Sap hana Overview

SAP HANA Session 1
Introduction

Problem Statement
In an organization every year massive
amounts of data is created and how fast
your business reacts to important
information determines whether you
succeed or fail. This is a big problem and
its getting bigger.
In a Sloan Management survey in 2010
60% of executives said their companies Few
have more data than they know how to
use effectively.
Facts
IDC estimates that worldwide digital
With data doubling every 18 months, content added up to 1 trillion gigabytes in
that percentage is going to keep growing. 2011. They predict this will double in 18
According to EMC, by the end of 2011 months, and every 18 months thereafter.
there was 1.8 Zeta byte of digital data.

www.xpress-analytics.com Ph: 8775734486

Now exactly what is a Zeta Byte ?


Real Time Consumption of Data

People want instant access to
information – ‘in the
moment’’ - whether that is a
moment of risk or a moment
of opportunity. If the moment
has passed and your business
has not taken the right action,
it has failed. People want
instant answers. They want
them to be right. They want
them anywhere, any time.


Agenda
1. Introduction to HANA: Vision and Strategy

2. Solution Overview & Roadmap

3. Business Value

4. HANA Modeling Studio

5. Connecting from BOE

6. Real time Examples


Solution – A Technology to process and analyze massive amounts
of data in real time

•In Memory Storage
•Multi Core Architecture
•Columnar Storage
•Partitioning
•Compression
•Massive parallel processing


Vision: In-Memory Computing
Technology Constrained Business Outcome
Current Scenario

Sub-optimal execution speed
Lack of responsiveness due to data latency
and deployment bottlenecks
Inability to update demand plan with
greater than monthly frequency

Increasing Data
Volumes Lack of business transparency
Sales & Operations Planning based on
Information subsets of highly aggregated information,
Calculation Speed
Latency being several days or weeks outdated.

Type and # of
Data Sources
Reactive business model
Missed opportunities and competitive
disadvantage due to lack of speed and
agility
Utilities: daily- or hour-based billing
and consumption analysis/simulation.


In-Memory Computing

Technology that allows the processing of
massive quantities of real time data
in the main memory of the server
to provide immediate results from
analyses and transactions


Vision: In-Memory Computing
Leapfrogging Current Technology Constraints

Future State
Flexible Real Time Analytics
Real-time customer profitability
Effective marketing campaign spend based
on large-volume data analysis

TeraBytes of Data Improve Business Performance
In-Memory IT rapidly delivering flexible solutions
enabling business
100 GB/s data Speed up billing and reconciliation cycles
Real Time
througput for complex goods manufacturers
Planning and simulation on the fly based on
Freedom from
the data source actual non-aggregated data

Competitive Advantage
E.g. Utilities Industry:
Sales growth and market advantage from
demand/cost driven pricing that optimizes
multiple variables – consumption data,
hourly energy price, weather forecast, etc.


In-Memory Computing – The Time is NOW
Orchestrating Technology Innovations

The elements of In-Memory computing are not new. However, dramatically improved hardware economics and technology
innovations in software has now made it possible for SAP to deliver on its vision of the Real-Time Enterprise with In-Memory business
applications

HW Technology Innovations SAP SW Technology Innovations

Multi-Core Architecture (8 x 8core CPU Row and Column Store
per blade)
Massive parallel scaling with many
blades Compression
64bit address space – 2TB in current
servers Partitioning
100GB/s data throughput
Dramatic decline in
price/performance No Aggregate Tables

Real-Time Data Capture
Insert Only on Delta


Using main memory as the data store
The most obvious reason to use main memory as the data store for a database is
speed of access
The main memory (RAM) is the fastest storage
type. Data in main memory can be accessed
more than a 100,000 times faster than data
on a spinning hard disk.

flash technology storage is 1000 slower than
main memory.

Main memory is connected directly to the
processors through a very high-speed bus,
whereas hard disks are connected through a
chain of buses (QPI, PCIe, SAN) and controllers
(I/O hub, RAID controller or SAN adapter,
and storage controller).


Minimizing data movement
Even though today’s memory capacities allow keeping enormous amounts of data
in-memory, compressing the data in-memory is still desirable. The goal is to compress
data in a way that does not use up performance gained, while still minimizing data
movement from RAM to the processor.


Columnar storage
Relational databases organize data in tables, which contain the data
records. The difference

between row-based and
columnar
Row-based storage stores
a table in a sequence of
rows.
Column-based storage
stores a table in a
sequence of columns.


Row or Column ?


Pushing application logic to the database

An application executing the application
logic on the data has to get the data from
the database, process it, and possibly
send it back to the database to store the
results. Leads to network over heads and
latency

How will it be to process the data
where it is, at the database ???


Data partitioning & Parallelization

on a 10-core processor the time needed
is one-tenth of the time
that a single core would need

servers available today can hold terabytes of
data in memory and provide up to
eight processors per server with up to 10 cores
per processor

To accommodate the memory and
computing power requirements that go
beyond the limits of a single server, data can
be divided into subsets and placed across a
cluster of servers, forming a distributed
database (scale-out approach).


In a recent independent benchmark HANA raced through a 100TB
test database with 100 billion records. First, HANA achieved a 20x
data compression level, which was remarkable. More impressive,
though, was that with no caching, indexing, or materializing of the
query results, the query responses were a mere 300 to 500
milliseconds. Compare this to some Oracle documentation that
has claimed it was "lightning fast" at processing 100 million records
in one second. HANA, then, can run 1,000 times more data in less
than one-half the time than Oracle.


Beyond benchmarks, in the real world of Wall Street, one
HANA application is using Sybase CEP (Complex Event
Processing) to feed more than 2.1 million updates per second
into the database. In a retail environment in Japan, one
customer achieved 400,000 times performance improvement
over its previous database environment. Adobe uses HANA to
analyze customer data in real time and T-Mobile runs three
HANA databases to analyze and reduce customer churn. It's
stories like these that make HANA the fastest growing product
in SAP history.


SAP HANA Use Cases
Agile Data Mart
In this scenario, SAP HANA acts as the central hub to collect data from a
few SAP and non-SAP source systems and then display some fairly simple
and focused analytics in a single-purpose dashboard for users

SAP Business Suite Accelerator
The second major scenario where SAP HANA is being used is to accelerate
transactions and reports inside the SAP Business Suite. Again, SAP HANA is
being set up as a stand-alone system in the landscape, side-by-side with the
database under the SAP Business Suite applications. In this scenario,
however, SAP HANA is being used to “off load” some of the transactions or
reports that typically take a long time (hours or days) to run, but it is not
being used as the primary database under the application.


SAP HANA Use Cases
Primary Database for SAP NetWeaver Business Warehouse

In this scenario a company replaces the previously underlying database for their SAP
BW system with SAP HANA. The IT team can perform a standard DB migration over to
SAP HANA and then enable specific objects to be in-memory optimized as necessary
depending on the company’s requirements.

Custom Applications for SAP HANA

As stated earlier, SAP HANA is a full-blown, do-just-about-anything-you-want
application platform. It speaks pure SQL, and it includes all of the most common APIs,
so you can literally write any type of application you want on top of it.


History behind SAP HANA


SAP Appliance Business Partners


SAP HANA Architecture


SAP HANA Configurations

www.xpress-analytics.com Ph: 8775734486 v

Product Availability Matrix


SAP BusinessObjects Data Services Platform
Rich Transforms

Integrate heterogeneous
data into BWA

Integrated Data Quality
Text Analytics

Extract From Any Data Source into HANA
Syndicate From HANA to Any Consumer

© SAP 2007/Page 39

SAP BusinessObjects Data Services Platform
Rich Transforms

Integrate heterogeneous
data into BWA

Integrated Data Quality
Text Analytics

Extract From Any Data Source into HANA
Syndicate From HANA to Any Consumer

© SAP 2007/Page 40

Real Time Enterprise: Value Proposition
Addressing Key Business Drivers
1. Real-Time Decision Making There is a significant interest from business to get agile
There is a significant interest from business to get agile
analytic solutions.
analytic solutions.
• Fast and easy creation of ad-hoc views on business „In a down economy, companies focus on cash protection.
„In a down economy, companies focus on cash protection.
The decision on what needs to be done to make
The decision on what needs to be done to make
• Access to real time analysis procurement more efficient is being made in the
procurement more efficient is being made in the
procurement department“.
procurement department“.
1. Accelerate Business Performance CEO of a multinational transportation company
CEO of a multinational transportation company
• Increase speed of transactional information flow in areas
such as planning, forecasting, pricing, offers…
Flexibility to analyse business missed by LoB.
Flexibility to analyse business missed by LoB.
1. Unlock New Insights „First performance, and the other is flexibility on a
„First performance, and the other is flexibility on a
business analyst level, who need to do deep diving to
business analyst level, who need to do deep diving to
• Remove constraints for analyzing large data volumes - better understand and conclude. The second would be
better understand and conclude. The second would be
that also front-end tools are not providing flexibility“.
that also front-end tools are not providing flexibility“.
trends, data mining, predictive analytics etc.
Executive of a global retail company
Executive of a global retail company
• Structured and unstructured data

1. Improve Business Productivity
Traditional data warehouse processes are too complex
Traditional data warehouse processes are too complex
• Business designed and owned analytical models and consume too much time for business departments.
and consume too much time for business departments.
„„The companies […] were frustrated with usual
The companies […] were frustrated with usual
• Business self-service  reduce reliance on IT problems […] difficulty to build new information views.
problems […] difficulty to build new information views.
These companies were willing to move data […] into
These companies were willing to move data […] into
• Use data from anywhere another proprietary file format […]. ““
another proprietary file format […].
Analyst
Analyst
1. Improve IT efficiency
• Manage growing data volume and complexity efficiently
• Lower landscape costs


Real Time Enterprise: Value Proposition
The Value Blocks
Value Elements In-Memory Enablers
 New business models  based on real-time  Run performance-critical applications in-memory
information and execution  Combine analytical and transactional applications
 Improved business agility  Dramatically improve
 No need for planning levels or aggregation levels
planning, forecasting, price optimization and other
processes  Multi-dimensional simulation models updated in one step
 New business opportunities  faster, more accurate  Internal and external data securely combined
business decisions based on complex, large data  Batch data loads eliminated
volumes

 High performance “real-time” analytics
 Sense and respond faster  Apply analytics to
internal and external data in real-time to trigger  Support for trending, simulation (“what-if”)
actions (e.g., market analytics)
 Business-driven data models
 Business-driven “What-If”  Ask ad-hoc
 Support for structured and un-structured data
questions against the data set without IT
 Analysis based on non-aggregated data sets
 Right information at the right time

 Eliminate BW database
 Lower infrastructure costs  server, storage,
database  Empower business self-service analytics – reduce
 Lower labor costs  backup/restore, shadow IT
reporting, performance tuning  Consolidate data warehouses and data marts
 In-memory business applications (eliminate database for
transactional systems)


HANA Information Modeler


Creating Connectivity to a new system


Creating Attribute View


Defining Attributes (Key Attribute, Attribute, Filter and Measure (for numeric data types)


Data Preview


Creating Analytic View


Connectivity from BO Enterprise Tools

1. Crystal Reports Enterprise - (ODBC, JDBC,
Universe)

2. IDT (Information Design Tool) - JDBC

3. Explorer – Connection configuration in CMC

4. Advanced Analysis for Office (Q1 2011 release)

5. Web Intelligence – Universe

6. Xcelsius - Universe

Sap hana Overview

Recomendados

Recomendados

Más contenido relacionado

La actualidad más candente

La actualidad más candente (20)

Similar a Sap hana Overview

Similar a Sap hana Overview (20)

Sap hana Overview

Notas del editor