ASUG 2014 - Big Data and Advanced Analytics

© 2014 Confidential and Proprietary, Cognilytics, Inc. Not to be distributed without express written permission.
September 17th, 2014
Big Data & Advanced Analytics Solutions
Data Foundation for Innovation
Speakers
Ravi Sundram- Sr Application Architect – SGI
Ravindra Shukla- Director Big Data- Cognilytics

2
About Speaker
 Ravindra Shukla
 Professional –
 Big Data, Advance Analytics
 PWC, IBM, Cognilytics
 IIT Bombay Graduate
 Personal –
 Outdoor Sports
 Author/Movie Director
 Astronomy/Relativity

3
Cognilytics Overview: Data To Decisions
Big Data & Advanced Analytics Solution Provider
with a mission statement to monetize data as a strategic asset.
Industry Use Cases
Modeling Language Scalability Distributed Computing Integration
Cross-sell and
Upsell
Sales & Marketing
Analytics
Supply Chain
Optimization
HR Attrition &
Diversity
Services Framework
Visualization
Technology Framework
SAP BODS, MDG,
Infosteward
HANA, Hadoop
SAP Infinite Insights, R,
SAS
Business Objects,
Lumira
Customer Churn
Brand Sentiment
Analysis
Demand
Forecasting
Customer
Segmentation
Anomaly
Detection
Predictive Analytics
Data Warehousing
and Modeling
Data Integration
Modeling Language Scalability Distributed Computing IntegrationHigh Performance Infrastructure
Predictive Asset
Maintenance

4
Excellence Awards
“Cognilytics recognized amongst Top 100 Most Promising Big Data
Companies 2014”
TiE50 Awards Program
Recognizing World's Most Enterprising Technology Startups
Cognilytics, Inc. receives a SAP® North America Partner Excellence
Award 2012 in the category “Top Consulting Partner – SAP North
America Services Partner Excellence Award 2012”.
Cognilytics, Inc. receives a SAP® North America Partner Excellence
Award 2013 in the category “Top Platform Solutions Partner – SAP
North America Services Partner Excellence Award 2013”.

5
Common issues surrounding data today …
 Redundant environments & tools
 High cost of current systems
 Poor data quality (both accuracy &
completeness)
 Non-integrated data across systems results
in inaccessibility of data from legacy systems
 Lack of consistent data architecture and
management practices
 Complex integration environments introduce
artificial dependencies
 Poor governance of enterprise data
architecture, data quality and master data
management
Common Business Barriers Common Technology Barriers
 No view into full lifecycle, cross business unit
cost / profitability of customers and
products
 No strategic approach to hypothesis
development based on data
 Multiple versions of the truth exist
 Not capitalizing on the wealth of information
in unstructured data
 Don’t understand cost of analysis vs. cost of
systems
 Introduction of new data sources takes too
long
 Inability to link and analyze data across
systems

6
© 2014 Confidential and Proprietary, Cognilytics, Inc. Not to be distributed without express written permission.© 2014 Confidential and Proprietary, Cognilytics, Inc. Not to be distributed without express written permission.
Data Evolution
Relational Database
Star Schema
Big Data
When scale of operation changes,
you need new solution!

7
Analytics Evolution
Raw
Data
Cleaned
Data
Descriptive
Reports
Ad Hoc
Reports &
OLAP
Predictive
Modeling
Predict Future
Events
Prescriptive
What happened?
Why did it happen?
What will happen next?
Take action
CompetitiveAdvantage
Analytics Maturity
The key is unlocking data to move decision making from sense & respond to predict & act
PredictiveDescriptive

8
We ensure all components of Data Management are addressed
Data Quality
Management
Data Security
Management
Database
Management
Data
Architecture,
Analysis &
Design
Data
Governance
Metadata
Management
Document
Record &
Content
Management
Data
Warehousing &
Business
Intelligence
Management
Reference &
Master Data
Management
1
2
3
45
6
7
8
Meta Data Management:
integrating, controlling and
providing consistent
definitions
Data Governance: planning,
supervision and control over data
and its use
Data Quality Management:
defining, monitoring and
improving data quality via
defined metrics and
measurements
Reference & Master Data
Management: managing
common and consistent non-
transactional data across the
enterprise
Database Management:
management and maintenance
of information stored in a
computer system
Data Security Management:
ensuring that the right
constituents have access do
data when needed
DCIS
Document Record &
Content Management:
ensuring that documents
are stored and easily
accessible across the
enterprise
Data Warehousing &
Business Intelligence
Management: ensuring
transactional data is
summarized and aggregated
into useful business value
Data Architecture, Analysis &
Design: ensuring development at
the project level adheres to data
modeling standards, procedures
and “Best Practices”
DB
Illustrative
Total Records: 1914
Percent of Quality: 61.44%
Number of Exceptions: 738
Number Passed: 1176
TxDOT Data Governance Review
Ready To Let (RTL) Date Exceptions Report
As of 02-04-2013
Projects are limited to those along the I-35 Corridor as well as in Houston, Dallas, San Antonio, Fort Worth and Austin areas
An exception is defined as any project where the Ready to Let Actual Date is less than 3 months before the District Let Date, or less than 2 months
before the District Let Date, if the responsible section is the district.
If the Ready to Let Actual Date is NULL the Ready to Let Baseline Plan Date is used. If the the Ready to Let Baseline Plan Date is NULL, the Ready to Let
Plan Date is used. If all Ready to Let dates are NULL, the record is considered an exception. All projects with a project class of RR, ROW, FS, PE or SRA
are considered acceptable.
DIST A DIST B DIST C DIST D DIST E DIST F DIST G
Manager
DaysRTL is
before District
Let Date
DaysRTL is
after District Let
Date
District Let
Date
Project Class Let Sched 3 PDP Code
Ready to Let
Actual Date
Ready to Let
Baseline Plan
Date
Read to Let
Plan Date
Actual Let
Date
Manager 256 2015-1-1 OV PL15 2015-9-14 2014-7-24
Manager 253 2015-1-1 OV PL15 2015-9-11 2014-7-11
Manager 77 2016-6-1 BR PL16 2016-8-17
Manager 2015-6-1 BWR PL15
Manager 74 2012-5-1 BWR PL12 2012-2-17 2012-2-17 2012-2-17 2012-5-1276201014 EXCEPTION DISTD 2012-2-17 ACTUAL
090248659 EXCEPTION DISTC NO DATES
009401033 EXCEPTION DISTB 2016-8-17 PLAN
001307078 EXCEPTION DISTA 2015-9-11 BASELINE
Responsible
Section
RTL Date RTL Date
Type
CCSJ Status District
Illustrative
Total Records: 1914
Percent of Quality: 61.44%
Number of Exceptions: 738
Number Passed: 1176
TxDOT Data Governance Review
Ready To Let (RTL) Date Exceptions Report
As of 02-04-2013
Projects are limited to those along the I-35 Corridor as well as in Houston, Dallas, San Antonio, Fort Worth and Austin areas
An exception is defined as any project where the Ready to Let Actual Date is less than 3 months before the District Let Date, or less than 2 months
before the District Let Date, if the responsible section is the district.
If the Ready to Let Actual Date is NULL the Ready to Let Baseline Plan Date is used. If the the Ready to Let Baseline Plan Date is NULL, the Ready to Let
Plan Date is used. If all Ready to Let dates are NULL, the record is considered an exception. All projects with a project class of RR, ROW, FS, PE or SRA
are considered acceptable.
DIST A DIST B DIST C DIST D DIST E DIST F DIST G
Manager
DaysRTL is
before District
Let Date
DaysRTL is
after District Let
Date
District Let
Date
Project Class Let Sched 3 PDP Code
Ready to Let
Actual Date
Ready to Let
Baseline Plan
Date
Read to Let
Plan Date
Actual Let
Date
Manager 256 2015-1-1 OV PL15 2015-9-14 2014-7-24
Manager 253 2015-1-1 OV PL15 2015-9-11 2014-7-11
Manager 77 2016-6-1 BR PL16 2016-8-17
Manager 2015-6-1 BWR PL15
Manager 74 2012-5-1 BWR PL12 2012-2-17 2012-2-17 2012-2-17 2012-5-1276201014 EXCEPTION DISTD 2012-2-17 ACTUAL
090248659 EXCEPTION DISTC NO DATES
009401033 EXCEPTION DISTB 2016-8-17 PLAN
Responsible
Section
RTL Date RTL Date
Type
CCSJ Status District
Districts
PDP Code
Project
Class
Data

9
Predictive Analysis Use Cases
Customer
Segmentation
Cross-
Sell/Up-Sell
Next Likely
Product Sale
Marketing
Analytics
Risk/Fraud
Detection
Predictive
Maintenance
Demand
Forecasting
Optimize
Capacity
Logistics
Strategy
Oil & Gas X X X X
Utilities X X X X X
Telco X X X X X X X
Healthcare X X X X X
Financial Services X X X X X
Insurance X X X X X
High Tech X X X X X X X X
Manufacturing X X X X X
Retail X X X X X
CPG X X X X
Agribusiness X X X
Sports &
Entertainment X X X X X
Travel & Tourism X X X X X
Professional
Services X X
Mining & Mineral
Processing X X X

10
Big Data & Advanced Analytics Use Cases
Healthcare
 Cancer Recurrence, Progression and Mortality Rates
 Inpatient Readmissions Analytics
 Payor, Diagnosis and Procedure Analytics
 Reduce Average Length of Stay (ALOS) in ICU
 Underpayment Recovery
 Automating Credit Resolution
Banking / Consumer Financial Services
 Strategic Customer Segmentation
 Banking Customer Churn/Retention
 Cross-Sell and Offer Optimization
 Mortgage Clustering
Insurance
 Sales & Claims Fraud
 Cross-sell and Up-sell
Manufacturing
 Predictive Asset Maintenance
 Machine To Machine Analytics
 Scenario-Driven Demand Forecasting
 Environmental Health & Safety
 Build Plan with Constrained Manufacturing Capacity
 Warranty Valuation Analysis
 Materials Requirements Forecasting and Inventory PlanningRetail
 Strategic Customer Segmentation
 Cross-sell and Offer Optimization
 Next Likely Product Sale – Market Basket Analysis
 Customer Churn Analysis
 Promotion and Coupon Effectiveness
 Loyalty Management: Program Health & Diagnosis
 Store Ops - Margin Analysis
Payments Industry
 Top of Wallet Analysis and Strategy
 Usage Trigger Campaigns
 Loyalty Management: Program Health & Diagnosis
 Cross-Sell and Offer Optimization
Cross-Industry
 Employee Turnover/Retention Analysis
 Evaluating Workers Compensation Claims
 Predictive Intrusion Detection
CPG
 Cross-sell and Upsell
 Brand Sentiment Analysis
 Scenario-Driven Demand Forecasting
 Customer Segmentation & Offer Optimization
 Pricing & Margin Analytics
 Marketing Mix Modeling
Model Risk Management
 CCAR Stress Testing
 Anti Money Laundering
 Basel Capital Allocation Models
 Credit Card Acquisition Risk Model
 Mortgage Insurance Model

11
Role of Visualization in problem Solving
“Know your data..”

12
© 2014 Confidential and Proprietary, Cognilytics, Inc. Not to be distributed
without express written permission.
Data Interpretation challenges
4 + 1 Not Equal to 5 Always?

13
Role of visualization in solving problems -
“In school days, we are taught that if there are four
animals in a room and you add two more, the total will
be six. That is logic. But behind this logic, there are
underlying assumptions. Now, if somebody tells you,
there are four rats in the room and if you add two more
cats in the room, how many animals in total exist in the
room now? The answer will depend upon assumption.
If you just use your mathematical brain, you will say six
animals. If you use your human brain, you will say two
animals.”
Role of Visualization in problem Solving – Cognitive Ability

14
Know your data
5 FISHES

15
Advance Analytics
Big Data
Powerful Machine with sophisticated
Mathematical algorithms
Human Intelligence – Advance Analytics
Input
Hana App
SGI -UV
Cog
Data Science

16
 Utilize onsite/offshore delivery model to contain cost
 Create HANA environment with R Integration / Predictive Analytics
 Identify data sources and design data extraction strategy
 Design and build data models in HANA (SAP Studio)
 Build sample data by converting transactional data structure to monthly snapshots
 Perform data discovery and modeling, including data cleansing, derived variable
creation, variable selection and data visualization
 Develop predictive models to identify key drivers associated with a high/low probability
of attrition and predict those employee segments in a given time period
 Develop model operationalization roadmap and transfer knowledge
 Industry: E-Commerce Discretionary
 Location: San Jose, CA
Business Problem
Solution Approach
Value Delivered by Cognilytics
Company Background
Scope: SAP HANA, Predictive Analysis, SAP BusinessObjects,
Data Model design, Predictive Model design
 Employee retention- Losing an employee leads to monetary,
productivity and intellectual capital loss as well as lower
customer satisfaction and revenues.
 Current systems do not provide capabilities to conduct
predictive analytics
 The structure of data can not be used for revealing employee
attrition insights and predictions
 Provide a analytical environment for predictive across multiple
disparate data sources
 Provide date structure fits predictive analytics needs
 Provide actionable insights into employee data that can help
prevent attrition and churn
 Provide a roadmap that the Employee Attrition Model can be
operationalized in production environment

17
 Industry: Consumers and Pharmaceuticals
 Location: New Jersey
 Analyzed current system and migrated POC from interim IS to AWS IS
 Prep AWS environment - Install HANA, BOBJ and DS
 Migrate HANA data model, XS components, Dashboard, DS systems and Data to AWS
 Verify migrated system – Basis/Admin, Verify Application Components
 Schedule the regular data provision options and delivery of reports to Mobile devices
 System installed, migrated and configured
– SAP BODS 4.2, HANA DB 1.0 SP6, HANA XS
– SAP Business Objects 4.1, Webi, BO Explorer, Business Objects Mobile 4 (apps for
iPAD, iPhone)
Solution Approach
Company Background
 Performance issue in data analytics
 Mobile delivery platform
 Efficient scalability and infra-structure support
 Inability to use real-time data
 High performance and high availability
 Optimized use of IT infrastructure and resources, Addressed
performance issues and enabled faster data execution
 Built a Dynamic, Flexible and a High-performance Enterprise
data warehouse solution in AWS - HANA as data mart
integrated with BOBJ and DS
 Mobility enable delivery, Secured as per organizational need.
 Non-disruption solution with High availability and high
performance
Business Problem Value Delivered by Cognilytics
Scope: Migration to AWS infra-structure & enhancement
BODS 4.2, HANA 1.0 SPS 6, HANA XS, BOBJ 4.1

18
 Created demo and provided comparative study among creating DWH by physically
moving data from MS SQL Server into HANA
 Optimized the long running reports using SAP HANA with two server cluster.
 Installed and configured BusinessObjects DataServices 4.1 environment to
replicate the data from MS SQL Server to HANA.
 Incremental loads are created to make HANA highly available for both US
and European data loads
 Industry: Toy & Board Games Manufacturing
 Location: Pawtucket, Rhode Island
Solution Approach
Company Background
 Slow response time on Key Business reports
 Complex data architecture resulting in slow delivery of business
solutions
 Sub-optimal utilization of existing BW infrastructure
 Optimized utilization of BW infrastructure
 Improved user adoption of BW reports and dashboards
 Achieved faster response time on key business reports
 Simplified data architecture ready to fulfill future business
needs
Scope: BusinessObjects Data Services 4.1, SAP BW
optimization, SAP HANA optimization

19
 Industry: Manufacturing
 Location: Maryland
Strategy – Decide technical strategy , roadmap and integration approach, prioritize Phase-I
and Phase-II scope and implementation approach
Enterprise Data Warehouse - Design EDW based on HANA enterprise solution to support
HANA data-mart and SAP BW need. Data from multiple Non-SAP source systems is staged in
HANA and data from SAP ECC is staged thru SAP BW. These data sets are then consolidated
and transformed for the reporting requirements. SAP HANA and Analysis Authorizations
within SAP BW were used to secure the access to data.
ETL – SLT, MDG, SAP BODS 4.2. Reporting - SAP BOBJ 4.1 as the reporting layer. Dashboard
4.1 – Management KPI. Operational Reporting – WebI and Explorer. Reports are accessed via
both Web using BI LaunchPad and on mobile devices using SAP BusinessObjects mobile app.
Solution Approach
 High manual operation across multiple plants
 No single source of truth, Issue with accuracy and
integrity of Data, Analytical system for business users
 Performance issue and frequent break-down
 Inability to execute operational reports in SAP
 Challenges in tracking high priority business
imperatives across the lines of business
 Global and flexible delivery model including mobile
 Analyzed the existing system and create roadmap for new implementation
and enhancement, Define technical strategy and integration of various SAP
and Non-SAP systems across various LOB – HR, Finance, Manufacturing,
SHE, Commercial, SCM, procurement. Prioritize Phase-I and Phase-II scope
and categories top management dashboard KPI & operational reports
 implemented HANA enterprise solution consisting of following
components - SAP HANA Enterprise supporting Data-mart and BW
(SAP HANA 1.0 SP6, SLT, BW 7.4, BODS 4.2), BOBJ reporting tools.
Scope: SAP LOB – HR, MGF, Finance, SHE, Commercial, SCM, Procurement, Non-
SAP - OMP, Hyperion, Taleo, MS, Oracle. SAP BW 7.4, BW 7.0, ECC 6.0, HANA 1.0
SP6, SLT, BODS 4.2, BOBJ 4.1 - tools
Company Background

20
Deployed the following:
 Managed multiple data bases (MS SQL, My Sql, License DB) and integrated in SAP BW
by using DB connect, Business Object Data Services Tools.
 Reports using SAP BW from their Legacy Applications DB data and SAP ECCi
 SAP BusinessObjects 4.0 suite Webi and Dashboards for Revenue, Sales Booking on IPad
and iPhone.
 Created 45 customized reports
 Delivered Webi and provided smooth go-live support while laying the foundation
for sound data strategies
 Designed and Developed Revenue ( COPA and Sales) and Sales Booking Dash Boards.
 Software Used: SAP ECC/ BW / ABAP, SAP Dashboard 4, SAP BusinessObjects 4.0 Webi,
SAP BO Data Services 4. SAP BusinessObjects Mobile 4
 Industry: High Tech
 Location: Santa Clara CA
Solution Approach
Company Background
 Fragmented enterprise reporting on multiple environments
 High Total Cost of Ownership due to multiple legacy systems
 Lack of a trusted and unified information source for effective
decision-making, impacting the overall enterprise effectiveness
 Provided Architecture and design and developed scalable Data
Models
 Deployed “easy to use” dashboards on iPad and iPhone
 Deployed Master data harmonization for data accuracy
 Developed Web-Intelligence Reports and exposed its data using
BI Services
Scope: SAP BW 4.0, SAP Business Objects 4.0 Suite/Mobile
4.0, WebI, SAP Dashboard 4.0, BO Data Services 4.0,

21
 Created 5 training case scorecards to measure key data quality dimensions and quality trends
over time for:
– Accuracy – Completeness
– Conformity – Uniqueness
 Established user and group security and integrated with Windows AD for SSO
 Implemented SAP Data Quality tool – MDG and related processes
 Created failed data universe and reports to provide row-level insight into established business
rules for data failures
 Developed Data Services real-time job as web service providing address cleansing and
suggestion lists
 Designed website showcasing use cases for real-time address cleansings and suggestions
 Industry: Banking
 Location: California
Solution Approach
Company Background
 Lack of proper tools and processes for enterprise data
governance resulting in customer dissatisfaction
 Sub-optimal usage of existing BusinessObjects infrastructure
 Optimized use of IT infrastructure and resources
 Established standardized process for enterprise data quality and
data governance
 Trained internal IT resources on BusinessObjects, enabling them
to meet future business needs
Scope: SAP Data Quality Tool, Master Data Governance (MDG)

22
 Migrated all the manufacturing legacy mainframe application to SAP
 Used SAP AIO methodology with Data Services 3.2/SQL Server 2008 on Windows Server
 Setup multi-User environment for Data Services
 Central Repository is set up to maintain the multiple versions of the Data Services Code
 Used SAP Data Insight to profile data and also to identify data patterns
 Cleansed the data for migration to achieve best data quality
 Provided integrated testing support for 3 test cycles
 Achieved 95% success rate on average for 2 migration test cycles
 Reconciled data and reports to measure the success rate and validity of the loads
 Created technical documentation
 Provided ongoing mentoring of 5 Lockheed developers on Data Services
for next implementation cycle
 Industry: Aerospace, Defense & Security
 Location: Morristown, New Jersey
Solution Approach
Company Background
One Runtime
Architecture
&
Services
Unified Metadata
ETL
Data Quality
Profiling
Text Analytics
 Multiple Legacy systems adversely impacting the overall
information quality
 Lack of a trusted and unified information source for effective
decision-making impacting the overall enterprise effectiveness
 High Total Cost of Ownership (TCO) with multiple systems to
maintain
 Consolidated enterprise reporting into a unified platform
and increased speed in delivering relevant information
 As a result of the SAP platform consolidation, streamlined
business processes across geographically-dispersed locations
 Enabled one source of reporting across the enterprise with
reliable data
Scope: SAP AIO, Data Services 3.2, SAP Data Insight

23
Speaker – Ravi Sundram
 Professional
– As Architect, Security, Database / System Management in US/Canada/Australia
– Mission critical Operational & Non Operational role from large Corporations to
small Corp.
– SGI, Rackable Systems, Intuit, PG&E….
 Personal –
– Outdoor activities in nature.
– Yearly long drives. Longest one 4000+ miles from Toronto to California.
– Gardening/Farming. Remote management of coconut plantations, offshore.

24
SGI Introduction
1996: Origin2000,
MIPS R10K
2003: Altix3000, Intel Itanium
2010: UV Gen1, Intel Xeon
 20 years of In-Memory
Computing Leadership
 100 + SGI in-memory patents

25
SGI SCM Analytics use case
Problem statement
 SGI’s supply chain management and planning organization relied on manual
and labor intensive processes to gather key performance metrics
– Reports were not available frequently enough to make informed decisions
– There was an inability to react to the information given the delayed access
 Trend analysis was not readily available to the business and was a key executive
requirement
 The business did not have the ability to drill down into data sets at a more
granular level to understand and analyze root cause

26
SGI SCM Analytics use case
Project benefit
 Supply Chain Management
– Streamlining data analysis process using SAP BI /HANA
– Reduce time and effort to prepare reports
– Improve reporting accuracy and timeliness of the report delivery
 Governance
– Single Source of Truth (SSOT) and moving rules and user processes to HANA
– Standardized definitions for data components, leading to improved data profiling & quality.
 Linkage from top KPI to detail reporting
– fosters analysis and decisions – actionable information
 Self-service Business Intelligence (BI)
– Provide user with many self-service reporting/visualization tools through SAP BI
 IT organization
– Implementation of visual data mart reduce maintenance needs for large disk based infrastructure
– Self service reporting/visualization option empower user and lessen the development work for IT

27
Current Architecture/Data Preparation
SAP BI 4.1
Sybase DB Server
BOE Application Server DS Job server
Dev DS
repo
Excel
Ora SCM
SCM
ANALYTICS
DEV Schema
SAP HANA
SUSE 11 UV2000
Current Architecture
CMS
UAT_SCM_A
NALYTICS
UAT Schema
PRD_SCM_A
NALYTICS
PRD Schema
BI Launchpad Dev rpt
Folder
Prod rpt
Folder
Test DS
repo
Prod DS
repo
Dev Job
server
Test Job
server
Prod Job
server
Others
Sources
Tomcat web application server (web portal)
(CMC, BI Launchpad, DS Management Console)
Semantic layer (universes)

28
Presentation
Layer
DW
Layer
Extraction
Layer
Source
Layer
To-Be System Landscape Strategy
SAP BI DEV SAP BI ProdSAP BI QA
SAP
Solution
Manager
for HANA
FLZ/ Flat
files
CRMoDClarify
Oracle
ERP
Rev
Pro
Agile
Physical Architecture
HANA
Server
(UV300)
HANA
Server
(UV300)
Dev
Instance
Test
Instance
Prod
Instance
Solman
Lumira
…
Solman
Lumira
…
BODS DEV/QA BODS PRD REP PRDREP DEV/QA

29
Why SGI UV300- Scale Up!
4 Socket
(3TB)*
CQ4 2014
4-Socket
Units
8 Socket (6TB)*
Oct 2014
16 Socket
(12TB)*
CQ1 2015
32 Socket
(24TB)*
CQ2 2015
Backup Storage+
UV300 Scales as Single Node
*rack configurations
subject to change

30
4 Socket
(3TB)
8 Socket
(6TB)
16 Socket
(12TB)
32 Socket32 Socket
(24TB)
Sweet Spot!
Single Node Landscape
Designed to deliver a “future proof” architecture.

31
H/A Simplified
Name
Server
Index
Server
Data
Volume
Log
Volume
Primary Node
Name
Server
Index
Server
Data
Volume
Log
Volume
Failover Node
Name
Server
Index
Server
Data
Volume
Log
Volume
D/R Node + (Test/Dev)Synchronous
mirrored
redo log
writing
Asynchronous
mirrored redo
log writing
Ship
Incremental
data
Ship
Incremental
data
Multi-Tier System Replication
• Easy to deploy and administer.
• Uses only native HANA/Linux tools
• No third party storage replication SW mgmt. or cost.
• Flexible options to meet RPT/RTO objectives today or in the future

32
Thank You!
North Americas Headquarters APAC Headquarters
Cognilytics Inc.
5900 Silver Creek Valley Rd.,
San Jose, CA 95138
Cognilytics Software and Consulting Pvt. Ltd.
2nd Floor, Tower 9B, DLF Cyber City – III,
Gurgaon, Haryana, India 122002
Corporate Headquarters
900 North McCarthy Blvd.
Milpitas, CA 95035
Tel: 669-900-8000, www.sgi.com

ASUG 2014 - Big Data and Advanced Analytics

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