Sap hana Overview

SAP HANA Session 1Introduction

Problem StatementIn 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 have more data than they know how to use effectively.

With data doubling every 18 months, that percentage is going to keep growing.According to EMC, by the end of 2011 there was 1.8 Zeta byte of digital data.

IDC estimates that worldwide digital content added up to 1 trillion gigabytes in 2011. They predict this will double in 18 months, and every 18 months thereafter.

Few Facts

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Now exactly what is a Zeta Byte ?



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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.



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Agenda1. 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



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Increasing Data Volumes

Calculation Speed

Type and # of Data Sources

Lack of business transparency

Sales & Operations Planning based on subsets of highly aggregated information, being several days or weeks outdated.

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.

Vision: In-Memory ComputingTechnology Constrained Business Outcome

Sub-optimal execution speed

Lack of responsiveness due to data latency and deployment bottlenecks

Inability to update demand plan with greater than monthly frequency

Current Scenario

Information Latency



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



TeraBytes of Data In-Memory

100 GB/s data througput Real Time

Freedom from the data source

Improve Business Performance

IT rapidly delivering flexible solutions enabling business

Speed up billing and reconciliation cycles for complex goods manufacturers

Planning and simulation on the fly based on actual non-aggregated data

Competitive AdvantageE.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.

Vision: In-Memory ComputingLeapfrogging Current Technology Constraints

Flexible Real Time Analytics

Real-time customer profitability

Effective marketing campaign spend based on large-volume data analysis

Future State



In-Memory Computing – The Time is NOWOrchestrating Technology Innovations

HW Technology Innovations

64bit address space – 2TB in current servers

100GB/s data throughput

Dramatic decline in price/performance

Multi-Core Architecture (8 x 8core CPU per blade)

Massive parallel scaling with many blades

Row and Column Store

Compression

Partitioning

No Aggregate Tables

Real-Time Data Capture

Insert Only on Delta

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

SAP SW Technology Innovations



Using main memory as the data storeThe most obvious reason to use main memory as the data store for a database is

speed of accessThe 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 movementEven 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 storageRelational 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.


https://www.experiencesaphana.com/community/blogs/blog/2012/04/09/sap-hana--scale-out-performance-test-results--early-findings


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 MartIn 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 AcceleratorThe 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.



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

Primary Database for SAP NetWeaver Business Warehouse

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.

SAP HANA Use Cases













History behind SAP HANA







SAP Appliance Business Partners





SAP HANA Architecture




SAP HANA Configurations

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Product Availability Matrix




© SAP 2007/Page 39

SAP BusinessObjects Data Services Platform

Integrate heterogeneous data into BWA

Extract From Any Data Source into HANA

Syndicate From HANA to Any Consumer

Integrated Data Quality

Text Analytics

Rich Transforms


© SAP 2007/Page 40

SAP BusinessObjects Data Services Platform

Integrate heterogeneous data into BWA

Extract From Any Data Source into HANA

Syndicate From HANA to Any Consumer

Integrated Data Quality

Text Analytics

Rich Transforms


Real Time Enterprise: Value PropositionAddressing Key Business Drivers

1. Real-Time Decision Making• Fast and easy creation of ad-hoc views on business• Access to real time analysis

2. Accelerate Business Performance • Increase speed of transactional information flow in areas

such as planning, forecasting, pricing, offers…

3. Unlock New Insights • Remove constraints for analyzing large data volumes -

trends, data mining, predictive analytics etc.• Structured and unstructured data

4. Improve Business Productivity• Business designed and owned analytical models• Business self-service reduce reliance on IT• Use data from anywhere

5. Improve IT efficiency• Manage growing data volume and complexity efficiently• Lower landscape costs

There is a significant interest from business to get agile analytic solutions.

„In a down economy, companies focus on cash protection. The decision on what needs to be done to make procurement more efficient is being made in the procurement department“.

CEO of a multinational transportation company

There is a significant interest from business to get agile analytic solutions.

„In a down economy, companies focus on cash protection. The decision on what needs to be done to make procurement more efficient is being made in the procurement department“.

CEO of a multinational transportation company

Flexibility to analyse business missed by LoB.

„First performance, and the other is flexibility on a business analyst level, who need to do deep diving to better understand and conclude. The second would be that also front-end tools are not providing flexibility“.

Executive of a global retail company

Flexibility to analyse business missed by LoB.

„First performance, and the other is flexibility on a business analyst level, who need to do deep diving to better understand and conclude. The second would be that also front-end tools are not providing flexibility“.

Executive of a global retail company

Traditional data warehouse processes are too complex and consume too much time for business departments.

„ The companies […] were frustrated with usual problems […] difficulty to build new information views. These companies were willing to move data […] into another proprietary file format […]. “

Analyst

Traditional data warehouse processes are too complex and consume too much time for business departments.

„ The companies […] were frustrated with usual problems […] difficulty to build new information views. These companies were willing to move data […] into another proprietary file format […]. “

Analyst


Real Time Enterprise: Value PropositionThe Value Blocks

Run performance-critical applications in-memory

Combine analytical and transactional applications

No need for planning levels or aggregation levels

Multi-dimensional simulation models updated in one step

Internal and external data securely combined

Batch data loads eliminated

Eliminate BW database

Empower business self-service analytics – reduce shadow IT

Consolidate data warehouses and data marts

In-memory business applications (eliminate database for transactional systems)

Lower infrastructure costs server, storage, database

Lower labor costs backup/restore, reporting, performance tuning

Value Elements In-Memory Enablers

Sense and respond faster Apply analytics to internal and external data in real-time to trigger actions (e.g., market analytics)

Business-driven “What-If” Ask ad-hoc questions against the data set without IT

Right information at the right time

New business models based on real-time information and execution

Improved business agility Dramatically improve planning, forecasting, price optimization and other processes

New business opportunities faster, more accurate business decisions based on complex, large data volumes

High performance “real-time” analytics

Support for trending, simulation (“what-if”)

Business-driven data models

Support for structured and un-structured data

Analysis based on non-aggregated data sets



HANA Information Modeler


HANA Information ModelerCreating Connectivity to a new system


HANA Information ModelerCreating Attribute View


HANA Information ModelerDefining Attributes (Key Attribute, Attribute, Filter and Measure (for numeric data types)


HANA Information ModelerData Preview


HANA Information ModelerCreating 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

Courtesy

Documents

Sap hana Overview