
• SAP HANA is a platform and not just a database• SAP HANA database is based on In-Memory technology• While migrating SAP Business Suite and BW on HANA, a separate app server is required• SAP HANA provides the opportunity to unify transactional and analytical processing on the same
system• SAP HANA helps accelerate the business processes and provides real-time analytics capabilities• SAP HANA leads to smaller data footprint due to high compression• SAP HANA reduces TCO of overall landscape
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