sap hana in-memory analytics - harness the power of real-time

8/18/2019 SAP HANA in-memory Analytics - Harness the Power of Real-time

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SAP HANA: Harness the power of real time

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SAP HANA in memory analytics: Harness the power

of real time

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR MASTER IN

INFORMATION MANAGEMENT

2014-2015

ROLL NO: MIM –I - 130

Jamnalal Bajaj Institute of Management Studies

Mumbai University


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ACKNOWLEDGMENT

Firstly, I thank University of Mumbai and Jamnalal Bajaj Management Institute of Management

Studies for giving me this opportunity. This project would not have been possible without the

support of the following:

1. My project guide who enlightened me with the subject and guided me throughout the

project duration.

2. My colleagues from my organization who shared Valuable material for the project

3. College library for providing reference material, books, articles and journals for this

project

4. I would also like to thank my seniors, friends, as well as executives from my

organization who advised me on various aspects of the topic and providing there valuable

guidance on the project.

Also, I would specially like to thank all the professors of this institute for their continuous

guidance and support to complete the project on time.


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EXECUTIVE SUMMARY

In today’s extremely competitive world, organizations need to undergo transformation in order to

compete with the best and create value for their stake holders. Many companies have identified the

benefits associated with ERP’s long back and implemented it as means of strategic investment to

help them get an edge over the others. There are several success stories of various organizations that

have used ERP’s in a very productive manner. SAP is the most common ERP used by almost all

organizations today. SAP has been around from quite some time with it’s different modules like

Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), Product

Lifecycle Management (PLM), Supply Chain Management (SCM), Supplier Relationship

Management (SRM), SAP Business Intelligence (BI) and many more.

The latest and hottest addition to the above list of SAP products is SAP HANA which stands for

"High-Performance Analytic Appliance”. It is an in-memory, column-oriented, relational

database management system developed and marketed by SAP AG. It is a completely re-imaged

platform for real-time business.

The scope of this project is to understand the needs associated with the implementation of SAP

HANA, the actual implementation process of SAP HANA in an organization, the functionalities

which are available once an organization implements SAP HANA successfully, to understand the

different features incorporated in SAP HANA which transforms future with better business

insights using predictive analytics, approaches to HANA adaptation, to observe as to how SAP

will leverage SAP HANA to accelerate the transition of its business to the cloud and lastly to

bring an outlook with respect to how businesses are responding to this changing environment

and their approach towards the same.


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Several case studies of organizations are taken into consideration to identify the causes which has led

to success of SAP HANA implementation in their business whereas hesitation or failure in adaptation

of the same by the others. Based on analysis of these case studies critical success and failure factors

associated with SAP HANA implementation is collated.


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Table of Contents

Acknowledgement ....................................................................................................................... 2

Executive Summary ..................................................................................................................... 3

1. Introduction ............................................................................................................................ 8

1.1 Background of the study .................................................................................................... 8

1.2 ERA of ERP ...................................................................................................................... 9

1.3 ERA of SAP .................................................................................................................... 10

1.4 Concept of SAP HANA .................................................................................................. 15

2 Theoritcal & Conceptual Framework ............................................................................... 17

2.1 Why SAP HANA ............................................................................................................ 17

2.2 Competitive advantage.................................................................................................... 20

2.3 Design & Implementation ............................................................................................... 21

2.3.1 Technical/Architecture- Overview…………………………………………...21

2.3.2 Extended System Landscape - Overview……………………………………30

2.3.3 Deployment - Overview…………………………………………………….33

3 Research Methodology ....................................................................................................... 38

3.1Purpose of study…………………………………………………………….…….38

3.2 Research Objective…………………………………………………………….…….38

3.3 Research Design…………………………………………………………….…….....38

3.4 Types of Research ............................................................................................................ 39


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3.5 Hypothesis.............................................................................................................. …….39

3.6 Limitations ...................................................................................................................... 39

3.7 Data Collection ............................................................................................................... 40

3.8 Sample Design ................................................................................................................ 41

3.9 Tools of data analysis...................................................................................................... 41

4 Data interpretation & Analysis.......................................................................................... 42

4.1 Primary Data ................................................................................................................... 42

4.2 Secondary Data ........................................................................................................... 52

5 Research findings & Recommendations ........................................................................... 72

5.1 Critical success & Failure factors ................................................................................... 72

5.2 Key recommendations .................................................................................................... 72

References .................................................................................................................................. 78

Annexure .................................................................................................................................... 80


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List of Figures

Figure 1.1 Worldwide ERP Software Market Share 15

Figure 2.1 Business benefits by HANA 17

Figure 2.2 SAP HANA system technical overview 22

Figure 2.3 SAP HANA index server 26

Figure 2.4 SAP HANA extended system landsacpe 30

Figure 2.5 Detailed landscape of SAP HANA 31

Figure 2.6 SAP HANA – open source, R language 32

Chart 4.1 Geographical footprint of organization 42

Chart 4.2 Period since first SAP implementation 43

Chart 4.3 Current Adoption level 44

Chart 4.5 Location of HANA instance 46

Chart 4.6 Netprofit of organization 47

Chart 4.7 Reasons for not adopting HANA 48

Chart 4.8 Benefits of SAP HANA 50

Chart 4.9 Performance gain of SAP HANA 50


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CHAPTER 1: INTRODUCTION

1.1 Background of the study

Numerous methods have been tried over several years for mapping the functions of business on

software however most of them have been limited to conversion of data from legacy systems

to new systems till the advent of ERP Systems. Many projects have failed, new systems

were not up to the mark and this meant additional cost and loss of business. With the arrival of

ERP, all this has changed.

Advent of ERP’s has led to introduction of new tools, custom methodology databases

and applications leading to effective and efficient utilization of resources & increase in

overall productivity. Motivation level of employees has improved focus on budget has made

executive level contribute with more enthusiasm.

In order to prepare for the arrival of the year 2000 (Y2K), many companies were engaged in

implementations of standard business software applications, the enterprise systems

particularly such as ERP, and supply chain management systems. While these software

systems solved the immediate problem of Y2K compliance, they were typically implemented

with an emphasis on speed and the need to fix the Y2K problems. The scope of data

conversion from the legacy to the new systems was not sufficient. Focus on business processes

was required in order to leverage the capabilities of a software to maximum. Need arised to re-

engineer the business processes completely for technology-driven business dynamics such as

the implementation of e-business applications, ERP, B2B, SCM or Data Warehousing


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applications.

1.2 Era of ERP

Major motivation for an ERP was data visibility. Because ERPs are highly integrated, they

have the potential to make much better decision-making information available to managers.

This visibility, which gives an end-to-end view of supply chain processes, was expected to

improve operating decisions. In addition, data visibility helped to present a single face to

distributed customers and to recognize global customers as a single entity. The impact of data

visibility was expected to extend to strategic decision making. The online, real-time transaction

processing characteristic of ERPs can provide current rather than historical information on a

firm's performance thereby facilitating increased responsiveness to market conditions and new

internal capabilities.

Advantages and Disadvantages

ERP systems can support a company’s work in many ways. Since ERP systems integrate all

parts of a company seamlessly, more proper control is possible. ERP systems are able to

minimize redundant data registration; control data produced by different departments, and

reduce registration errors. The interconnectivity among all the modules of ERP systems

reduces the time to perform the different operational tasks, so the company’s efficiency can be

increased. ERP systems enable users to access timely information and accurate reports can be

produced at any time. The main reasons that companies undertake ERP systems are

summarized as follows:

• Integrate financial information.

• Integrate customer order information.


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• Standardize and speed up manufacturing processes.

•

Reduce inventory.

• Standardized HR information.

There are also few drawbacks of ERP system, which can be given as under

• Inflexibility

• Long implementation periods

• Overly hierarchical organizations

1.3 ERA OF SAP

SAP (Systems, Applications & Products in Data Processing)

SAP is the world's largest inter-enterprise software company and the world's third largest

independent software supplier. It is one of the largest software companies in the world. It is an

European multinational software corporation that makes enterprise software to manage business

operations and customer relations. It is headquartered in Walldorf, Baden-Württemberg, Germany,

with regional offices around the world. Five software engineers at IBM in Germany had the

idea for a cross-functional information system. However, the idea was rejected by IBM, so the


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engineers founded their own company in 1972.

R/2 was SAP’s first integrated system, which ran on mainframes. Introduced in 1992 R/3, the next

version of the system was a client/server system. mySAP which is the successor to SAP R/3, is

the first service-oriented business application in the market, based on SAP Net Weaver, an open

integration platform that allows new applications to be developed. SAP has about 94,170

customers, 75 million users, 17500 installations, more than 2,700 partners and a share of over

25 percent of the ERP market. SAP’s strength is the breadth and extensive capability of its

software’s functionality. SAP spends much more on R&D than any other competitor. SAP Net

Weaver became the first platform to allow seamless integration among various SAP and

non-SAP solutions, reducing customization and solving the integration issue at the business

level. The solution of SAP regarding the integration issue is the use of open standards that allow

software applications to be accessed as web services.

Products:

SAP's products focus on Enterprise Resource Planning (ERP). The company's main product is SAP

ERP. The current version is SAP ERP 6.0 and is part of the SAP Business Suite. Its previous name

was R/3. SAP ERP is one of five enterprise applications in SAP's Business Suite. The other four

applications are:

• Customer Relationship Management (CRM) – helps companies acquire and retain customers,

gain marketing and customer insight

• Product Lifecycle Management (PLM) – helps manufacturers with product-related information

• Supply Chain Management (SCM) – helps companies with the process of resourcing its

manufacturing and service processes


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• Supplier Relationship Management (SRM) – enables companies to procure from suppliers

Other major product offerings include SAP Business Intelligence (BI), SAP Business Objects

(BO), SAP HANA, NetWeaver platform, Governance, Risk and Compliance (GRC) software, Duet

(joint offering with Microsoft), Performance Management software and RFID. SAP offers service-

oriented architecture capabilities (calling it Enterprise SOA) in the form of web services that are

wrapped around its applications.

While its original products were typically used by Fortune 500 companies, SAP now actively

targets small and medium sized enterprises (SME) with its SAP Business One and SAP Business

All-in-One.

Benefits of SAP

Many companies are realizing that SAP solutions have become extremely important to their

businesses. This realization is arising from the fact that many Fortune 500 companies use SAP

systems extensively internally for their daily operations and reporting. Over time, many companies

start to discover deficiencies in their information systems architecture. Most legacy business

systems were made up of islands of automation – separate systems that handled some core business

needs. There may have been systems to handle the General Ledger, another to handle the sales

processes, a separate system to manage the manufacturing or production processes, etc. Data had to

be exchanged between these sub-systems in order to generate the reports that various levels of

management needed to run their operations. When there were errors or inconsistencies between

these sub-systems, these flowed on into the consolidation process and skewed the management

reports.


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SAP ERP systems – Business Suite / R/3 and Business One – are fully integrated business real-

time systems. They enable transactions to be processed end-to-end and eliminate data

inconsistencies between sub-systems. Many companies replace their old business systems with the

best-of-breed ERP package developed by SAP. SAP delivers systems that are modern and highly

efficient. Their support infrastructure is unparalleled in the software industry. In addition, SAP has

developed industry-specific ERP solutions that address the needs of over 28 industrial sectors. SAP

is present in more than 120 countries worldwide. SAP has managed to build up a client base of

more than 12 million users worldwide. Those numbers are estimated to grow as more and more

enterprises jump on the SAP ERP bandwagon. The main business benefit of using SAP is that you

get a comprehensive set of integrated, cross-functional business processes.

Here are some other benefits of using SAP:

• Align Strategies and Operations

Prior to the implementation of an SAP system, a thorough analysis of the current environment was

done. This was usually referred to as the ‘As-Is’ analysis. All issues were identified for rectification

during the project. The short, medium and long-term strategies of the business were identified,

clarified and prioritized. All internal workflows were aligned to enable the eventual effective use of

the SAP system. In the next major step, the future state of the business’ information system was

specified. This was referred to as the ‘To-Be’ state. It was the responsibility of the project team and

possibly consultants to bridge the gaps between the ‘As-Is’ and ‘To-Be’ states to build a workable

project plan.

• Enhance Productivity and Insight


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The productivity enhancements arising from an SAP implementation can be tremendous. Data is

only entered once. There are no sub-systems to consolidate and verify. Data can be forwarded to

others within the organization by efficient workflows – using internal messages, emails, SMS alerts

or other means. Operations can be authorized and passed along for the next person to process.

Employees can process many transactions on their from various access terminals. This can include

leave applications and submission of expense claims. The overall reporting and analytics

environment is enhanced to a level that facilitates operation management.

• Minimize Costs by Increasing Flexibility

In order to improve process standardization, efficiency and adaptability, SAP relies on enterprise

services architecture. SAP extends its business eco-system by extrapolating transactions,

information and collaborative functions.

•

Reduce Risk

Solves complex business challenges as trusted partner for long-term growth, with over 30 years of

experience working with organizations of all sizes in more countries than any other vendor.

• Improve Financial Management and Corporate Governance

Financial and management accounting functionalities combined with business analytics offers the

SAP user deep visibility into their organizations. Furthermore SAP increases profitability,

improves financial control, and manages risk.

• Optimize IT Spending

SAP integrates and optimizes business processes as well as eliminates high integration costs and

the need to purchase third-party software. Use of SAP incrementally improves cash flow and

reduces costly borrowing.

•

Gain Higher ROI Faster


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A business can deploy SAP by using rapid-implementation techniques which cost less than half

what traditional approaches cost. It is also possible in certain cases for companies to reduce

implementation time and costs by leveraging on preset defaults and pre-packaged versions

available for specific industries.

As on 2013, SAP continued to be the market leader with about 24% share followed by Oracle

having about 12% market share. Rest of the market was split up by small players in the industry.

Market Size was about 25.4B$ which was about 3.8% more than that in 2012.

1.4 Concept of SAP HANA

SAP HANA, short for "High-Performance Analytic Appliance" is an in-memory, column-

oriented, relational database management system developed and marketed by SAP SE. It is

massively parallel, thus exploiting the maximum out of multicore processors and subsequently


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enabling very fast query execution. SAP HANA originates from developed or acquired

technologies, including TREX search engine, an in-memory column-oriented search engine,

P*TIME, an in-memory OLTP database acquired by SAP in 2005, and MaxDB with its in-memory

liveCache engine. In 2008, teams from SAP SE working with Hasso Plattner Institute and Stanford

University demonstrated an application architecture for real-time analytics and aggregation,

mentioned as "Hasso's New Architecture" in SAP executive Vishal Sikka's blog. Before the name

HANA settled in, people referred to this product as New Database.

The product was officially announced in May 2010. In November 2010, SAP SE announced the

release of SAP HANA 1.0, an in-memory appliance for business applications and business

Intelligence allowing real-time response. The first product shipped in late November 2010. By mid-

2011, the technology had attracted interest but the conservative business customers still considered

it "in early days". HANA support for SAP NetWeaver Business Warehouse was announced in

September 2011 for availability by November.

In 2012, SAP promoted aspects of cloud computing. In October 2012, SAP announced a variant

called HANA, one that used a smaller amount of memory on Amazon Web Services for an hourly

fee.

In January 2013, SAP enterprise resource planning software from its Business Suite was

announced for HANA, and became available by May. In May 2013, a software as a

service offering called the HANA Enterprise Cloud service was announced. Rather

than versioning, the software utilizes service packs.


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CHAPTER 2: Theoretical & Conceptual Framework

2.1 Why SAP HANA

SAP HANA is a market-disrupting technology, providing cost-effective management of large

volumes of data, simultaneously allowing analysis of current and complete information to

provide immediate answers to any question in “real real-time.”


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• The in-memory technology lets users explore and analyse all transactional and analytical data in

real time from virtually any data source. Source-agnostic data access & integration services allow

accessing and indexing external data from across the entire organisation and adding them to

existing analytical models.

• Real-time analytical processing can be performed to analyse business operations in real-time using

huge volumes of detailed information while business is happening.

• Data can be aggregated from many applications and data sources without perturbing in any way the

on-going business transactions.

• Views of business information can be persisted in a Persistent Data Repository, and reconstituted in

case of a crash.

• Real-time Replication Service can be used to access and replicate data from SAP ERP.

• Tight integration with SAP Business Objects BI solutions for insight and analytics.

• SQL and MDX interfaces for third- party application access.

• Unified information modelling and design environment. The great advantage here is that all data

models are purely virtual, and calculate results based on the underlying detailed operational data.

• Simplification of existing models, of modelling and re-modelling.

• Reduced costs through simplifications in hardware, maintenance and testing.

• Simplified Operations and Monitoring with the integration of basic HANA administration

capabilities with the BW Admin Cockpit.

• Unified information modelling and design environment. The great advantage here is that all data

models are purely virtual, and calculate results based on the underlying detailed operational data.

• Simplification of existing models, of modelling and re-modelling.

• Reduced costs through simplifications in hardware, maintenance and testing.


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• Simplified Operations and Monitoring with the integration of basic HANA administration

capabilities with the BW Admin Cockpit.

• With BW in-memory-optimized objects, complex analysis and planning scenarios with

unpredictable query types, high data volume, high query frequency, and complex calculations can

be processed with a high degree of efficiency.

• Loading SAP HANA-optimized BW objects can also be done more efficiently.

• The SAP HANA database replaces both any previous database and SAP NetWeaver BWA,

reducing infrastructure costs. Instead of both database administration tools and additional SAP

NetWeaver BWA administration tools, the SAP HANA database requires just a single set of

administration tools for monitoring, backup and restore, and other administrative tasks.

• Data modeling is simplified. Using in-memory-optimized objects you do not need to load a BWA

index, for example. In addition, the architecture of the HANA datbase allows you to delete

characteristics from an InfoCube that still contains data.

• With its high compression rate, the column-based HANA datastore requires less data be

materialized.

• With BW in-memory-optimized objects, complex analysis and planning scenarios with

unpredictable query types, high data volume, high query frequency, and complex calculations can

be processed with a high degree of efficiency.

•

Loading SAP HANA-optimized BW objects can also be done more efficiently.

• The SAP HANA database replaces both any previous database and SAP NetWeaver BWA,

reducing infrastructure costs. Instead of both database administration tools and additional SAP

NetWeaver BWA administration tools, the SAP HANA database requires just a single set of

administration tools for monitoring, backup and restore, and other administrative tasks.


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• Data modeling is simplified. Using in-memory-optimized objects you do not need to load a BWA

index, for example. In addition, the architecture of the HANA datbase allows you to delete

characteristics from an InfoCube that still contains data.

• With its high compression rate, the column-based HANA datastore requires less data be

materialized.

2.2 Competitive advantage

Information is an asset companies can use to make better decisions. The capability to capitalize on

this asset remains one of the highest priorities for organizations of all types. However, delivering

on this capability for everyone in the organization remains elusive. In-memory computing is a

disruptive force that provides the speed and agility to power analytics at exceptional performance

levels while remaining cost effective. In summary, SAP HANA built on Intel(r), Xeon(r)

processor 7500 series delivers:

• Speed and agility: The business imperative for rapid change is creating new demands for

business and technology. The need to get all the right information to business users, without

the delay of typical enterprise data warehouses, is critical to the use of data as a competitive

differentiator

• Performance and cost: New hardware technologies and advances in software have

dramatically improved performance, with similar reductions in costs, making new

computing models possible.

• Alignment of business and IT: Business requirements demand that business analysts have the

flexibility to define their views of the information and the application. Efficient IT

departments strive for low redundancy and high reuse of system, information, and human

resources.


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• More efficient data processing: Traditional disk-based data warehouses have limited

capability to benefit from major technology trends such as multicore CPUs, in-memory

processing, and columnar storage. The move to SAP HANA engine is a move to a

foundation that can truly fulfill the promise of real-time business now and in the future.

• Technology to power business analytic applications: All industry-specific solutions and

functional areas of business share common information needs. At the same time, every

organization is unique in the way it can use data to enhance business in new ways.

Customers need the capabilities of powerful technology to use all their data with ease, so

they can flexibly model their business in a rapidly changing, competitive

All the above factors enables a firm to hold a strong and competitive position in the market.

2.3 Design and implementation

2.3.1 Technical/Architecture – Overview

The SAP HANA database is developed in C++ and runs on SUSE Linux Enterpise Server. SAP

HANA database consists of multiple servers and the most important component is the Index

Server. SAP HANA database consists of Index Server, Name Server, Statistics Server,

Preprocessor Server and XS Engine.


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Index Server:

o

Index server is the main SAP HANA database component

o It contains the actual data stores and the engines for processing the data.

o The index server processes incoming SQL or MDX statements in the context of

authenticated sessions and transactions.

Persistence Layer:

The database persistence layer is responsible for durability and atomicity of transactions. It ensures

that the database can be restored to the most recent committed state after a restart and that

transactions are either completely executed or completely undone.

Preprocessor Server:

The index server uses the preprocessor server for analyzing text data and extracting the information

on which the text search capabilities are based.

Name Server:

The name server owns the information about the topology of SAP HANA system. In a distributed

system, the name server knows where the components are running and which data is located on

which server.


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Statistic Server:

The statistics server collects information about status, performance and resource consumption from

the other servers in the system.. The statistics server also provides a history of measurement data

for further analysis.

Session and Transaction Manager:

The Transaction manager coordinates database transactions, and keeps track of running and closed

transactions. When a transaction is committed or rolled back, the transaction manager informs the

involved storage engines about this event so they can execute necessary actions.

XS Engine:

XS Engine is an optional component. Using XS Engine clients can connect to SAP HANA

database to fetch data via HTTP.

The heart of SAP HANA – Index server

The SAP HANA Index Server contains the majority of the magic behind SAP HANA.

Connection and Session Management

o This component is responsible for creating and managing sessions and connections for the

database clients.

o Once a session is established, clients can communicate with the SAP HANA database using

SQL statements.


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o For each session a set of parameters are maintained like, auto-commit, current transaction

isolation level etc.

o Users are Authenticated either by the SAP HANA database itself (login with user and password)

or authentication can be delegated to an external authentication providers such as an LDAP

directory.

The Authorization Manager

o This component is invoked by other SAP HANA database components to check whether the

user has the required privileges to execute the requested operations.

o SAP HANA allows granting of privileges to users or roles. A privilege grants the right to

perform a specified operation (such as create, update, select, execute, and so on) on a specified

object (for example a table, view, SQLScript function, and so on).

o The SAP HANA database supports Analytic Privileges that represent filters or hierarchy

drilldown limitations for analytic queries. Analytic privileges grant access to values with a

certain combination of dimension attributes. This is used to restrict access to a cube with some

values of the dimensional attributes.

Request Processing and Execution Control:

o The client requests are analyzed and executed by the set of components summarized as Request

Processing and Execution Control. The Request Parser analyses the client request and

dispatches it to the responsible component. The Execution Layer acts as the controller that

invokes the different engines and routes intermediate results to the next execution step.


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SQL Processor:

o Incoming SQL requests are received by the SQL Processor. Data manipulation statements

are executed by the SQL Processor itself.

o

Other types of requests are delegated to other components. Data definition statements are

dispatched to the Metadata Manager, transaction control statements are forwarded to the

Transaction Manager, planning commands are routed to the Planning Engine and procedure

calls are forwarded to the stored procedure processor.


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SQLScript:

The SAP HANA database has its own scripting language named SQLScript that is designed to

enable optimizations and parallelization. SQLScript is a collection of extensions to SQL.

o SQLScript is based on side effect free functions that operate on tables using SQL queries for

set processing. The motivation for SQLScript is to offload data-intensive application logic

into the database.

Multidimensional Expressions (MDX):

MDX is a language for querying and manipulating the multidimensional data stored in OLAP

cubes.

o Incoming MDX requests are processed by the MDX engine and also forwarded to the Calc

Engine.

Planning Engine:

o Planning Engine allows financial planning applications to execute basic planning operations

in the database layer. One such basic operation is to create a new version of a data set as a

copy of an existing one while applying filters and transformations. For example: planning

data for a new year is created as a copy of the data from the previous year.

o

Another example for a planning operation is the disaggregation operation that distributes

target values from higher to lower aggregation levels based on a distribution function.

Calc engine:

o The SAP HANA database features such as SQLScript and Planning operations are

implemented using a common infrastructure called the Calc engine.


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o The SQLScript, MDX, Planning Model and Domain-Specific models are converted into

Calculation Models. The Calc Engine creates Logical Execution Plan for Calculation

Models. The Calculation Engine will break up a model, for example some SQL Script, into

operations that can be processed in parallel.

Transaction Manager:

In HANA database, each SQL statement is processed in the context of a transaction. New sessions

are implicitly assigned to a new transaction. The Transaction Manager coordinates database

transactions, controls transactional isolation and keeps track of running and closed transactions.

When a transaction is committed or rolled back, the transaction manager informs the involved

engines about this event so they can execute necessary actions.

The transaction manager also cooperates with the persistence layer to achieve atomic and durable

transactions.

Metadata Manager:

o Metadata can be accessed via the Metadata Manager component. In the SAP HANA database,

metadata comprises a variety of objects, such as definitions of relational tables, columns, views,

indexes and procedures.

o Metadata of all these types is stored in one common database catalog for all stores. The database

catalog is stored in tables in the Row Store. The features of the SAP HANA database such as

transaction support and multi-version concurrency control, are also used for metadata

management.


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In the center of the figure you see the different data Stores of the SAP HANA database. A store is a

sub-system of the SAP HANA database which includes in-memory storage, as well as the

components that manages that storage.

The Row Store:

The Row Store is the SAP HANA database row-based in-memory relational data engine.

The Column Store:

The Column Store stores tables column-wise. It originates from the TREX (SAP NetWeaver

Search and Classification) product.

Persistence Layer:

The Persistence Layer is responsible for durability and atomicity of transactions. This layer ensures

that the database is restored to the most recent committed state after a restart and that transactions

are either completely executed or completely undone. To achieve this goal in an efficient way, the

Persistence Layer uses a combination of write-ahead logs, shadow paging and savepoints.

The Persistence Layer offers interfaces for writing and reading persisted data. It also contains the

Logger component that manages the transaction log. Transaction log entries are written explicitly

by using a log interface or implicitly when using the virtual file abstraction.


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2.3.2 Extended System landscape - Overview

Above figure provides an example illustration of the various components and applications that

make up the extended SAP HANA system landscape. The various components / applications are:

• SAP BusinessObjects BI Suite and SAP HANA Information Composer are types of

Business Intelligence tools

• The “primary persistence” scenario, exemplified by SAP BW and SAP Business Suite

•

SAP HANA Apps.

• R runtime.

• Host Agent, SMD Agent x SAP HANA Studio.

• SAP Solution Manager.

• Data provisioning scenarios: SLT; SAP Data Services, DXC


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Another way to view the extended SAP HAN– section 3.3SAP A Extended System Landscape is

by examining the various types of connections that exist with other applications and components:

Above figure illustrates the connections that exist to various components, applications, and

entities in the extended SAP HANA Extended System Landscape. Details are provided about the

nature of the interface, and the relevant port number ranges are visible as well


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SAP HANA Predictive Capabilities and Open Source R Language

SAP HANA includes native capabilities which enable predictive analytics, such as the Predictive

Analysis Library. One such aspect is integration with the open source language called R. R is an

open source programming language and software environment for statistical computing. The R

language is widely used among statisticians and data scientists in developing applications which

feature the use of predictive analytics.

The above figure depicts the SAP HANA predictive scenario utilizing the R open source language.

The R client has been included within SAP HANA; the R runtime must be installed on a separate

server than the SAP HANA server hardware. This architecture provides the ability to develop

predictive applications and functionality where data physically resides inside SAP HANA, but the

R language can be invoked to access the data in SAP HANA. Essentially, the application running

on SAP HANA requests the R server to run a modeled predictive algorithm on the data residing in

SAP HANA. The operation in the R sever will read the necessary data from SAP HANA, transfer


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the required data set to the R server, and then perform the predictive operation in the R server. The

result set of that operation is then returned to SAP HANA.

2.3.3 Deployment – Overview

There are various approaches to deploying SAP HANA itself and applications and

scenarios that can run on SAP HANA. On-premise options are for single-host, scale-up, scale-out,

and there is concept of Tailored Data Center Integration. There is also option for deploying SAP

HANA on the cloud.

On-Premise:

The default option for installing SAP HANA on-premise is the appliance delivery model. This

means the hardware partner provides factory pre-installation for hardware, operating system, and

SAP software. The hardware vendor may add their own specific best-practices and SAP HANA

software configuration. Additionally, the hardware vendor finalizes installation with an on-site

setup and configuration of the SAP HANA components. Different technical architectures of SAP

HANA appliances exist depending on the particular hardware partner, which is manifested in the

approaches to scalability. The primary architectural difference is in the provision of (disk) storage.

Some utilize dedicated Storage Area Network (SAN) block storage, some use dedicated Network

Attached Storage (NAS) file storage, and some opt for directly attached storage devices, in

combination with a clustered file system.


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Single Node Systems and Scale-Up

A single node SAP HANA is made up of one and only one hardware server, and thus a single node

system is the most straightforward type of SAP HANA installation. While this is often a choice for

production SAP HANA environments, this is also common configuration for systems used for

development, test/QA, and other purposes such as sandbox, training, demo, etc. as well. A single

node SAP HANA system can support capacity planning through the scale-up concept. Scale-up

essentially means that additional CPU, memory, and disk capacity can be added to an existing

single node system, and the additional resources can be utilized effectively and efficiently

Scale-up offers the following set of benefits, when compared to scale-out:

• Performance advantages: no overhead of network communication between hosts

particularly efficient use of all available resources (especially main memory) possible.

• Cost benefits may exist depending on hardware partner.

•

Support for virtualization (non-production).

Scale-up offers the following constraints when compared to scale-out:.

• Hardware of same size required for HA

• Less total hardware capacity than with multi-node

Single-node SAP HANA systems with scale-up are typically deployed in the following scenarios:

• Moderate amount of data and/or concurrent operations expected

• SAP Business Suite systems

• Non-production systems, such as QA, test, development, sandbox, etc.

• Virtualized non-production systems

• Custom data marts

• Relatively small SAP NetWeaver Business Warehouse system


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Multiple Node SAP HANA Systems and Scale-Out

SAP HANA supports multiple instances of the same system, each on separate server hardware.

Data is distributed across the SAP HANA nodes and is data processing takes place on these

separate hardware servers. Indeed, with a scale out system, separate hardware servers are combined

together to form a larger unit. A scale-out system is characterized by different types of index server

processes

Scale-out offers the following set of benefits, when compared to scale-up:

• Extensive scalability to handle large amount of data and/or concurrent operations

• Table distribution automated for SAP NetWeaver Business Warehouse

• A small number of standby nodes is sufficient for HA (fail-over) of an SAP HANA multi-

node cluster.

Scale-out offers the following constraints when compared to scale-up:

• Table distribution/partitioning required (currently automated for SAP NetWeaver Business

Warehouse on SAP HANA only)

• Additional rack and storage system may be required when a server node is added

(depending on hardware partner configuration options)

Single-node SAP HANA systems with scale-up are typically deployed in the following scenarios:

• Large amount of data and/or concurrent operations expected.

• SAP NetWeaver Business Warehouse system with extensive active data volume


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Tailored Data Center Integration for SAP HANA

As an alternative to the appliance delivery approach, SAP HANA tailored data center integration

provides a flexible approach which, depending on the scenario, may offer advantages for total cost

of ownership. It may be possible with this approach to reduce hardware and operational cost by

reusing existing hardware components (such as existing enterprise storage) and familiar IT system

management processes. Additionally, it may be possible for the customer to gain additional

flexibility in hardware vendor selection by leveraging the existing partner ecosystem.

The restrictions involved in utilizing the tailored data center integration approach are as follows:

• The server is listed in the SAP HANA product availability matrix at service.sap.com/pam

•

Variations such as no local disks, no flash cards required, and additional Fiber

Channel adapters for SAN boot are allowed.

• The storage solution has successfully passed SAP HANA hardware certification.

• The installer of SAP HANA must have has passed the certification exam E_HANAINS131

SAP HANA Cloud Deployment Options

In addition to the on-premise deployment options, SAP HANA is also available alternatively in a

cloud deployment. There are several cloud deployment options available for SAP HANA, which

are geared toward different use cases and needs:

• SAP HANA One

• SAP HANA Developer Edition


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• SAP HANA Cloud Infrastructure

•

SAP HANA Cloud Platform

• SAP HANA Enterprise Cloud

SAP HANA Technical System Deployment Options

The various different types of technical deployment options, such as:

• Standard deployment.

• More than one SAP HANA database SID running on one SAP HANA hardware system

• More than one application running on one SAP HANA database

• Virtualization.


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CHAPTER 3 : Research Methodology

3.1 Purpose of study

The purpose of this report is to analyze the procedures & benefits of an SAP H A N A

implementation in various sectors. The report tries to identify the critical factors which can

lead to Success/Failure of SAP HANA implementation. The purpose is to understand and analyze

how SAP HANA would benefit a business and help them to have upper hand in the market. The

study would include analyzing the effects of inclusion of SAP HANA in one’s business. Along

with the analysis, the study would also include the statistical information as to what the current

organizations using SAP feel about the SAP HANA implementation in business.

3.2 Research Objective

• Exhaustive study on the process of SAP HANA implementation.

• Study of SAP HANA implementation in an Organization.

• Discussing about SAP HANA’s advantages & disadvantages.

• To analyze the benefits/drawbacks arising in an organization after SAP H A N A

implementation.

• To identify the Critical Success Factors which can help an organization benefit from a SAP

HANA implementation

3.3 Research Design

The research design used for this research is HYPOTHESIS TESTING. The primary reason for the

same is the responses of the questionnaire that was provided to record the reactions of the

organizations to SAP HANA. Based on the responses, inferences can be made about SAP HANA


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as the future of in-memory analytics.

There are multiple case studies which are analyzed, based on the CASE STUDY inferences are

drawn based on success ratios with respect to increase in statistics of number of companies

welcoming SAP HANA.

3.4 Type of Research

The research is carried out by analyzing the SAP H A N A implementation of major

companies

The research is q u e s t i o n n a i r e a n d case study based and inferences are drawn

based on the success and failures of various S A P H A N A functionalities, after analyzing

the above companies

3.5 Hypothesis

SAP HANA implementation is always beneficial for all current organizations in any sector.

3.6 Limitations

This report is limited on the extent of the comparison of the SAP HANA implementation i n

v a r i o u s c o m p a n i e s o f t h e m e n t i o n e d c a s e s t u d i e s Tata Steel / Rolls Royce /

Tata Motors & Whirlpool and the theoretical framework. The report does not cover the

selection of SAP HANA systems over it’s other competitors by observing no other

systems besides SAP HANA. Because of a restricted period of time the interview was

conducted with a single manager of a company, so the conclusions cannot be generalized.

Also for couple of organizations getting primary data was difficult and secondary data was used


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to analyze the success/failure of SAP HANA Implementation.

3.7 Data Collection

3.7.1 Primary Data Collection:

The primary data for this research is collected through questionnaires. A structured questionnaire

was designed to find out from managers of various organizations who currently use SAP about

their reactions and willingness to adopt SAP HANA .

**A copy of questionnaire, which is used to collect the primary data from the respondents, is

attached in the annexure**.

3.7.2 Secondary Data Collection:

Most important sources of secondary information was from case studies of leading organizations

from various sectors who have already implemented SAP HANA.

Other secondary sources were SAP forums, SAP journals and other internet sources.

3.8 Sample Design

3.8.1 Target Population

As the customers are globally spread and are from different sectors, so are the target companies

too, the target population selected for the survey are the working officials from various

organizations who use SAP. The survey takers belong to varied age groups, geographies, profiles

and cultures too.


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3.8.2 Sampling Frame

The sampling frame is the age group above 18 globally (majority from Mumbai itself), people

with more than 10 years of work experience

Since the actual users of the system would be the managers, the sampling frame is concentrated on

the same people.

3.8.3 Sampling Unit

The sampling unit is an adult individual from the corporate sector.

3.8.4 Sampling

The sampling method used for the research is non-probability – judgment sampling. The sample

included equal number of respondents from each age group states in the questionnaire. Judgment

sampling was considered for the reason, having equal number of respondents from each age group

would give a sufficiently accurate response representing the target population.

3.8.5 Sample Size

The sample size (p) selected for the purpose of the research is over 10 companies which use SAP.

3.9

Tools of Data Analysis

3.9.1. Significance Level

- The significance level chosen in hypothesis testing is 0.01

3.9.2. Method of Hypothesis testing used

- Hypothesis test of proportion


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CHAPTER 4: Data Interpretation & Analysis

4.1Primary Data

4.1.1 What is the footprint of your organization?

Majority of the companies interviewed that had a global footprint had SAP HANA implemented

due to the many benefits it offered which make it detrimental for a global presence to maintain a

competitive IT edge.

0

2

4

6

8

10

12

14

16

18

Local Pan India Global

SAP implemenaion

Geog!ap"ical #oop!in o#

o!gani$aion


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4.1.2 How many years it has been since the first implementation of the SAP ERP in

the organization?

As it is clearly indicated, most of the respondents that have SAP implementation over a period of

time also see a case in adopting the SAP HANA module for the advantages it offers. As is

indicated all the organization that have SAP implemented since 10 or more years have SAP

HANA implemented. As can be seen there is a clear correlation between the number of SAP

implementation years and adaptation of SAPA HANA module.

0

2

4

6

8

10

12

14

16

%5 &ea!' 5(10 &ea!' ) 10 &ea!'

Chart Title

Pe!iod 'ince #i!' SAP implemenaion SAP *A+A implemened


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4.1.3 What best describes your company's current adoption level of HANA?

The above analysis depicts that most of the companies interviewed, who already had SAP ERP

system in place had already implemented SAP HANA module and some already have it under

consideration. Only a minority few did not have SAP HANA implemented or even under

consideration for various reasons ranging from cost limitations to not being aware of the benefits

of it.

8,

62,

15,

15,

Current adoption level

In-e'igaing "e ec"nolog& *a-e a p!odcion in'ance

/aiing #o! eeci-e app!o-al' +o in-e'igaing an& a'pec o# ec"nolog&


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4.1.4 Which of the following use cases best describe your HANA implementation(s)?

It is clearly illustrated that the major use case for companies that already have SAP HANA

implemented is for its accelerator use for faster generation of reports and quicker developments

when needed. Followed by the ease of SAP HANA being able to be implemented and aligned with

the current SAP system that is in place within the organization. Lastly, it is followed by the

operational or agile data mart quality improving the use of large data tables adding efficiency to

the whole process.

21,

42,

37,

Use case

pe!aional o! Agile daa ma!

*A+A ba'ed accele!ao!

Sideca! o! ol(on o ei'ing SAP en-i!onmen


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4.1.5 Is your HANA instance primarily on-premise or in the cloud?

As can be seen most of the database is based on the cloud. This is primarily due to the reason that

most of the users are not located in a centralized location and would need the access to the data at

any time. The justification for having the data on own premise is that up to an extent it ensures

the safety and accessibility of confidential data.

25,

75,

Location of HANA instance

P!emi'e lod


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4.1.6 What is the annual net profit of the organization?

The above analysis is inconclusive in arriving at a correlation between adaptation of SAP HANA

and the profitability of the organization. This is due to the fact that SAP HANA is not limited to a

sector or the profitability of the company. It primarily depends on the use case an organization

sees in adopting the technology.

0

1

2

3

4

5

6

7

%100 illion 100 ( 500 million ) 500 million

Net Profit in $

+e P!o#i in


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4.1.7 What’s holding back customers from buying HANA?

As clearly demonstrated the respondents including those who have HANA implementation feel

that the cost involved in implementing a new SAP module is the cause that it has a low adaption

rate even in companies who already have SAP as their main ERP system.

31,

61,

8,

Reason for not adopting SAP HANA

+o able o pin don b'ine'' ca'e o' in-ol-ed "e!'


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4.1.8 What are the business benefits of HANA?

The major benefit as can be seen is the ability to accelerate development and reduce software costs

within an organization.

24,

32,

32,

12,

Benefits of HANA

"a!da!e co' edce 'o#a!e co'

Accele!ae de-elopmen Inc!ea'e p!odci-i&


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4.1.9 What is the performance gain after SAP HANA? (Please select one or

more of the following)

The major performance gain that has been demonstrated by SAP HANA implementation is the

data size it can handle at the same time reducing the hardware cost due to data compression and

type and structure of data as it has the capability of arranging the data even the most complex ones

in a structured way.

35,

22,

35,

8,

Performance gain after SAP HANA

aa 'i$e and comp!e''ion :e!& complei& ;&pe o! '!c!e o# daa "e!


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4.1.10 Please describe the situation before SAP HANA

It can be seen that the major reason for adoption of the HANA module is the slow reporting

of large amount of data in a non HANA SAP background and with the the adoption of this

particular the same has been addressed to. Also the overall time spent on analytics the data

was much more then desirable and this too has been addressed to with the adoption of

HANA.

69,

23,

8,

Scenario efore HANA implementation

Slo !epo!ing o!e ime 'pen on anal&ic' "e!'


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4.2 Secondary Data

4.2.1 SAP HANA Implementation at SAFRAN

SAGEM, a member of the French aerospace and defense conglomerate SAFRAN, is a global

leader in optronics, avionics, and electronics for aircraft. It makes the stuff that enables fighter jets

to go fast and outmaneuver adversaries. Safran is a French multinational aircraft and rocket

engine, aerospace component, and security company. It was formed by a merger between the

aircraft and rocket engine manufacturer and aerospace component manufacturer

group SNECMA and the security company SAGEM in 2005. It is headquartered in Paris.

Problem:

In a company like this, there is, naturally, little patience for slow reporting and analysis. It expects

its IT systems to be as quick and agile as the fighter jets it helps equip.,having data is only half the

battle; to be of value, Sagem needs to be able to quickly access and analyze mission critical

information. As any fighter pilot will say, speed and agility are always relative. In a competitive

contest, it is not just how good you are, but how good you are relative to your competitor.


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Solution:

Sagem already relied on SAP’s industry-leading NetWeaver Business Warehouse application for

reporting and analytics. Migrating its SAP Netweaver Business Warehouse to the SAP HANA

platform, though, opened up the possibility of dramatically improving performance.

This was exactly what Sagem’s IT team was looking for. SAP Active Global Support delivered

SAP MaxAttention helped Sagem migrate its existing NetWeaver Business Warehouse to the SAP

HANA platform without disruption, enabling Sagem to dramatically improve reporting speed and

analytical agility.

Benefits:

The results were dramatic. From a technical perspective, with its SAP Netweaver Business

Warehouse running on SAP HANA, Sagem saw blazingly fast query execution, data activation and

cache execution paired with significant database compression. Translated into business speak, the

company was able to significantly improve the speed with which its analysts could access and

analyze mission critical data—transforming information into insight much more quickly and

agilely than they were able to previously. And they were able to do this while avoiding disruptions.


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4.2.2 SAP HANA Implementation at eBAY

About ebay:

With more than 100 million active users globally, eBay is the world's largest online marketplace,

where practically anyone can buy and sell practically anything.

Problem:

The collective impact on ecommerce is staggering: In 2012, the total value of goods sold on eBay

was $175 billion. Over next three years, eBay predicts that number to grow to $300 billion -- more

than the gross domestic product of all but 33 of the world's countries. In addition, eBay owns

PayPal. With more than 128 million active users in 193 markets and 25 currencies around the

world, PayPal enables global commerce. PayPal’s net Total Payment Volume for 2012, the total

value of transactions, was $145 billion. eBay CFO describes eBay as a dynamic self-regulating

economy similar to how the free-market based economy of a country , like United States, operates.

Similar to how Ben Bernanke, Federal Reserve Chairman can manage the US economy by

manipulating monetary policies (changing interest rates, etc.), eBay CFO believes they can manage

the eBay economy as well by modifying variables at micro/macro level (like listing rates, policies,

etc).

To achieve this goal, eBay’s vision is to build a system that has four components:


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a. WHAT – Track several thousand business metrics, forecast for 1 day in future (tomorrow)

and if there is deviation between forecast and actual, alert (send a signal)

b. WHY – Determine causality, once a signal (deviation from forecast) is detect. They

expect the system to automate the process of root-cause analysis to a large extent.

c. WHAT IS LIKELY TO HAPPEN – Generate near-term, mid-term and long-terms

forecast, all the way from 1 day to 18 months in future with differing confidence levels

d. WHAT IF – Ability to run simulations to visualize the impact of decisions on eBay

economy

eBay needs more than 300 analysts, just to track the metrics (“WHAT” part of the project). Even

then the key issue was still how to distinguish the signal from the noise. In addition, eBay team was

concerned about the impact of added workload on the central EDW and slowing down rest of

critical eBay operational reporting. Moreover, they were missing the agility to change the data

structures and build predictive models for exploratory work. For the “WHY” part, eBay ended up

spending more 2-3 weeks of an analyst’s time to do root-cause analysis. A large part of their

process tends to be manual relying on a degree of luck and lots of experience. Consequently,

signals are missed leading to delayed corrective action. The third and fourth part of the system

didn’t exist at all.

Solution:

SAP successfully delivered the first phase (WHAT) of the project as a POC using SAP HANA

Platform and Performance and Insight Optimization (PIO) services. The solution involved building

a comprehensive Signal Detection system that can automatically handle any of the thousands of

time series metrics that eBay tracks on a daily basis by combining the following features:


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1. Data cleansing operations to remove duplicates, and fill in missing data for planned and unplanned

events.

2. Determination of time series characteristics (such as multiple seasonal cycles, and the Hurst

exponent)

3. A library of different time series models that consider the trend, seasonality, lagged effects, and

also correlation against holidays and promotions.

4. And, as no one model works well for all the metrics, a decision tree based framework that

determines the best time series model to use.

5. Finally, a dashboard for business users that helps them in finding the signals within a day; and

understand the strength of the signals based on the probability of the metric value being an outlier

on any single day.

Benefits:

By using the signal detection system, eBay analysts can focus on the most important outliers within

a day rather than spending 2-3 weeks before learning about potential problems.


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4.2.3 SAP HANA Implementation at Charite hospital (Germany)

About Charite:

The Charité is the largest University hospital in Europe with more than 100 clinical departments

and 3700 hospital beds. It also houses a large number of translational and basic science research

groups. One of the 4 major research areas is oncology. In 2008, the Charité has created the Charité

Comprehensive Cancer Center (CCCC), which is governing its oncology clinical and research

programs.

Problem:

To research, teach, and heal. Charité provides 150,000 inpatient and 600,000 outpatient treatments

per year. The hospital’s 3,800 doctors and scientists are committed to the highest levels of

healthcare and research – and the organization is equally committed to providing the accurate,

timely reports and analysis required for success.

Charité already has a mature analytics program. This enables them to think creatively about how

they use patient data, medical records, and study results within their business. Researchers wanted


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to look at millions of data points and ask questions in a flexible reporting environment – and they

wanted to make their in-house analytics systems as fast and easy to use as a Google search.

Solution:

To make the above possible, the hospital invested in SAP in-memory technology designed to

harness the big data associated with medical records. Already, more than 600 users are taking

advantage of the technology.

When the SAP HANA platform was first introduced at Charité, the hospital held workshops to

brainstorm use cases, focusing on several key questions: What is possible, and where might the

university make improvements? Where could the technology have the biggest impact on patient

care and healthcare research? Can we use it to look for trends in patient cases?

For its first project with SAP HANA, the team decided to focus on the hospital’s cancer database

and its use in selecting patients for clinical trials. A typical clinical trial involves a research

partnership with a commercial sponsor such as a

medical device or pharmaceutical company; the hospital has only a small window of opportunity in

which to get the study, participants, and funding in place. The faster Charité can identify suitable

study participants, the greater its chances of landing the study and conducting the research.

Benefits:

Charité now uses the SAP HANA Oncolyzer to analyze data merged from its cancer and medical

admin databases to find the best candidates for each new trial. The Oncolyzer searches and

examines information such as tumor types, gender, age, risk factors, treatments, and diagnoses – to


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find the best candidates based on the study criteria. In the future, when DNA is added to the data

set, the Oncolyzer will analyze up to 500,000 data points per patient. Both structured and

unstructured data is analyzed, accelerating the identification process greatly – and giving Charité a

competitive advantage over other prospective research partners.


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4.2.4 SAP HANA Implementation at BMW

Bayerische Motoren Werke, commonly known as BMW or BMW AG, is a German automobile,

motorcycle and engine manufacturing company founded in 1916. BMW

is headquartered in Munich, Bavaria. It also owns and produces Mini cars, and is the parent

company of Rolls-Royce Motor Cars. BMW produces motorcycles under BMW Motorrad. In

2012, the BMW Group produced 1,845,186automobiles and 117,109 motorcycles across all of its

brands. BMW is part of the "German Big 3" luxury automakers, along with Audi and Mercedes-

Benz, which are the three best-selling luxury automakers in the world.

Problem:

• Sustain broad adoption of SAP BusinessObjects BI solutions.

• Accelerate reports on 3rd-party database as data volume increases.


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• Lower TCO as data access optimization and tuning increases costs

The resolution:

• Load detailed data into SAP HANA from 3rd-party database.

• Push calculation on detailed data down to SAP HANA, avoiding pre-aggregation.

• Move existing SAP BusinessObjects reports to the new environment

The key benefits:

• A minimum of 20x performance improvement.

• No change to the SAP BusinessObjects reports.

• Enabled drill down to details that was not possible in the previous environment


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4.2.5 SAP HANA Implementation at Asian Paints

About Asian Paints:

Asian Paints Limited is an Indian chemicals company headquartered in Mumbai, India. It

manufactures paints for decorative and industrial use. Asian Paints is India's largest paint company

and Asia's third largest paint company, with a turnover of Rs 96.32 billion. Besides Asian Paints,

the group operates around the world through its subsidiaries Berger International Limited, Apco

Coatings Limited, SCIB Paints and Taubmans.

Problem & Solution:

It is an early adopter of cutting edge IT solutions, and is widely regarded in the SAP customer community as very

prolific user of SAP BI. In late 2011, Asian Paints decided to implement HANA for their growing analytical needs

for the large volume ERP and BW implementations, and with the help of SAP Consulting implemented SAP

HANA running under SAP BW in only 3 weeks.

Benefits:

• Data volume compression savings of 6:1 moving from BW/Oracle to BW/HANA.

• Significant improvement in the query performance: average query performance improvement was15 x

with maximum improvement 266x times.


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• Certain queries, for analyzing orders and billing that could not run in the past on BW-Oracle, are

returning the results with an impressive query response time of 15-20 secs with BW-HANA.

• Data load time reduced by an average of 95% with some of the delta data load completing in 2 minutes in

BW-HANA, paving the way for near real-time data extraction and analysis, compared to more than 35-

40 minutes it used to take in BW-Oracle.


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4.2.6 SAP HANA Implementation at Usha International Limited

About Usha:

A household name in India, Usha International Limited (UIL) is a multi-product consumer durable

manufacturing, marketing and distribution company, which has more than 33 warehouses, 70

company-owned retail stores, and a distribution network of more than 14,000 dealers.

Problem:

Being in a competitive market, the key business need of the company was to be amongst the first

few who quickly respond to the changing market needs and immediately tap the profits. In order to

achieve this, firstly UIL wanted to gain granular visibility across its widespread supply chain, sales

and inventory data. And secondly the company wanted to be able take quick and informed

decisions by churning out real-time business-critical insights from the enterprise’s business data.

For example, UIL wanted that the real-time data on cash-flow, daily sales and inventory data

should all be available for operations review at the click of a button.

Solution:

After a lot of evaluation and brainstorming, UIL decided to implement SAP High Performance

Analytics Appliance (HANA) Database which uses in-memory technology that facilitates in

obtaining quick results from even complex data queries. Traditionally majority of data storage,


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calculation and aggregation, happens on-disk which substantially impacts the end-user experience.

But with SAP HANA, a complete in-memory database is built that combines transactional data

processing, analytical data processing and application logic processing functionality, in-memory.

In February 2012, UIL started the implementation of SAP HANA Database over which the SAP

NetWeaver Business Warehouse (the Business Intelligence and Analytics piece) was to be run. For

this implementation, UIL worked closely with a dedicated SAP Ramp-Up coach and SAP

Enterprise Support services and successfuly completed the entire project within just four weeks. So

by March 2012, the project was live and was rolled out to its 16 branch offices, 30 depots, and 4

manufacturing plants.

Talking about the benefits SAP HANA has brought to the company, Subodh Dubey, Group CIO,

UIL says, “To take full advantage of our business data, we needed high-performance business

analytics. That means faster data loads and faster query response times. Implementing SAP HANA

has improved performance tremendously with faster data loads and better storage compression and

has given a whole new life to our business intelligence solution.”

One of the major benefits that UIL derived out of the transparency that SAP HANA

implementation brought was reduction of inventory levels by 50 percent and elimination of out-of-

data inventory. This in turn led to lowering of inventory carrying costs and better cash flow. Also

with SAP HANA, UIL has for the first time achieved sales visibility down to the individual Stock

Keeping Unit (SKU) and territory. “SAP HANA on Business Warehouse is providing UIL the

visibility into live information with minimal time-lag to make the right decisions at the right time

and respond faster to their consumer demands," adds Dubey. UIL recently won the SAP ACE

Award for Customer Excellence 2012 for being the first ever customer to do the real-time analysis


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of Big Data using the new SAP HANA for Business Warehouse.

Benefits:

• 50 percent reduction in inventory levels

• 50 times faster query performance

• About 75 percent improvement in reporting and analytics performance

• Achievement of 8 times faster data loads


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4.2.7 SAP HANA implementation by TCS for client Lexmark

Above TCS:

Tata Consultancy Services Limited (TCS) is an Indian multinational information technology (IT)

service, consulting and business solutions company headquartered in Mumbai, Maharashtra. TCS

operates in 46 countries. It is a subsidiary of the Tata Group and is listed on the Bombay Stock

Exchange and the National Stock Exchange of India. TCS is the largest Indian company

by market capitalization and is the largest India-based IT services company by 2013 revenues.

About Customer

Lexmark International Inc. provides businesses with a broad range of printing and imaging

products, solutions and services that help customers print less and save more. The company is a

leading provider of process and content management software that helps organizations fuel greater

operational efficiency.

Problem:

Lexmark’s data warehousing applications had been disconnected from its operational applications,

resulting in a lag time of one day between gathering data and analyzing it. TCS delivered real-time


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visibility into Lexmark’s financial data by implementing SAP HANA. Lexmark produces huge

volumes of financial and transactional data—inventory, sales orders, general ledger balances and

invoices—daily. However, it lacked the ability to capture and analyze data in real time.

sap hana in-memory analytics - harness the power of real-time

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