Research Report Hiren Final Printout 3

7/30/2019 Research Report Hiren Final Printout 3

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In Memory Analyticsusing SAP HANA


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Index1. Introduction to In Memory Analytics 32. Need of In Memory Analytics 53. Advantages of SAP HANA for In-Memory Analytics 74. Types of Analytics with SAP HANA 95. Design of SAP HANA 106. How SAP HANA Works 137. Limitations of SAP HANA 158. Conclusion 159. References. 16


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1. Introduction to In-Memory Analytics

Analysis of data is a process of inspecting, cleaning, transforming, and modelingdata with the goal of highlighting useful information, suggesting conclusions, and

supporting decision making.

Its fine to analyze few thousand rows of data but when it exceed more and

becomes millions and billions of rows of data it takes huge amount of time(in hours and

days) to analyze it Along this there is another problem of disk read speed. With time

RAM size, capacity of memory and processing speed increased but there is very less

improvement in the speed with which we read the data from the spinning hard disk.

Thus due to these two problems it takes a huge amount of time in analysis of

large amount of data which is very common in huge companies in current times.

Time is something which the current organizations cant afford as this

organizations 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 business

has not taken the right action, it has failed.

To overcome this problem In-Memory Analytics was evolved. In In-Memory

Analytics we just pull all the data from the traditional RDBMS which lies on disks and

load it into the main memory. As we know that RAM (Main Memory) is extremely fast as

compared to hard disks this can boost the speed of analytics significantly and can make

analytics in real time.

And with presence of multi core processor we can have up to 2 TB (2 Tera Byte =

2048 GB) of RAM on servers to accomplish requirement of In-Memory Analytics.
http://en.wikipedia.org/wiki/Datahttp://en.wikipedia.org/wiki/Informationhttp://en.wikipedia.org/wiki/Informationhttp://en.wikipedia.org/wiki/Data


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2. Need of In Memory Analytics

Vishal Sikka (Head of SAP HANA Development Team) has once said that if you

ask a question today and get the answer after 3 days ,one will even forget what the

question was originally.

Today the Organization faces challenges of not only processing of huge amount

of data which changes at exponential rate and comes from different data source but

also to analyze them in different manner and that too in seconds.

When I say real Time Data for a retail shop perspective the POS (Point of sale)

data would be available for analytics even before the customer leaves the retail store.

Currently there are two big problems the analytical projects are facing, these are twobig Vs that comes in performance of and analytical project. These two Vs are:

1) Volume and 2) Velocity.

1) Volume:-Before many years when you had 1GB pen drive in your hand you were walking

like a King. But those golden days are gone now.

In a Company every year huge amounts of data is created and how fast your

business reacts to important information determines whether you wins or fail. This is a

big problem and its getting bigger.

2) Velocity (Speed):-


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To be successful in business, organization has to take decision in movements. It

could be movement of risk or movement of opportunity. If the movement is gone and

organization doesnt react on that, the organization fails. Thus today every organization

want result in fraction of seconds (in moments).

Today organizations want quick answers. They want them to be accurate to rely

on and they want them instantly without waiting for long time. And organizations also

want them to be anywhere and 24x7.

Day are gone when companies use to rely on quarterly review and annual

budgets for their decision making. Now they want instant responses. Companies today

want to know the current market conditions and trends, to take decision of how to

change their policies and supply chains to have competitive advantage over other rivals.

RDBMS are failed to achieve bothRDBMS were designed for transactional processing purpose (insert and

update).Its hard to find database that can do both transaction processing (insert and

update) at the same time good at aggregations, joins (typical in Analytical solutions).

Also the structured query language (SQL) is designed to efficiently fetch rows of data

while BI queries usually involve fetching of partial rows of data involving heavy

calculations.

We can write complex queries in SQL but, it has been observed that these queries

that very long time to complete and also these brings down the performance of

concurrent transactional processes. To get fast results often multidimensional

databases or cubes also called MOLAP .Which stands for multidimensional online

analytical processing were formed.

To design a cube design is very complex and elaborate process, the IT staff has

to give significantly huge amount of time to these cube designing. Changing the cubes

structure to adapt to dynamically changing business needs was cumbersome. These

cubes are then populated with pre calculated data, to answer so particular queries.

Though this decrease the queries time significantly but cant answer the ad hoc queries

efficiently.
http://en.wikipedia.org/wiki/SQLhttp://en.wikipedia.org/wiki/SQL


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1. Advantages of SAP HANA for In-Memory Analytics


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SAP HANA the in-memory analytics technology from SAP AG which was launched

late last year, is winning rave reviews around the world, and promises to do for SAP

what R/3, its powerful ERP software, had done for it in the late 1990s.

Due to HANA, SAP AGs revenue was outpaced by that of competitor Oracle last

quarter for the first time in two and a half years

As define by SAPs official website SAP HANA is SAP HANA is an in-memory data

platform that is deployable as an on-premise appliance, or in the cloud. It is a

revolutionary platform thats best suited for performing real-time analytics, and

developing and deploying real-time applications. At the core of this real-time data

platform is the SAP HANA database which is fundamentally different than any other

database engine in the market today.


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Whenever organizations have to go deep within their databases to ask complex

and interactive queries, and have to go broad(which means working with enormous data

sets that are of different types from one another and from different data source) at the

same time, SAP HANA is well-suited. Increasingly there is a need for this data to be

recent and preferably in real-time. Add to that the need for high speed (very fastresponse time and true interactivity), and the need to do all this without any pre-

fabrication (no data preparation, no pre-aggregates, no-tuning) and you have a unique

combination of requirements that only SAP HANA can address effectively. When this set

of needs or any subset thereof have to be addressed (in any combination), SAP HANA is

in its elements.


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4. Types of Analytics with SAP HANAThere are 3 main types of In Memory Analytic that can be accomplishing by SAP Hana.

They are as follows:-

a. Operational Reportingb. Data Warehousingc. Predictive and Text analysis on Big Dataa. Operational Reporting:-

In this type of analytics companies can do the thing which they use to do

in day-today basis. That's what BASF for example is using it for. There are folks

who need to do profitability analysis quickly, to understand which products they

earn money on; these are complicated calculations, because the cost has to be

allocated to each product they make. For very large companies with complex

product structures, it's a report that could take up to three hours to generate.

With Hana, it takes a couple of seconds.

b. Data Warehousing:-With in-memory computing the need of complex data warehouse is been

minimize in the industries. Data warehouses aggregate data in various different

aggregates in order to have the answer ready when the question comes. With in-

memory computing, you don't need to do the aggregates.

You can just calculate on the fly. That means less cost for infrastructure to

run large-scale analytics systems. The third use case is, solve problems that could

not be solved before.

c. Predictive and Text analysis on Big Data:-Companies has to think beyond just delivering best and effective products

to people and uncover customer/employee /vendor/partner trends and insights,

anticipate behavior and take proactive action. SAP HANA provides the ability to


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perform predictive and text analysis on large volumes of data in real-time. It does

this through the power of its in-database predictive algorithms and its R

integration capability. With its text search/analysis capabilities SAP HANA also

provides a robust way to leverage unstructured data.

5. Design of SAP HANASOFTWARE DESIGN:

Instead of storing data row wise which majority of database do today,

HANA stories the in column manner for fast computing.

For example: If system wants to find aggregate of the second column


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i.e. 10+35+2+40+12.

In Row wise: The system has to jump memory locations to collect subsequent values for

aggregation. That is data records are available as complete tuples in one read which

makes accessing of few attributes expensive operation.

In Column wise: A single scan would fetch the results much faster.

Another important aspect of a column-based RDBMS is data compression. Since

all values in a column are stored together, there is the possibility of storing the value

only once, alongside the number of occurrences. So in the example table we've just

seen, the last column might be stored as follows:

EUR, USD, 2: EUR, USD

This might not seem important, but in a table that contains several million lines,

the space savings are potentially huge. SAP indicates that data can be compressed to

between 10 percent and 25 percent of its original size. Of course, this means less data to

scan through for the systemand since data is in memory, it means more data

between 4 and 10 timescan be kept in memory at once.

HARDWARE DESIGN:Large amount of data is divided into multiple sets which are then crunched

individually by the Blades as shown below.


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Data is divided into 4 blades with 2 standby blades

The Blades are composed of multiple CPUs per blade and each CPU has multiple

cores per CPU. This means that if you have for example 8 cores per CPU and 4 such

CPUs per blade. So just 4 blades will have 128 cores crunching data in parallel.

The SAP HANA box itself is a massively multi-core, multi-CPU server, with a great

deal of Memoryup to several terabytes. For example, on May 16, 2012, IBM

announced that in collaboration with SAP, they had built a machine with 100 TB of main

memory.

At the time, SAP indicated that this machine would be sufficient to run the eight

largest clients of SAP ERPall at the same time!

One of the main strong points of SAP HANA is its ability to process data in

parallel, cutting the initial (large) amount of data into small chunks, and then giving each

chunk to a separate CPU to work onhence the need for the large number of CPU

cores.

One other aspect of the system is that wherever possible, data is kept in memory,

in order to speed up access time. Where a traditional database system might set aside a

gigabyte or two of memory as a cache, SAP HANA takes this to the next level, using

nearly all the server's memory for the data, making access times nearly instantaneous.


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6. How SAP HANA Works


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For implementing SAP HANA, organization doesnt need to make drastic changes

to there IT investments (infrastructure). In the above diagram show that data in the

database can be replicated in real time into HANA and can be used for reporting with a

number of Business Intelligence tools directly sitting on the top of the HANA.

There are 3 major steps for using HANA:-


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1) Loading data into HANA from existing data source.2) Modelling data into HANA for facilitating data analysis using HANA Studio.3) Analysing the data in HANA using BI tools.

SAP HANA is designed to replicate and ingest structured data from SAP and non-

SAP relational databases, applications, and other systems quickly. One of three styles of

data replication trigger-based, ETL-based, or log-based - is used depending on the

source system and desired use-case. The replicated data is then stored in RAM rather

than loaded onto disk, the traditional form of application data storage. Because the data

is stored in-memory, it can be accessed in near real-time by analytic and transactional

applications that sit on top of HANA.

7. Limitations of SAP HANASAP HANA is not comfortable in analyzing very large amount of data (in Peta

Bytes) mostly known as big data. Therefore, HANA is not suited for social networking

and social media data analytics. For such uses cases, enterprises are better off looking to

open-source big-data approaches such as Apache Hadoop or LexisNexis HPC CSystems.

While SAP has announced a slew of new HANA-optimized applications, currently

only a few are on the market. It is incumbent upon SAP to follow through on its

commitment with practical applications that address real-world business problems.


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Also, SAP HANA is not made to support non-SAP applications, and to support

such application requires significant application re-engineering on the part of enterprise

IT groups.

You cannot expect great change by just replacing your current database

infrastructure by HANA. You also need to re design the application to some extent to

get best out of HANA.

8. ConclusionSAP HANA is a great in Memory analytical tool which can analyze huge amount

of data in real time which is great advantage to many huge companies around the

world. But its still new to adopt extensively by the enterprise BI community but its

usage is increasing day by day and is seen as a great revolutionary technology for

future.

9. References

Site:- http://sapignite.com/what-is-sap-hana/


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(Date: 15rd Sep 2012)

http://sapignite.com/why-sap-hana-database-applianc/(Date: 15rd Sep 2012)

http://en.wikipedia.org/wiki/In-Memory_Processing(Date: 15rd Sep 2012)

http://www.bluefinsolutions.com/insights/blog/the_sap_hana_faq_answering_key_sap_in_memory_questions/(Date: 20rd Sep 2012)

http://www.saphana.com/docs/DOC-2272(Date: 20rd Sep 2012)

http://articles.timesofindia.indiatimes.com/2011-09-21/strategy/30183979_1_hana-jim-hagemann-snabe-data(Date: 26rd Sep 2012)

http://archive.ciol.com/cgi-bin/printernew.asp?id=114860(Date: 14

rd

Oct 2012)

http://wikibon.org/wiki/v/Primer_on_SAP_HANA(Date: 6rd Nov 2012)

http://articles.businessinsider.com/2012-01-25/news/30662175_1_new-database-oracle-hasso-plattner(Date: 8rd Nov 2012)

http://www.cio.com.au/article/373945/in-memory_computing/(Date: 15rd Nov 2012)

http://en.wikipedia.org/wiki/In-memory_database
http://sapignite.com/why-sap-hana-database-applianc/http://sapignite.com/why-sap-hana-database-applianc/http://www.bluefinsolutions.com/insights/blog/the_sap_hana_faq_answering_key_sap_in_memory_questions/http://www.bluefinsolutions.com/insights/blog/the_sap_hana_faq_answering_key_sap_in_memory_questions/http://www.bluefinsolutions.com/insights/blog/the_sap_hana_faq_answering_key_sap_in_memory_questions/http://www.bluefinsolutions.com/insights/blog/the_sap_hana_faq_answering_key_sap_in_memory_questions/http://www.bluefinsolutions.com/insights/blog/the_sap_hana_faq_answering_key_sap_in_memory_questions/http://www.saphana.com/docs/DOC-2272http://www.saphana.com/docs/DOC-2272http://articles.timesofindia.indiatimes.com/2011-09-21/strategy/30183979_1_hana-jim-hagemann-snabe-datahttp://articles.timesofindia.indiatimes.com/2011-09-21/strategy/30183979_1_hana-jim-hagemann-snabe-datahttp://articles.timesofindia.indiatimes.com/2011-09-21/strategy/30183979_1_hana-jim-hagemann-snabe-datahttp://articles.timesofindia.indiatimes.com/2011-09-21/strategy/30183979_1_hana-jim-hagemann-snabe-datahttp://articles.timesofindia.indiatimes.com/2011-09-21/strategy/30183979_1_hana-jim-hagemann-snabe-datahttp://archive.ciol.com/cgi-bin/printernew.asp?id=114860http://archive.ciol.com/cgi-bin/printernew.asp?id=114860http://wikibon.org/wiki/v/Primer_on_SAP_HANAhttp://wikibon.org/wiki/v/Primer_on_SAP_HANAhttp://articles.businessinsider.com/2012-01-25/news/30662175_1_new-database-oracle-hasso-plattnerhttp://articles.businessinsider.com/2012-01-25/news/30662175_1_new-database-oracle-hasso-plattnerhttp://articles.businessinsider.com/2012-01-25/news/30662175_1_new-database-oracle-hasso-plattnerhttp://articles.businessinsider.com/2012-01-25/news/30662175_1_new-database-oracle-hasso-plattnerhttp://articles.businessinsider.com/2012-01-25/news/30662175_1_new-database-oracle-hasso-plattnerhttp://www.cio.com.au/article/373945/in-memory_computing/http://www.cio.com.au/article/373945/in-memory_computing/http://en.wikipedia.org/wiki/In-memory_databasehttp://en.wikipedia.org/wiki/In-memory_databasehttp://en.wikipedia.org/wiki/In-memory_databasehttp://www.cio.com.au/article/373945/in-memory_computing/http://articles.businessinsider.com/2012-01-25/news/30662175_1_new-database-oracle-hasso-plattnerhttp://articles.businessinsider.com/2012-01-25/news/30662175_1_new-database-oracle-hasso-plattnerhttp://wikibon.org/wiki/v/Primer_on_SAP_HANAhttp://archive.ciol.com/cgi-bin/printernew.asp?id=114860http://articles.timesofindia.indiatimes.com/2011-09-21/strategy/30183979_1_hana-jim-hagemann-snabe-datahttp://articles.timesofindia.indiatimes.com/2011-09-21/strategy/30183979_1_hana-jim-hagemann-snabe-datahttp://www.saphana.com/docs/DOC-2272http://www.bluefinsolutions.com/insights/blog/the_sap_hana_faq_answering_key_sap_in_memory_questions/http://www.bluefinsolutions.com/insights/blog/the_sap_hana_faq_answering_key_sap_in_memory_questions/http://sapignite.com/why-sap-hana-database-applianc/


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(Date: 15rd Nov 2012)

http://en.wikipedia.org/wiki/SAP_HANA(Date: 15rd Nov 2012)

http://whatis.techtarget.com/definition/in-memory-database(Date: 16rd Nov 2012)

http://slashdot.org/topic/datacenter/the-rise-of-in-memory-databases/(Date: 17rd Nov 2012)

http://www.webopedia.com/TERM/I/in_memory_analytics.html(Date: 17rd Nov 2012)

http://www.saphana.com/docs/DOC-1085(Date: 20rd Nov 2012)

http://www.clivemargolis.com/articles-about-bi/qlikview-sap-hana-cognos-tm1-%E2%80%93-in-memory-analytics-what%E2%80%99s-it-all-about/(Date: 22rd Nov 2012)

https://scn.sap.com/thread/2065611(Date: 23rd Nov 2012)

Book:- SAP HANA Master Guide By SAP Press

(Date: Oct-Nov 2012)
http://en.wikipedia.org/wiki/SAP_HANAhttp://en.wikipedia.org/wiki/SAP_HANAhttp://whatis.techtarget.com/definition/in-memory-databasehttp://whatis.techtarget.com/definition/in-memory-databasehttp://slashdot.org/topic/datacenter/the-rise-of-in-memory-databases/http://slashdot.org/topic/datacenter/the-rise-of-in-memory-databases/http://www.webopedia.com/TERM/I/in_memory_analytics.htmlhttp://www.webopedia.com/TERM/I/in_memory_analytics.htmlhttp://www.saphana.com/docs/DOC-1085http://www.saphana.com/docs/DOC-1085http://www.clivemargolis.com/articles-about-bi/qlikview-sap-hana-cognos-tm1-%E2%80%93-in-memory-analytics-what%E2%80%99s-it-all-about/http://www.clivemargolis.com/articles-about-bi/qlikview-sap-hana-cognos-tm1-%E2%80%93-in-memory-analytics-what%E2%80%99s-it-all-about/http://www.clivemargolis.com/articles-about-bi/qlikview-sap-hana-cognos-tm1-%E2%80%93-in-memory-analytics-what%E2%80%99s-it-all-about/http://www.clivemargolis.com/articles-about-bi/qlikview-sap-hana-cognos-tm1-%E2%80%93-in-memory-analytics-what%E2%80%99s-it-all-about/http://www.clivemargolis.com/articles-about-bi/qlikview-sap-hana-cognos-tm1-%E2%80%93-in-memory-analytics-what%E2%80%99s-it-all-about/https://scn.sap.com/thread/2065611https://scn.sap.com/thread/2065611https://scn.sap.com/thread/2065611http://www.clivemargolis.com/articles-about-bi/qlikview-sap-hana-cognos-tm1-%E2%80%93-in-memory-analytics-what%E2%80%99s-it-all-about/http://www.clivemargolis.com/articles-about-bi/qlikview-sap-hana-cognos-tm1-%E2%80%93-in-memory-analytics-what%E2%80%99s-it-all-about/http://www.saphana.com/docs/DOC-1085http://www.webopedia.com/TERM/I/in_memory_analytics.htmlhttp://slashdot.org/topic/datacenter/the-rise-of-in-memory-databases/http://whatis.techtarget.com/definition/in-memory-databasehttp://en.wikipedia.org/wiki/SAP_HANA

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