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2. • What is HANA ?
• Why the Need for HANA?
- IT and Business
• Why Use HANA ?
• What is the Reasons Companies Use HANA?

3.  Key Terminology
 HANA Architecture
 The Heart of SAP HANA
 In Memory Computing Engine
 What is in Memory
 HANA Column storage VS Row Based Storage
 Multiple Engines
 What is Ad-Hoc Analysis

4. • Where I can Use HANA
• Use Cases for HANA
• Key takeaways about HANA

6.  SAP HANA is the Latest in-memory analytics
Product. Using HANA, Companies can do ad hoc
analysis of large volume of big data in real time.
 SAP HANA is completely re- imagined platform
for real tome business.
 SAP HANA Transforms Businesses by streamlining
transactions, analytics, planning and predictive
data processing on a single in-memory data
base so business can operate in real time.

7. IT Challenges:
 “Big Data” Volume growing and challenge for real-
time access to operational Enterprise systems.
 Costly for IT to purchase and maintain hardware to
handle increasing data volume.
 Dissatisfied Business Users.
 Processing and analysis results delayed.
 Data Not in Real time.

8. Business Challenges:
 Inadequate access to real-time operational
Information
 Need to react faster to events impacting
business
 Need to quickly uncover trends and patterns by
functional users, Empower users/organizations.
 Need to improve business processes.

9.  SAP HANA Enables business to make smarter,
Faster decisions through real-time analysis and
Reporting, combined with dramatically
accelerated business processes.
 Lack of delay between insight and action turns
Business into a “real time business”.

10.  Speed : Manage Massive volumes at high speed.
 Agility: Enable real time interactions across
value Chain
 Any Data: Gain Insights from Structured and
Unstructured Data.
 Insight: Unlock new insights with predictive,
complex analysis.
 Applications: Run Next generation Applications.

11.  Cloud: Step up into next Advanced Platforms
 Innovation: Deploy ultimate platform for
business innovation
 Simplicity : Manage fewer layers and Landscapes
for lowest costs.
 Value : Innovate without disruption and add
value to legacy investments.
 Choice : Work with preferred partner at every
level.

12. Key Terminology:
 Aggregation
 BICS (Business Intelligence Consumer services)
 Business Objects
 Business Warehouse (BW)
 Compression
 Data Stripping
 In memory computing Engine (IMCE)
 Multi-Dimensions Expressions (MDX)
 Online Analytics Processing (OLAP)
 Partitioning
 Structured Query Language (SQL)

14. • A powerful and flexible data Calculation Engine.
• Support access via SQL and MDX for clients like SAP
Business objects and Excel.
• Access Data from Virtually and Data source, including
SAP BW
• Unified Information modelling Design environment.
• A data repository to persists views of business
information.
• Data Services Functionality.

16.  In-memory means all the data is stored in
the memory (RAM).
 There is no time wasted in loading the
Data from hard disk to RAM, or while
processing, keeping some Data in RAM
and some data on Disk Temporarily.
 Everything is In-memory all the time,
Which give the CPUs quick access to Data
for processing.

18.  HANA has multiple Engines inside its
Computing engine for better performance.
 HANA Supports Both SQL, OLAP Reporting
tools; there are separate engines to perform
operations respectively.

19.  In Traditional Data warehouses, such as SAP BW, a lot
of Pre-aggregations is done for quick results.
 The IT administrator decides which information might
be needed for analysis and prepares the results for
the end Users.
 Results in fast performance but the end user has no
Flexibility.
 With SAP HANA and its speedy Engine, no pre-
aggregation is required. The User can be perform
any kind operation in reports and does not have to
wait Hours to get the data ready for analysis in real
time.

20. Any Where There are large Volumes of Data.
 Aerospace & Defence
 Automotive
 Banking
 Chemical
 Consumer products
 Cross Industry
 Customer Services Top Functional Areas of
 Finance Data
Usage
 Healthcare
 High Tech
 Insurance
 Industry Machinery & Components
 Life Sciences

21.  Manufacturing
 Marketing
 Media
 Mill products
 Mining
 Oil & Gas
 Professional Services.
 Public sector Top Functional
Areas of Data Usage
 Retail
 Sales
 Supply chain
 Tele communications
 Transportations
 Utilities
 Wholesale Distribution

22.  Sales Reporting (CRM)
 Financial reporting (FICO)
 Shipping Reporting (LE- SHP)
 Purchasing Reporting (P2P/SRM)
 Master Data Reporting (DG/MDM)

23. Empower Your Organizations
 Reduce reliance on IT resources
 Real-time Visibility to complete Data for transactions & analytics.
 Enable 360 degree view of your business.
Real time analytics for operational Data
 Go from what happened yesterday to real time.
 Close to zero latency.
 Ability to leverage and analyze large volumes of data.
Low Total Cost of Ownership (TCO)
 Non disruptive to existing enterprise data warehousing (EDW)
Strategy
 Low TCO by leveraging the latest Technology and delivery as pre-
configured appliance.

24.  Financial Accounting
 Financial Supply chain Management (FSCM)
 Controlling (CO)
 Materials Management (MM)
 Sales and Distribution (SD)
 Logistics Execution (LE)
 Production planning (PP)
 Quality Management (QM)
 Plant Maintenance (PM)
 Project Systems (PS)
 Human resources (HR)