OpenSAP HANA INTRO Transcripts

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openS An Int UNIT 1: Intr 00:00:00 00:00:21 00:00:30 00:00:38 00:00:48 00:00:57 00:01:04 00:01:13 00:01:25 00:01:32 00:01:46 00:01:58 00:02:05 00:02:13 00:02:25 00:02:32 00:02:40 00:02:48 SAP troduc roduction a So why di how we e So the ba 80s, early when SQL away from and mana structured And that's And at the the hardw hardware Or even a the hardw How was have main and... wel And then accessing Somethin And the o database The DRAM It was mill thing was And so M itself. And alrea limitation, governed the proces in order to by 2003 a reach. ction t nd Backgro id we do HAN nded up here sic idea was y 90s, L was a relat m file-based d agement of s d relational w s what SQL m e time when ware that imp that is availa already 10, 1 ware was alre it different? S n memory, l, actually the there is the d g it out of dis g like 10,000 ther thing tha when it was M was much lions of times that CPUs b oore's Law w dy by 2003 i by things tha ssors were n o continue th also, it was cl to SAP ound of SAP NA, and wha e? s very straigh tional algebra data manage pecialized st way to manag means. It's a the RDB wa lemented rel able now. 1, 12 years a eady very diff So if you loo ere are layer disk. And acc k. 0 times or mo at happened first built. h more expen s worse in pr back then we was largely a t was clear t at we cannot not going to b e performan lear that a co P HAN P HANA at is some of htforward. Th a, and SQL s ement tructures and ge data. structured q s designed, lational data ago, when w ferent then. ok at this pict rs in between cessing data ore faster. So d was.... so th nsive and mu rice/performa ere single co attained beca that this was t control, like be single cor nce and manu ompletely ne NA by D the backgro he relational d started to be d so forth, an query langua bases was s we started thi ure, typically n as well; the a from memo o this is slow his was not c uch smaller t ance than it i re. ause of impro running into e the speed o re any more ufacturing be ew kind of da Dr. Vis ound, how HA database wa ecome popula nd objects, a ge, that was significantly d nking about y in computer e on board ca ory is dramat w, and it is ug clear in the d han it is now s now. Also, ovement in th a physical w of light and th enefits of Mo tabase parad shal S ANA came a as designed ar. People w nd get into a s the original different than re-doing the rs we have C aches and s tically faster t gly, and we d days of the re w. , the other fu he single cor wall, into a ha hings like tha oore's Law. S digm was wi Sikka bout and in the late wanted to get a more name for it. n the database, CPUs, we o forth. than don't like it. elational ndamental re CPU ard at, and that So already thin our

description

Introduction to SAP HANA

Transcript of OpenSAP HANA INTRO Transcripts

Page 1: OpenSAP HANA INTRO Transcripts

openSAn Int

UNIT 1: Intr

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SAP troduc

roduction a

So why dihow we e

So the ba80s, early

when SQLaway from

and manastructured

And that'sAnd at the

the hardwhardware

Or even athe hardw

How was have main

and... wel

And then accessing

Somethin

And the odatabase

The DRAM

It was millthing was

And so Mitself.

And alrealimitation,

governed the proces

in order toby 2003 areach.

ction t

nd Backgro

id we do HANnded up here

sic idea wasy 90s,

L was a relatm file-based d

agement of sd relational w

s what SQL me time when

ware that impthat is availa

already 10, 1ware was alre

it different? Sn memory,

l, actually the

there is the dg it out of dis

g like 10,000

ther thing thawhen it was

M was much

lions of timesthat CPUs b

oore's Law w

dy by 2003 i

by things thassors were n

o continue thalso, it was cl

to SAP

ound of SAP

NA, and whae?

s very straigh

tional algebradata manage

pecialized stway to manag

means. It's a the RDB wa

lemented relable now.

1, 12 years aeady very diff

So if you loo

ere are layer

disk. And acck.

0 times or mo

at happenedfirst built.

h more expen

s worse in prback then we

was largely a

t was clear t

at we cannotnot going to b

e performanlear that a co

P HAN

P HANA

at is some of

htforward. Th

a, and SQL sement

tructures andge data.

structured qs designed,

lational data

ago, when wfferent then.

ok at this pict

rs in between

cessing data

ore faster. So

d was.... so th

nsive and mu

rice/performaere single co

attained beca

that this was

t control, likebe single cor

nce and manuompletely ne

NA by D

f the backgro

he relational d

started to be

d so forth, an

query langua

bases was s

we started thi

ure, typically

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ause of impro

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ufacturing beew kind of da

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ovement in th

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enefits of Motabase parad

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ANA came a

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he single cor

wall, into a ha

hings like tha

oore's Law. Sdigm was wi

Sikka

bout and

in the late

wanted to get

a more

name for it.

n the

database,

CPUs, we

o forth.

than

don't like it.

elational

ndamental

re CPU

ard

at, and that

So already thin our

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And whendatabase,

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ytical workloahe belief tha

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and built ahis friends

started to projects th

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who was awas an in

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back in 20was pretty

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unbelieva

Part of thehistory of

As we arelaunch of HANA bec

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So that is supposed

Everybodymost miss

including the next 1

And we arsystems o

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UNIT 2A: S

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SAP HANA T

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Seattle,

anything oone core,

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billion scans

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so's notes an

s, you can do

basically wh

out 12.5 to 1

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rallelism in tharallelism.

at is, in a cosm.

a little job ane can take a

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relational daendous adva

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ttle things lik

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gregations

agine,

s. All the

you can

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SAP HANA T

The secon

The colum

The colum

But basicastore ever

So this is store is in

like optimmemory, y

And so weteam had

So we havtransactio

is that youalready th

I hope youwhen you

one of ouBSEG,

which is othe heade

The accou

The docunot — 320

So it is a vstructures

wants to khandle ma

So in a coyou are in

and demodisk, you

then you athis inform

Here, not massively

In fact, yo

Technology:

nd big one in

mn stores are

mns, they are

ally, you takerything abou

column store-memory, an

istic, latch-freyou have to

e do latch-fredesigned, a

ve both of thons quite quic

u can do anahe number.

u guys reme think about

r sales order

one of the twoers and this is

unting line ite

ment name, 0 pieces of in

very, what ws and whenev

know somethaybe 10, 20

olumnar datanterested in g

onstrate that.are grabbing

are identifyinmation up. So

only do you y parallel, bec

ou can take m

Row and C

n HANA is th

e basically lik

e not all unifo

e a relation wt it in column

e, and you hnd it has som

ee index travbe able to do

ee index travnd so on and

ese. And theckly. The ben

alytics and re

mbered this:enterprise d

rs, for examp

o core tabless the line item

ems. And the

number, typnformation a

we would call ver somebod

hing about, leout of these

a structure, thgetting inform

. In a row stog things row

ng the columo it's dramati

get just the ccause you ca

more than on

8

Column Stor

e row and co

ke that.

orm, and I'll g

which sort of ns.

ave the row me pretty ama

versal, whicho that withou

versal. So thid so forth.

e benefits, ofnefit of the co

eads dramati

: three and aata structure

ple, or the ma

s; BKPF is thms.

e BSEG table

e, is it creditbout that.

wide data stdy, a normal

et's say, acco320 fields.

hat means thmation on, qu

ore, in a tradiby row, and

ns out of thecally slower.

columns thaan assign dif

ne core for on

res

olumn stores

get into the r

looks like a

store, whichazing inventi

h is when youut locking up

s was a new

f course, of tolumn store,

cally faster.

a half billion ses,

anufacturing

he other one,

e has somet

, is it debit, is

tructure. Andhuman bein

ounting line i

hat you just puickly assem

itional disk-bthen after yo

e rows that yo.

t you are intefferent cores

ne particular

s.

eason for wh

table or an E

is more tradons,

u have to stothe whole th

w data structu

he row store like I alread

How much fa

scans per se

order, or the

, in our Finan

hing like 320

s this the add

d when you hg,

items, our br

pick out the oble them into

ased row stoou have retrie

ou are lookin

erested in, bu to grab the

column. And

hy that is.

Excel spread

ditional, exce

ore a transaching.

ure that Sang

e are that youdy talked abo

aster? Well,

econd per cor

e account se

ncials applica

0 fields in it.

dress, is it ov

have such wi

rains have th

ones that youo the result,

ore, you are eved all the r

ng for, and th

ut, in fact, dodifferent colu

d this is the f

sheet, and

ept our row

ction in

g and his

u can do out,

I mentioned

re. And

egments, the

ation. This is

verdue or

ide data

he ability to

u need, that

going to the rows,

hen pulling

o that umns.

fancy

s

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cocktail th

So that's bthey were

and our tethe way w

So you hathat we ca

So this is transactio

very quickmerge, intslow

so whenethe main,

And if eveAnd one o

is we have

which sitsLike let's s

that is flyinat the sam

or you waknow, eveon the fiel

or MRI mawhich are

those cangets somehappen,

and then things. So

which enatime prese

and absorit, the worsitting the

and you atranslating

and everydown his

So this is

hing that I tol

basically thee slow when

eams workedwe do that is

ave the basicall the delta s

the main colons come into

kly, and everto the main s

ever there is athen you ca

erything that of the things

e added a co

s as a buffer say if you are

ng in the skyme time to th

ant to captureery piece of ild,

achines of Pe sending out

n come into the breathing r

in the meanto this is an ex

ables us to ruerving the be

rb the transarld sort of woere and trans

are speaking g the thing in

y once in a wthoughts in E

the main des

d you about,

idea. Now trit came to tra

d super hard actually a bu

c column stostore.

lumn store, ao the delta

ry once in a wstore. And th

a question, ifn do a join b

you are lookthat we have

oncept of wh

in front of thee capturing e

y, and all the e ground. Yo

e every tradenstrumentati

hilips, or Siet super-fast t

he L1 delta, room, you du

time, if there xtremely nov

un very, veryenefits of the

actions at a vorks like that.lating things

English supn Chinese to

while you get English, that

sign of the ce

9

, the intra-op

raditionally, oansactions,

over the yeaunch of quite

re here. In a

and this is th

while, they ghen wheneve

f there is infobetween the d

king for is in te added rece

hat we call an

e delta. And events comin

engines of eou want to ab

e that is goingion that is co

emens, or Getransactions,

and as the trump them int

are questionvel and supe

y fast queriese row store,

very high spe. If you ever gfor you,

per fast and tthe other pe

so fast that is sort of like

entral structu

perator parall

one of the m

ars to make se clever, ama

ddition, we h

e delta colum

et merged, ter there is a q

ormation thatdelta and the

the main, theently into this

n L1 delta, w

this can absng out of eve

every airplanbsorb like a m

g on in Wall oming from a

eneral Electri

ransactions ato the delta o

ns that comer-high perfor

s and column

eed into the rgo to China,

hen there is eople who are

he has to take the L1 delta

ures of HANA

elism.

yths of our c

sure that thisazing techniq

have a less o

mn store. And

hrough a proquestion — b

t is partly in te main.

en of course s design of th

hich is a vari

sorb transactery airplane o

e in the sky million event

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c, or any of t

are closed door into the ma

e in, you do jormance archi

nar operation

row store. Anand you hav

this guy whoe sitting in th

ke out a little a buffer here

A. It's the co

column store

s is not the cques.

optimized col

d what happ

ocess called by the way, t

the delta and

it is really, rehe delta and

iation on this

tions really, rof your airline

are sending ts per second

ou want to cas of John De

these kinds o

own or whenain, as things

oins across titecture

ns while at th

nd when youve a translato

o is continuohe room,

pen and stae.

lumn store th

s was that

case. And

lumn store

pens is that

the delta his is not

d mostly in

eally fast. the main

s row store,

really fast. e company

out events d,

apture, you eere that are

of things

n the system s will

these three

he same

think about or who is

usly

art to write

hat's our

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main inve

that enabfor transa

And keep think that

this is not analytics ainside the

as a way design. A

and the otdifficult fo

ention, and in

les us to be actions.

in mind: Onthe row store

the case. Yoand transact

e column stor

to buffer up tnd we were a

ther people hr them to do

n addition the

able to achie

e of the thinge is for trans

ou can do rotions in the cre,

the transactiable to do th

have decadeso than for u

10

e parallel row

eve a dramat

gs that peopsactions and

ow analytics acolumn store,

ons in the rohat because w

es of legacy tus.

w store

tic performan

le get confusthe column s

and transact, and we hav

ow. So it is a we started fr

that they hav

nce, not only

sed about in store is for a

ions in the rove some attri

completely wom scratch,

ve to protect.

y for analytics

HANA is thaanalytics:

ow store, youbutes of the

without comp

. And it is mu

s but also

at people

u can do row store

promise

uch more

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SAP HANA T

So the thi

Dynamic a

And when

Like that, that you g

That icon compress

I think it's So, what

So in a cosomething

when youis maybe

So grabbiand more

and you juthose, andout.

So this is we want o

and to foll

In the pasthat you c

In the wormentioned

That givessame thin

Wheneveaggregatio

So if you winformatio

you can ccache tha

and our kfaster, andunnecess

and intermaggregatio

Technology:

rd main area

aggregation,

n looking for

and some ofgrabbed. Like

that Sanjay sion is someh

more like a is going on in

olumn store, g, when you

need to get10 fields out

ng those out going like th

ust need 10 od achieve a d

a dynamic pour applicatio

low the princ

st, because dcan out of the

rld of HANA d; three and

s us the abiling applies to

r you need toons per seco

want to calcuon and then c

calculate the at and answe

ids at HPI had so on. But

sary, cluttered

mediate valueons — this is

Projections

a of innovatio

, and lightwe

the icons, th

f these thinge that.

has is the icohow like a we

squeezed din projections

like I said, Bneed to proj

, let's say, alt of these 320

t of the 320, his,

out of these dramatic per

projection. Onon programm

cipal of minim

databases wee database, k

this is not nea half billion

ty to do minidynamic agg

o calculate aond per core

ulate weekly calculates a w

week's worther the second

ave been wothe point is, d data struct

es, and so fos hugely imp

11

s, Dynamic

on is in the a

eight compres

is is the proj

s are filled, a

on for projeceird disk with

sk than a cos, dynamic ag

BSEG has 32ect on this,

ll the custom0.

so you have

— this one, rformance be

ne of the prinmers to get, is

mal projection

ere slow, wekeep it in the

ecessary, be scans per s

imal projectiogregation.

a total, this sp, and we can

sums, insteaweek's worth

h of totals ond time around

orking on thisaggregation

tures, and ta

orth. You canportant in ana

Aggregatio

rea of projec

ssion, or... le

ections icon,

and then you

ctions, and agh these arrow

mpression, bggregation, a

20 fields in it.

mers which ar

e 320 fields in

that one, thaenefit, improv

nciples that ws to do this p

n, meaning g

e used to have application,

cause of thesecond per co

ons just as o

peed is twelvn do this dyn

ad of havingh of totals ou

n the fly. Andd much more

s fancy new ons don't havebles,

n calculate thalytics, espec

n, Integrated

ctions. And I'

et's call it inte

, it goes som

u do from the

ggregations,ws going in.

but I think yoand the integ

When you n

re overdue, a

n here in the

at one, and svement in ge

we teach, thaprojection as

get just the d

ve this notion and then do

tremendousore.

ften as we n

ve and a halfamically.

a batch procut of that,

d if it hasn't ce quickly.

object cache to be stored

hem on the flcially in analy

d Compress

ll explain wh

egrated comp

mething like th

ere into just t

, and the icon

ou know whagrated compr

need to aggre

and their add

column stor

so on — so juetting just tho

at we recommoften as the

ata that you

n that, grab eo processing

s scan speed

need, and so

f to fifteen m

cess that tak

changed, the

, which maked into these

ly. So dynamytics on raw

sion

hat this is.

pression.

hat.

hose two

n for

t I mean. ression?

egate

dresses, that

re, like that,

ust grab ose 10 fields

mend, that y need,

need.

everything on that.

d that I

on. The

illion

kes the raw

n HANA can

es it even

mic

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transactio

because yto group tunrestricte

do that limthe weekl

If you wanYou don't

Because thave to be

Here, diretime you f

And then reason I h

is becaus

You just taeven if yo

there are that there

So you caencoding

So you geis someth

that we haperson, yo

so there'sridiculouscan think

So the intespecially

when youof the field

What we customers

routinely gknow, up

what I waevery timeit was like

And out o700 gigab

onal data,

you can thinkhings togethed manner,

mited only byy totals in Ch

nt to know thhave to be l

then you aree limited to a

ectly, on-drawfeel like, on t

the integratehave been dr

e when you

ake just the fou have a bill

like, what, 20 are only 200

an create a dfor which co

et a dramaticing quite am

ave the abilitou know, the

s one bit, andly small amoof.

egrated comy when it com

have very sds are empty

have been ss who run an

get ten timesuntil the time

s always tolde I looked, it e 1.8 terabyte

of this, sometbytes is the w

k about aggrer and add t

y their imaginhina, except

e total from imited to tho

e limited to thas fresh as th

w informationthe fly.

ed compressrawing these

organize thin

fields that arion records o

00 countries0 values out

dictionary, hoountry that is

c compressiomazing about

ty to do that. ere's male an

d then you caount of memo

mpression in Hmes to things

sparse fields,y, and stuff li

eeing with Hnalytical data

s, twenty timee when it use

d was that it wis never mor

es.

thing like 1.1working mem

12

regations, yothings togeth

nation. So if yfor the big to

last night midose aggregat

hose questionhat informatio

n, you can ag

sion: This is se columns in

ngs by colum

re necessaryof informatio

s in the worldof there.

old the valuesin these billi

on improvemHANA,

You know, ind female,

an store inforory. And so o

HANA gives s like in analy

, when you hke that.

HANA is just aa warehouses

es, even thirted to run on

was somewhre than 2 tera

terabytes ismory of HANA

ou can think aher and enab

you want to kop 5 cities, yo

dnight until rtions that som

ns that someon is.

ggregate thin

something quunequal leng

mns, you don

y for columnsn,

, so if one of

s for these 2on columns.

ent without c

f one of thes

rmation on son and so for

us an abilityytics,

have things in

amazing. Wes, data marts

ty times comDB2,

here betweeabytes, inclu

s the actual dA.

about any kinle people to

know not justou can do th

ight now, youmebody did f

ebody though

ngs any time

uite amazinggths

n't have to sto

s, and you sto

f these fields

00 countries

compromisin

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y to do treme

n transaction

e see that ans, things like

mpression. Ou

n 11.5 and 1uding the wor

atabase size

nd of way thado this comp

t the weekly hat on the fly.

u can do thafor you in ad

ht about for y

e you think ab

g. What happ

ore every sin

ore those. Fo

s is Country,

s, and then ju

g performan

s, let's say, th

half billion pey kind of field

endous savin

nal systems w

nalytical workthis,

ur own ERP

12 terabytes rking memor

e, and the re

at you want pletely in an

totals but .

at on the fly. vance.

you. You

bout, any

pens is, the

ngle row.

or example,

you know

ust have an

ce. And that

he sex of a

eople in a d that you

gs in space,

where a lot

kloads,

system, you

of data. Andry, so today

maining

u

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So this is mean, if y

if you get you have and data

these are you just n

So the amlandscape

So that wa

quite amazinyou think abo

rid of all the the standardmarts,

all copies, aeed to keep

mount of savie with HANA

as projection

ng that we arout it, if you g

totals, all thed system from

and copies ofthe raw data

ngs, the amoA is simply am

ns, dynamic a

13

re able to geget rid of the

e indices, if ym the OLTP

f copies, anda. And the ra

ount of simpmazing, enor

aggregation,

et that much caggregates,

you get rid ofsystem, and

d extracts of caw data itself

lification thatrmous.

, and the inte

compression

f the redunda then you ha

copies, and tf is compress

t we can ach

egrated comp

n, and as a re

ant replicas oave the data

things like thsed.

hieve in an en

pression of H

esult, I

of data, so warehouse

his. In HANA

nterprise

HANA.

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UNIT 2D: S

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SAP HANA T

The next o

which we are some fact, for a

And those

and hot astore,

a great wa

And so yorecord he

And you iinformatioinformatio

then in coseparate

The beneinvalidatio

It is possiover a pe

And in HAHANA

as a comb

One of thethe fact, w

and that isfancy pict

...maybe hpieces, an

I don't knoHasso's s

But basicapartition th

across mapart of the

That partihave som

and stuff lthe colum

Technology:

one is a cou

did becausereally valuab

ny kind of an

e are things l

nd cold, activ

ay to deal wi

ou add new cre and just m

nvalidate theon, like you hon,

olumn storageprocess, inva

fit of this is inon strategy —

ble to createriod of time?

ANA, we get

bination of an

e things in Hwith HANA w

s the ability ture that San

he drew it liknd stuff like t

ow how to drslides, that's S

ally, what is shat across no

achines that e data is in o

tioning can bmetimes in fac

like that, whemn into differe

Insert Only

ple of more,

e we could, bble capabilitin application.

ike INSERT

ve and passi

th transactio

columns in thmaking the rig

e previously hhave to updat

e, it is very aalidate the p

n fact — dep— it is possib

e audit trails. And that is a

this natively.

n insert and

ANA that is ewe did this rig

to partition dajay has draw

ke that, and that.

raw that.... likSanjay at wo

says is, if yoodes,

are connectene machine,

be done by roct sheets, fac

ere you haveent parts and

14

y, Partitionin

fundamenta

because we wes for our kin.

ONLY, parti

ive storage.

ons that come

here. Addingght insert in

held entry. Ste an addres

advantageourevious entry

pending on hble to recreat

It is possiblean extraordin

. In fact, the

then an inva

extremely imght at the beg

ata, and to swn of a...

hen this has

ke that... So ork, who cam

u have, let u

ed to each o, and partly in

ow or by coluct tables, or

e tons of infod send them

ng & Scale-O

ally new capa

were doing itnd of applica

tioning and s

So, INSERT

e in is to sim

a new entrythe appropria

So even whenss of a custom

s to simply cy that you ha

ow you invale histories.

e to do time tnarily valuab

update sequ

alidation of th

mportant: In mginning, from

scale out acro

four pieces,

you'll see thame up with th

us say, a lot o

other, you can another.

umn, meaninin point-of-sa

rmation all into different p

Out, Active a

abilities,

t from scratchations, for ent

scale-out,

ONLY is, wh

mply insert the

y in here meaate place.

n you have tomer, or upda

create a new ad.

lidate this, ho

travels. How ble capability

uence operat

he thing that's

many databam scratch,

oss machine

and then thi

at. When youis idea of pa

of information

n dynamicall

ng if you havale data,

n one columnparts of mem

and Passive

h, and othersterprise app

hen we have

em.

ans taking a

o update a pate some pie

w entry, and t

ow you have

did somethi.

tion is implem

s not valid an

ases, they did

es. So this is

is one is like

u see that icoartitioning.

n, and you n

ly partition th

ve giant colum

n, then you cmory on one s

e Storage

s cannot, lications; in

e a column

part of that

piece of ce of

hen as a

e your

ng change

mented in

nymore.

d this after

this very

e a zillion

on in

eed to

hem so that

mns like we

can split up server,

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or multipledatabase

And then Then you

Typically,

On a 16–nlike that.

So of couinherently

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the hot...

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so that weHANA ena

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get even bettede the databa

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nce. And

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AP HANA T

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Predictive

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ngs don't ogrammer,

ut any mpliant.

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more eng

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ractively be y to do in

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AP HANA Pe

So, what d

When we existed be

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Which is awhen you

that is basthat, if you

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erformance B

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think about etween OLTP

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mazing thingg in HANA at

re in this 10,mple of that.

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22 million. Ay used to do

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was a three d

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sically like, if u were to fly

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mes faster ths in the 10,00

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my conclusionink the notio

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g that I foundt least 10,00

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nded the come 22 million to

nd out of thoin our ERP s

ould calculathem as well a

day long run,

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large numbe000 times per

f you were tothere,

ously walk ate 6 minutes o

,000 times fa. Usain Bolt i

an a snail. T00 club.

appens is thaonal in natur

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20

s

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have to alsomarks, and so

we have mores, eleven hu

e have now 2er than they d

an extraordin

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his father is ners in Japan.

are loyalty. n Oracle dat

ntives to pay ases made b

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t sort of give000 times fas

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r us?

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cessing of unapplications

o rethink the o on.

e than 2,000 ndred implem

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ary situation

he head of ITHe's from the

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three days, ies.

human mind

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notion of pe

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what they

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end this. So

pared to

at, if you

w much

yhow, so 28

re doing this

ransaction

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that has c

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nk the notions at the ICDE

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lex query.

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nd finally, it is

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ese five dime

ns out that th

ings that

n people, so

ational data to achieve

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quickly does

se time.

stions

8 seconds.

ate on the

mean, look

nds out.

onstrate s

ensions,

here are, you

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know, as

Imaginatioto.

So there'smake that

And we'veitself; give

many as you

on is our only

s the paper tht available.

e been startinen the abilitie

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y limitation w

hat I have wr

ng this recenes of HANA:

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e.

when we thin

ritten about t

nt effort to re

k about the k

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kinds of thing

guys can tak

ncept of perfo

gs that we ca

ke a look at,

ormance ben

an apply this

and we'll

nchmark

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AP HANA Ro

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and its ev

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responsiv

realtime cthat we ha

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26

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AP HANA in

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In the oil iexplorator

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28

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But, with tdeployme

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Page 31: OpenSAP HANA INTRO Transcripts

00:13:51

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Page 32: OpenSAP HANA INTRO Transcripts

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