OpenSAP HANAINTRO1 Transcripts
Transcript of OpenSAP HANAINTRO1 Transcripts
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open An In
UNIT 1: Int
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AProdu
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o already
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And when
database,
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columnar
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other sort
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then it has
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n why colu
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ts of compa
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ery differen
structures
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nar technol
20 years ol
long time, p
ce. In the c
more than
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we looked
AP had to
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look at the ti
Munz did hi
database t
build TREX
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my PhD adv
also his stud
-memory ro
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03. When
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ople have k
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had becom
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ink about
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ology.
ase. And Fr
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the EUCLI
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ry had,
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hnology for
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So Hasso
investigat
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ths ago. In t
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And it ha
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UNIT 2A: S
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AP HANA
So let's ta
technolog
We can't
Hasso ha
those.
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data local
plus the f
And espe
run, that
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the fact th
We have
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server, it
2 terabyt
persisten
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us to fully
Unlike th
low, and
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This is 80
gigahertz
It's an un
So everyt
One of thprocesso
Three an
is nearly
with the n
means is
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kind of a t
calculate
echnology:
lk some HA
y.
be all about
s this amazi
thing to kn
ity in memo
ct that we r
cially thanks
e could wo
sically the s
at it runs m
the ability in
el.
operator in
as up to 80
s of DRAM,
e on the se
s a pretty a
utilize all of
assumptio
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ou have to s
you get 80
CPUs, roug
of clock spe
elievable a
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most impo 3.5 billion s
a half billio
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ssively par
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tore, the mo
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n integer sc
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ve some bu
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Seattle,
anything
one core,
or more o
from. We
because
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major op
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throw tha
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That is so
parallelis
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r less in one
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re of HANA
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means is th
s, we can e
run that also
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y has this f
little windo
epict a CPU
rt of the ico
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f it requires l
second on
dition to th
algorithm in
e can basic
hink of.
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culations, in
we call intra
d: Vishal sai
at basically
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nky icon th
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se scans,
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hat not only
hin the oper
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ething like
atabases or
where som
llel operator
t sort of loo
n the middl
look at Has
.
billion scan
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rs, we do ab
te anything
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rallelism. Th
tor paralleli
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ator itself, w
rallel. So it's
and a half
yesterday's
of the trem
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ks like that
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, you can d
basically wh
out 12.5 to 1
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rallelism in t
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UNIT 2B: S
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00:00:20
00:00:26
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00:00:44
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00:01:08
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00:01:23
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AP HANA
The seco
The colu
The colu
But basic
store eve
So this is
store is in
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memory,
And so w
team had
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The docu
not — 32
So it is a
structure
wants to
handle m
So in a cyou are in
and dem
disk, you
then you
this infor
Here, not
massivel
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echnology:
d big one i
n stores ar
ns, they ar
lly, you tak
ything abou
column stor
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istic, latch-fr
ou have to
do latch-fr
designed, a
e both of thns quite qui
can do an
e number.
guys reme
think about
r sales orde
ne of the tw
rs and this i
nting line it
ment name,
pieces of i
ery, what w
and whene
now somet
ybe 10, 20
lumnar datterested in
nstrate that.
are grabbin
re identifyi
ation up. S
only do you
parallel, be
u can take
Row and C
HANA is th
basically li
not all unif
a relation
t it in colum
, and you h
d it has so
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be able to d
e index trav
nd so on an
ese. And thkly. The be
lytics and re
mbered this:
enterprise d
s, for exam
o core table
the line ite
ms. And th
number, typ
formation a
e would call
er somebo
ing about, l
out of these
structure, tetting infor
In a row st
things row
g the colum
it's dramati
get just the
cause you c
ore than on
7
olumn Stor
e row and c
e that.
rm, and I'll
hich sort of
s.
ave the row
e pretty am
ersal, whic
that withou
ersal. So thi
so forth.
benefits, oefit of the c
ads dramati
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le, or the m
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e, is it credit
bout that.
wide data s
y, a normal
t's say, acc
320 fields.
at means thation on, q
re, in a tradi
by row, and
ns out of the
cally slower.
columns tha
an assign di
e core for o
es
lumn store
et into the r
looks like a
store, which
azing inventi
is when yo
t locking up
s was a ne
course, of tolumn store,
cally faster.
half billion
s,
nufacturing
e other one,
e has somet
, is it debit, i
ructure. An
human bein
ounting line i
at you justickly assem
tional disk-b
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ferent cores
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eason for w
able or an
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ons,
have to st
the whole th
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order, or th
in our Fina
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this the ad
when you
g,
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rested in, b
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column. An
y that is.
xcel spread
itional, exc
re a transac
ing.
re that San
are that yoy talked abo
ster? Well,
cond per co
account se
cials applic
fields in it.
dress, is it o
ave such wi
ains have th
nes that yo the result,
re, you are
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g for, and th
ut, in fact, d
different col
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sheet, and
pt our row
tion in
and his
can dout,
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e. And
gments, the
tion. This is
erdue or
de data
e ability to
need, that
going to the
ows,
en pulling
that
mns.
ancy
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cocktail t
So that's
they were
and our t
the way
So you h
that we c
So this is
transactio
very quic
merge, in
slow
so whene
the main,
And if ev
And one
is we hav
which sits
Like let's
that is flyi
at the sa
or you wa
know, evon the fiel
or MRI m
which are
those can
gets som
happen,
and then
things. S
which entime pres
and abso
it, the wor
sitting the
and you
translatin
and ever
down his
So this is
ing that I tol
asically the
slow when
ams worke
e do that is
ve the basi
ll the delta
the main col
ns come int
ly, and ever
o the main
ver there is
then you ca
rything that
f the things
e added a c
as a buffer
ay if you ar
ng in the sk
e time to th
nt to captur
ry piece of id,
chines of P
sending ou
come into t
breathing r
in the meant
this is an e
bles us to r rving the b
b the trans
ld sort of w
re and trans
re speaking
the thing i
once in a
thoughts in
the main de
d you about,
idea. Now t
it came to tr
super hard
actually a b
column sto
tore.
umn store,
the delta
y once in a
tore. And th
question, i
n do a join b
you are look
that we hav
ncept of wh
in front of th
e capturing
, and all the
e ground. Y
every trad
nstrumentati
hilips, or Sie
super-fast t
e L1 delta,
oom, you du
ime, if there
tremely no
n very, verynefits of the
ctions at a v
rks like that.
lating things
English sup
Chinese to
hile you get
nglish, that
ign of the c
8
the intra-o
raditionally,
nsactions,
over the ye
nch of quite
re here. In a
nd this is th
hile, they g
en whenev
there is inf
etween the
ing for is in
added rec
at we call a
e delta. And
vents comi
engines of
u want to a
that is goin
ion that is c
mens, or G
ransactions,
and as the t
mp them int
are questio
el and supe
fast querie row store,
ery high sp
If you ever
for you,
er fast and t
the other p
so fast that
is sort of lik
entral struct
erator parall
one of the m
rs to make
clever, ama
ddition, we
e delta colu
et merged, t
r there is a
rmation tha
elta and th
he main, th
ntly into thi
L1 delta, w
this can abs
g out of ev
very airplan
sorb like a
g on in Wall
ming from a
neral Electri
ansactions
o the delta
s that come
r-high perfor
and colum
ed into the r
go to China,
hen there is
ople who ar
he has to ta
the L1 delt
res of HAN
elism.
yths of our c
ure that thi
zing techniq
ave a less
n store. An
rough a pr
uestion —
is partly in t
main.
n of course
design of t
hich is a vari
orb transact
ry airplane
e in the sky
illion event
Street, or yo
ll the tractor
c, or any of
re closed d
r into the m
in, you do j
mance archi
ar operatio
ow store. A
and you ha
this guy wh
sitting in th
e out a little
buffer her
. It's the co
olumn store
is not the c
ues.
ptimized col
d what happ
cess called
y the way, t
he delta an
it is really, r
e delta and
iation on thi
ions really, r
f your airlin
are sending
s per secon
u want to c
of John D
these kinds
own or whe
in, as thing
ins across t
itecture
s while at th
d when you
e a translat
is continuo
e room,
pen and st
.
lumn store t
s was that
ase. And
umn store
ens is that
the delta
his is not
mostly in
ally fast.
the main
row store,
eally fast.
company
out events
,
pture, you
ere that are
f things
the system
will
hese three
e same
think about
r who is
usly
rt to write
at's our
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main inve
that enab
for transa
And keep
think that
this is not
analytics
inside the
as a way
design. A
and the o
difficult fo
ntion, and in
les us to be
ctions.
in mind: On
the row stor
the case. Y
and transact
column sto
to buffer up
nd we were
her people
r them to do
addition th
able to achi
e of the thin
e is for trans
u can do ro
ions in the c
e,
he transacti
able to do th
ave decad
so than for
9
parallel ro
ve a drama
s that peop
actions and
w analytics
olumn store
ons in the r
at because
s of legacy
s.
store
ic performa
le get confu
the column
nd transact
, and we ha
w. So it is a
e started fr
hat they ha
ce, not only
ed about in
tore is for a
ions in the r
e some attri
completely
om scratch,
e to protect.
for analytic
HANA is th
nalytics:
w store, yo
butes of the
ithout com
And it is m
but also
t people
can do
row store
romise
ch more
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UNIT 2C: S
00:00:00
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00:00:43
00:00:56
00:01:04
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AP HANA
So the thi
Dynamic
And whe
Like that,
that you g
That icon
compress
I think it's
So, what
So in a c
somethin
when youis maybe
So grabbi
and more
and you j
those, an
out.
So this is
we want
and to foll
In the pa
that you c
In the wo
mentione
That give
same thin
Wheneve
aggregati
So if you
informati
you can c
cache tha
and our k
faster, an
unnecess
and inter
aggregati
echnology:
rd main are
aggregation,
looking for
and some o
rabbed. Lik
that Sanjay
ion is some
more like a
is going on i
lumn store,
, when you
need to get10 fields out
ng those ou
going like t
st need 10
achieve a
a dynamic
ur applicati
ow the prin
t, because
an out of th
ld of HANA
; three and
us the abili
g applies to
r you need t
ons per sec
ant to calc
n and then
alculate the
t and answ
ids at HPI h
d so on. But
ary, cluttere
ediate valu
ons — this i
Projection
of innovati
and lightwe
the icons, th
these thing
that.
has is the ic
ow like a w
squeezed di
n projection
like I said, B
need to proj
, let's say, al of these 32
of the 320,
is,
out of these
dramatic per
rojection. O
n program
ipal of mini
atabases w
database,
this is not n
a half billion
ty to do mini
dynamic ag
o calculate a
nd per core
late weekly
alculates a
week's wort
r the secon
ve been wo
the point is,
d data struct
es, and so f
hugely imp
10
s, Dynamic
n is in the a
ight compre
is is the proj
s are filled,
on for proje
ird disk wit
sk than a co
, dynamic a
SEG has 3
ect on this,
l the custom0.
so you hav
— this one,
formance b
e of the pri
ers to get, i
al projectio
ere slow, w
eep it in th
cessary, be
scans per s
mal projecti
regation.
total, this s
, and we ca
sums, inste
week's wort
of totals o
time aroun
rking on this
aggregation
ures, and ta
rth. You ca
ortant in an
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rea of proje
sion, or... l
ections icon,
nd then you
tions, and a
these arro
mpression,
ggregation,
0 fields in it.
ers which ar
320 fields i
that one, th
nefit, impro
ciples that
to do this
, meaning
used to ha
application,
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econd per c
ns just as o
eed is twel
do this dyn
ad of having
of totals ou
the fly. And
d much mor
fancy new
s don't have
bles,
calculate t
lytics, espe
n, Integrate
tions. And I'
t's call it int
it goes so
do from the
gregations,
s going in.
ut I think yo
nd the inte
When you
e overdue,
here in the
t one, and s
ement in g
e teach, th
rojection as
et just the d
e this notion
and then d
tremendou
re.
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amically.
a batch pro
t of that,
if it hasn't c
quickly.
bject cache
to be store
em on the fl
ially in anal
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ll explain wh
grated com
ething like t
re into just t
and the ico
u know wha
rated comp
eed to aggr
nd their add
column stor
o on — so j
tting just th
t we recom
often as the
ata that you
that, grab
processing
scan spee
eed, and so
to fifteen m
cess that ta
hanged, the
, which mak
into these
ly. So dyna
tics on raw
ion
at this is.
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at.
hose two
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t I mean.
ession?
egate
resses, that
e, like that,
st grab
se 10 fields
end, that
y need,
need.
verything
on that.
that I
on. The
illion
es the raw
n HANA can
s it even
ic
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transactio
because
to group t
unrestrict
do that li
the weekl
If you wa
You don't
Because
have to b
Here, dir
time you
And then
reason I
is becaus
You just t
even if yo
there are
that there
So you c
encoding
So you g
is someth
that we h
person, y
so there's
ridiculous
can think
So the int
especiall
when you
of the fiel
What we
customer
routinely
know, up
what I wa
every tim
it was like
And out o
700 giga
nal data,
ou can thin
hings togeth
d manner,
ited only by
y totals in C
t to know th
have to be l
hen you ar
limited to
ctly, on-dra
eel like, on t
the integrat
ave been d
e when you
ake just the
u have a bill
like, what, 2
are only 20
n create a d
for which co
t a dramati
ing quite a
ve the abilit
u know, th
one bit, an
ly small am
of.
egrated co
when it co
have very s
s are empt
have been s
who run a
et ten time
until the tim
s always tol
I looked, it
1.8 terabyt
f this, somet
ytes is the
about aggr
er and add t
their imagin
ina, except
e total from
imited to tho
limited to th
s fresh as t
informatio
he fly.
d compress
awing these
organize thi
ields that ar
ion records
00 countries
values out
ictionary, ho
untry that is
compressio
azing about
y to do that.
re's male an
then you c
unt of mem
pression in
es to things
parse fields,
, and stuff li
eeing with H
alytical data
, twenty tim
when it us
was that it
is never mo
s.
hing like 1.1
orking mem
11
egations, yo
hings togeth
ation. So if
for the big t
last night mi
se aggregat
ose questio
at informati
, you can a
ion: This is
columns in
gs by colu
e necessary
f informatio
in the world
of there.
ld the value
in these billi
n improvem
HANA,
You know, i
d female,
n store info
ry. And so
HANA gives
like in anal
when you h
ke that.
ANA is just
warehouse
s, even thir
d to run on
was somew
re than 2 ter
terabytes is
ory of HAN
u can think
er and enab
ou want to
p 5 cities, y
dnight until r
ions that so
ns that som
n is.
gregate thi
omething q
unequal len
ns, you don
for columns
n,
, so if one o
for these 2
on columns.
ent without
f one of thes
mation on s
n and so fo
us an ability
tics,
ave things i
amazing. W
, data mart
y times co
DB2,
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abytes, inclu
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bout any ki
le people to
now not jus
u can do th
ight now, yo
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body thoug
gs any time
ite amazing
ths
't have to st
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these fields
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ompromisin
e columns i
even and a
th, for every
to do treme
transaction
see that a
, things like
pression. O
n 11.5 and 1
ding the wo
atabase siz
d of way th
do this com
t the weekly
at on the fly.
u can do tha
or you in ad
t about for
you think a
. What happ
re every sin
ore those. F
is Country,
, and then j
g performan
, let's say, t
alf billion p
kind of field
ndous savin
al systems
alytical wor
this,
ur own ERP
2 terabytes
rking memor
, and the re
t you want
letely in an
totals but
t on the fly.
ance.
ou. You
out, any
ens is, the
gle row.
r example,
you know
st have an
ce. And that
e sex of a
ople in a
that you
gs in space,
here a lot
loads,
system, you
of data. And
y, so today
maining
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So this is
mean, if y
if you get
you have
and data
these areyou just n
So the a
landscap
So that w
quite amazi
ou think ab
rid of all the
the standar
marts,
all copies,eed to keep
ount of savi
with HANA
as projectio
g that we a
ut it, if you g
totals, all th
system fro
nd copies othe raw dat
ngs, the am
is simply a
s, dynamic
12
re able to g
et rid of the
indices, if
the OLTP
copies, an. And the r
ount of simp
azing, enor
aggregation,
t that much
aggregates,
ou get rid o
system, and
extracts ofw data itself
lification tha
mous.
and the int
ompressio
the redund
then you h
opies, andis compres
we can ach
grated com
, and as a r
ant replicas
ve the data
things like thed.
ieve in an e
pression of
sult, I
f data, so
warehouse
is. In HANA
terprise
ANA.
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UNIT 2D: S
00:00:00
00:00:18
00:00:31
00:00:43
00:00:53
00:01:02
00:01:15
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00:01:36
00:01:45
00:01:55
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AP HANA
The next
which we
are some
fact, for a
And thos
and hot a
store,
a great w
And so y
record he
And you i
informati
informati
then in co
separate
The bene
invalidati
It is possi
over a pe
And in H
HANA
as a com
One of th
the fact,
and that i
fancy pict
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pieces, a
I don't kn
Hasso's s
But basic
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across m
part of th
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have som
and stuff l
the colum
echnology:
one is a cou
did becaus
really valua
ny kind of a
are things l
nd cold, acti
y to deal wi
u add new
re and just
nvalidate th
n, like you h
n,
lumn storag
process, inv
fit of this is i
n strategy
ble to create
riod of time?
NA, we get
ination of a
things in H
ith HANA w
the ability t
ure that San
e drew it lik
d stuff like t
w how to d
lides, that's
lly, what is
at across n
chines that
data is in o
tioning can
etimes in fa
ike that, wh
n into differ
Insert Onl
ple of more,
we could, b
le capabiliti
application.
ike INSERT
e and passi
th transactio
olumns in t
aking the ri
previously
ave to upda
e, it is very
lidate the p
fact — dep
it is possib
audit trails.
And that is
this natively.
insert and
ANA that is
e did this rig
o partition d
jay has dra
e that, and t
hat.
aw that.... li
Sanjay at w
ays is, if yo
odes,
are connect
ne machine,
e done by r
t sheets, fa
re you hav
nt parts and
13
, Partitioni
fundamenta
ecause we
es for our ki
.
ONLY, parti
ive storage.
ns that com
ere. Adding
ght insert in
held entry.
te an addre
dvantageou
revious entr
ending on h
le to recreat
It is possibl
an extraordi
. In fact, the
then an inv
extremely i
ht at the be
ata, and to s
n of a...
hen this has
e that... So
rk, who ca
u have, let u
ed to each
and partly i
ow or by col
t tables, or
tons of info
send them
g & Scale-
lly new cap
ere doing it
nd of applic
tioning and
So, INSERT
e in is to sim
a new entry
the appropri
o even whe
s of a custo
s to simply
that you h
ow you inval
e histories.
to do time t
arily valuab
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lidation of th
portant: In
inning, from
cale out acr
four pieces,
you'll see th
e up with th
s say, a lot
ther, you ca
another.
umn, meani
in point-of-s
rmation all i
to different
ut, Active
bilities,
from scratc
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cale-out,
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ply insert th
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you have t
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reate a new
d.
idate this, h
ravels. How
le capability
ence operat
e thing that'
any databa
scratch,
ss machine
and then thi
t. When yo
is idea of pa
f informatio
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and Passiv
h, and other
terprise app
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m.
ns taking a
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w you have
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ion is imple
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ses, they di
s. So this is
is one is like
see that ic
rtitioning.
n, and you n
ly partition t
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ory on one
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or multipl
database
And then
Then you
Typically,
On a 16–
like that.
So of cou
inherently
But HAN
able to ru
And som
which is
one of th
transactio
And we t
1,200 billi
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That's a v
three qua
So you c
Each nod
And basi
case wer
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was quart
So this is
other infr
And here
and scale
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In Financi
the curre
and so a
the open
as well a
months o
so then y
servers. A
in one mac
unleash the
get really a
we see perf
ode cluster
rse it depen
it is.
gives us th
n workloads
of the mor
nonymous r
biggest co
ns per day.
ok 10 years
on records.
trillion, in c
ery, very lar
rter times th
n kind of gu
e is 40 CPU
ally the incr
between 6
uestion tha
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like, these a
structures.
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t year. The
ear, of cour
items from t
the items fr
information
u'll do also
d of course,
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esome per
ormance sc
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panies in t
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ge number. I
e population
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ter compari
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ple, we kno
ear is typic
se, is typical
e previous
om this year
for one yea
the previous
14
, you can pa
on, or a co
re sitting on
ormance.
le-up and s
e performa
ture of the q
apabilities th
hines.
amples that
m one of ou
e world. An
ta. Wow! So
ounting, th
t's one and
of the world
retailer is.
terabyte of
as, all the
ds and 3.1
seconds w
on over 10
f questions
n a couple o
is hot and c
w that the a
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lly a little bit
ear that ca
that you wil
r. And then i
year. So th
rtition colum
bination of
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ce improve
uestion, and
at other dat
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t's 1.2 and t
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days on
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mpany is
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ant to keep
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maybe 14
arison,
an that in
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the hot...
You can
additional
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this kind
so that w
HANA en
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in active me
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compressio
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can get ev
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as INSERT
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rest of it,
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UNIT 2E: S
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AP HANA T
The next
That's the
completel
And yet, f
using for
Anyhow,
25 million
There is,
even in th
depends
So yeah,
support f
and the r
The ends
compliant
this is reg
SQL-like l
for multidi
inside HA
text functi
forth,
for specia
not in the
We also
people ar
I mean b
amount o
if you wa
10% to all
That is a
inverse o
you want
demonstr
And then
language
so people
We have,
the librari
echnology:
one I want t
language t
y redesigne
rom the out
ecades.
ere in the
people aro
ou know, t
e consumer
n SQL.
don't let the
ll SQL, so b
w store, an
see SQL 9
, no learning
ular SQL. A
anguage, st
mensional d
NA. We hav
ons, and th
l syntaxes a
standard S
upport map
e so fascina
sically, whe
data when
t to, let's sa
l your plan,
ap operati
that, that y
to do a filter
ated map-re
we have the
called L, wh
can write c
of course H
s for GIS d
SQL, Libra
talk about i
access HA
from scrat
ide, the inte
alley, there
nd the worl
ns and tons
world, all ou
fool you. S
oth on the c
both of the
and then s
, no special
d beyond S
arted at Micr
ata traversa
e support fo
n all kinds o
round that,
L.
reduce oper
ed by.
n you think
you want to
, increment
r something
n that appli
u want to re
or somethi
duce inside
stored proc
ich is a part
de, low-lev
ANA itself is
ta, for text
16
ries, and S
s SQL.
NA. Now th
h, ground u
rfaces are e
is a lot of tal
who know
and tons of
r ability to a
QL is no le
lumn store,
se have the
bsequent v
voodoo of o
QL we offer
osoft,
l, especially
r text-orient
f functional
eographical
ations, whic
bout it, a m
map an ope
everything
like that.
s widely, a
duce somet
g of that so
REX at the
edure langu
of the LLVM
l code direc
written in C
ata, for wh
mmary
beauty of
p.
actly the sa
k about no S
ow to progr
usage of S
k questions
s important
SQL front e
rsions of S
urs, no prop
all kinds of
used in anal
d things,
nhanceme
syntaxes, s
h is what ty
p is like a s
ration,
r do an agg
d we have
ing, you wa
t. So we off
DKOM in 2
ge SQLScr
,
tly in L, and
++, and ther
t we call the
ANA is that
me interfac
QL and stuf
am SQL.
L. The entir
and do tran
ow than it
d on top of
L that cam
ietary thing
dditional thi
ytics. We su
ts for busin
uff like that,
ically the “N
an that you
regation ove
map API. It
nt to do an
r map-redu
06.
pt. We have
have that b
are lots of
Business F
we did all of
s that peopl
f like this. T
e enterprise
sactions ba
as ever bef
them.
in, fully sta
here,
ngs: MDX,
pport MDX
ss function
which som
o SQL” mov
distribute o
r something
reduces ty
ggregation,
ce. In fact,
a native, lo
attached in
C++ librarie
unction Libr
this stuff
e have been
ere are like
world, and
ically
ore. So we
ndards-
hich is a
atively
and so
times are
ement
er a large
, or add
ically the
e
-level
o HANA.
in HANA,
ry, and the
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Predictiv
We integr
function li
and we a
anybody'
Now safe
HANA is
When we
HANA,
there is al
really sup
And that'
comes int
with Live
memory
because t
each oth
And so wi
happen.
working o
training w
So, those
If I forgot
So the ov
have sort
So you b
So these
things lik
And then
We also
and other
store for.
And then
these. So
calc engi
which incl
operation
We have
Analytics Li
ate R, which
braries like t
e coming u
library, insi
y here is ex
ntirely runn
insert code
ways a dan
er careful a
why we ha
o HANA. Yo
ache, we h
nd things lik
he program
r every onc
th HANA, w
o there are
n any datab
hatsoever, g
were five of
some, the b
erall picture,
of looks like
sically have
are things li
this.
these are st
ave the gra
stores that
So this is al
we have be
these are th
e, join engi
udes things
, rolling for
the geograp
brary.
is a statisti
his,
with a gen
de HANA in
remely imp
ing in-memo
like this and
er that bad
out how thi
e written so
u know, in t
d lots of ex
e that,
area of the
in a while,
have gone
all kinds of li
se that you
et up to spe
the most im
auty of the
when you t
this.
the core, le
e integers,
red in the r
h store that
e can add l
o text.
ond the cor
ings like the
e. We have
like aggreg
casts, and
hic engine in
17
al package
ric way, wh
a safe way.
rtant, beca
ry.
run that in t
things can h
code is int
me extreme
e early day
erience wit
ystem and
and stuff lik
to great len
ibraries like
can think of
ed and runn
portant tech
e MOOCs i
ink about th
's say data
nd text, an
w store, in
we are wor
ater on, as
e data types
OLAP engi
a special e
tions, disag
ersions of p
here, and s
in there, we
t we call th
se when we
e same ser
appen. So
grated into
ly rigorous a
of APO,
the system
he data are
that.
gths to ensu
hat. But, if y
in the world
ing on HAN
nical aspect
that I'll go
e power of
ypes and st
geographic
he column
ing on,
e think abo
and stores,
e,
gine for pla
regations,
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o on. This is
have IMSL..
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insert code
er, in the s
e have to b
ANA.
nd strict gui
crashing b
of the syst
re that thes
ou are a reg
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of HANA.
ack and ad
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res.
data, and s
tore.
t it. And tex
we have the
ning,
omplex pla
ory, and thin
also extens
., let's see...
ay to integr
like this and
me process
really, reall
elines of ho
cause of co
m used to
kinds of thi
ular SQL pr
and, withou
andards-co
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t we use th
engines th
ning-orient
gs like this.
ible, so we
all kinds of
ate people,
it runs,
space as
y, really,
w this code
rruption of
ollide with
gs don't
grammer,
t any
pliant.
on.
that we
ings, and
column
t work on
d
an also add
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more eng
And so o
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And other
about.
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able to ch
HANA.
And then
these are
PAL, text,
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with som
which incl
All kinds
users, an
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like the m
or master
data, or t
And all thoutside o
So as yo
us and fo
And just
happene
ines here as
. So there a
we have SQ
language s
have the a
ange them.
we have all
things like
statistics, o
HANA is m
form, on whi
thing that w
udes the la
f things to
stuff like th
we have all
essaging se
data servic
e rule engin
ese things t the databa
can see, H
our future.
hen the oth
in the last
we go. The
re all these
L. So the S
pport: MDX
ility to fully
These are t
kinds of libr
FL,
urs, others,
re than a d
ch we are b
e call the ap
guage runti
anage user
at.
kinds of new
rvice,
for MDM, o
e.
at used to tr e; all are av
NA goes fa
nd that is s
ers are start
years? HA
18
graph engin
ngines that
L plan and
and the oth
isualize the
e kinds of a
ries up her
ll kinds of li
tabase.
uilding all ki
plication ser
e, so supp
sessions, u
capabilities
r data servi
aditionally bailable as e
r beyond a
omething th
ing to think
A became
e will come
are inside H
execution, r
er language
plans that
mazing thin
. And these
braries like t
ds of platfor
vices of the
rt for JavaS
er authenti
from the pl
es for extra
e in integratitensions an
atabase, an
at is super-p
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ANA.
nning and
, SQLScrip
e make in
s that we h
libraries are,
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owerful.
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rming, and l
or in middlside HANA.
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ractively be
to do in
entioned,
rted off
in there.
memory for
g in here,
oading
ware
latform for
guess what
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UNIT 3: SA
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00:00:24
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P HANA Pe
So, what
When we
existed b
and betw
or betwe
we have t
itself. We
The reas
already,
sorts
and the a
somethin
So they a
first exam
A couple
Yodobas
so his gra
told me th
So that's
do is, the
once a mpurchase
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And whe
a perform
Which is
when you
that is ba
that, if yo
if you wer
were to fl
then you
faster 10,
10,000 ti
customer
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on data t
So every
rformance
does all that
think about
tween OLT
en structur
n being abl
o... one of
have to reth
n I say that
nd a thousa
mazing thin
in HANA a
re in this 10,
ple of that.
of years ago
i, and he's
ndfather fou
at they hav
2 million. A
used to do
nth, they w made by t
as a three
they ran thi
ance improv
very, very l
look at 10,
ically like, if
were to fly
e to continu
there in lik
ould be 10
000 times is
es faster th
in the 10,0
ason this h
at is operati
last transact
enchmark
technology
HANA and t
and OLAP
d processin
to build ne
y conclusio
ink the notio
is, I mention
nd or so imp
that I found
least 10,00
000 club. A
, Fujisawa s
lso the son
nded the co
22 million t
nd out of tho
in our ERP
uld calculatem as well
ay long run,
s thing on H
ement of ap
arge numbe
00 times pe
you were to
there,
usly walk a
6 minutes
000 times f
Usain Bolt i
an a snail. T
00 club.
ppens is th
onal in natur
ion that is n
19
s
mean? Wha
he ability, th
,
g of informa
application
s is that we
n of bench
ed earlier,
lementation
was that w
0 times fast
d it's quite
an told me t
of the owne
mpany and
tal custom
se, 5 million
ystem on a
e what inces the purch
on our ER
ANA, this ra
proximately
r. That is dif
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walk from
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ster. So tha
s about 10,
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t aggregate
t can it do fo
opportunit
ion and pro
s on legacy
have to als
arks, and s
e have mor
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have now
r than they
n extraordin
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of the firm.
is father is
rs in Japan.
are loyalty.
n Oracle dat
tives to payases made
system on
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provement,
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f 3 miles an
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00 times fa
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r us?
to shatter t
essing of u
applications
rethink the
on.
than 2,000
dred imple
7 or 28 cus
id before.
ary situation
he head of I
He's from th
ow the hea
So this is th
abase,
these guys,y everybod
n Oracle d
three days, i
s.
human mind
o to New Yo
hour, and i
s you an ide
ter than a s
o think abo
bashi exam
to look at e
his barrier th
structured i
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notion of pe
customers
entations
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T and opera
e founding f
of the com
e total. And
based on th.
tabase.
in 2 second
to compreh
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t it. Well an
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end this. So
ared to
t, if you
much
how, so 28
e doing this
ansaction
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that has c
They wan
this perso
and this l
that is pr
So a larg
that is ch
dramatic
So how d
HANA, w
and rethi
about this
and I thin
So there
performa
The first
gets, and
And the s
The ques
be pretty
finding m
on data.
of the sys
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the syste
A fourth d
How quic
answered
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At less th
and at les
system wi
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at these fi
the more
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tremendo
and think
and bring
ome in. It's
t to calculat
n did. So w
rge amount
bably a few
amount of
nging as w
erformanc
o we think a
have to ret
k the notion
at the ICD
that it com
re five dim
ce, by Dr.
ne is, of co
so forth.
econd one i
ions can ra
ime-consu
dians, perc
he more co
tem, and so
the third on
absorb ne
imension is,
ly can we g
in less than
uman brain
n 3 second
s than one
ith interactivi
way our br
ve dimensio
of these five
ple way to th
us value wo
about what
HANA to b
very compl
the purcha
en you take
of data, it is
billion recor
data, compl
speak, this
.
out this in
hink the con
of benchma
Conferenc
s down to fi
nsions of p
ikka.
rse, the dat
the query c
ge from sim
ing, to highl
ntiles, doin
plex our q
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is, let us ca
informatio
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et our questi
3 seconds,
tarts to lose
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econd, or le
ty, with realt
ins, our se
ns. And I be
dimensions
ink about th
ld be when
inds of sce
ar on those
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ex query.
ses made b
a combinati
22 million r
s.
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know, as
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to.
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UNIT 4: SA
00:00:00
00:00:19
00:00:29
00:00:39
00:00:48
00:01:00
00:01:12
00:01:24
00:01:35
00:01:47
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P HANA Ro
So that w
are we d
the road
SAP. So,
or it is on
Suite. An
The ERP
including
has been
knock on
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all kinds
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on HANA
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running n
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accelerat
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admap and
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ap of this is
bar none. Ei
the way to r
the Busine
application:
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running on
wood, we ar
hing; more t
f things are
ourse our o
for the last
e other appli
ow on HAN
ome amazi
yDesign, S
that run on
bringing H
at, all the te
in Novembe
and Stefan
that year, at
gineering a
uld non-dis
d it. So the
hings in B
n 500+ time
ading time iSO activatio
ubes that a
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data service
echnical pr
e all been o
ice picture h
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ce. And wh
HANA techn
very straigh
ther everyth
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ss Suite no
we have a d
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ANA as I s
e about to cl
an 60,000
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and a half
cations in th
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ng things wi
alesOnDem
HANA.
NA to ever
chnology pr
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and the tea
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chievement
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ducts are ru
timized to r
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22
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en we think
ology,
tforward. W
ing already
. And of cou
runs on H
ozen or so
al ERP sys
eak for app
ose the qua
mployees, t
ANA now.
m that Rob
onths, sin
e Business
application
h Ariba and
nd. Hybris
single prod
ducts.
id quite an a
delivered
e in China,
hat he had
HANA unde
d remain un
n hundreds,
been madeSA activatio
BW, those
gle thing, fr
master dat
nning on H
n on HAN
nt to show
evelopme
about what
are bringin
uns on HAN
rse that me
NA.
ustomers al
tem,
roximately 6
rter, close o
ime recordi
and Bill run
e March of t
uite, the in
.
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as already
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mazing thin
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asso, he th
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changed. It
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re we going
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ns we start
eady runnin
weeks, 5 a
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g, Financial
the Sales or
his year.
ustry applic
tors already
emonstrate
ngle applica
, and it was
.
nked me, a
ut in a way
ot compiled
nds of time
arallel loadie the stagin
times faster
engines, PI
d MDM,
application
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to do with t
ry single pro
first with the
g ERP live
d a half we
HANA.
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ganization o
ations, they'
running in t
d some of th
tion that we
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nd he called
that dramati
into HANA.
faster. Ma
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, messaging
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duct in
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ks. And,
ervice, HR,
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e all
he Cloud,
e great
have.
ig honours
it the
cally
y of the BW
ns things
retty
, and so
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this
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picture. E
So there i
Everythin
The ABA
platform
lots of thi
that's all t
And the J
HANA.
And you
So the G
HANA.
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three tier
It was inv
was creat
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it.
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runtime, i
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services li
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stuff like t
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and depl
and that i
applicatio
on top.
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call the in
— so this
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can talk t
Perl,
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applicatio
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erything he
s BW, ERP
g running on
7.4 is opti
nd HANA te
gs in the de
here.
ava platform
ee here inte
teway runs
at we call th
plan was kin
ented becau
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of the nearly
al scalabilit
pplication s
JavaScript
rMonkey run
ike user ma
s, all the kin
hat. This is
urse with th
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something
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he native, w
egrated en
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HANA. An
people's fav
d run with H
urse with th
n inside of
, one of the
re I mention
and the Busi
ABAP 7.0.
ized for HA
ams togeth
sign time en
runs also o
gration servi
in HANA. An
e XS engin
d of invente
se the data
application
unlimited s
of HANA is
rvices are n
in this case,
time from M
agement, a
ds of things
ll within HA
new, with t
ge, and life
that is quite
ory around t
ich is the X
ironment
and ABAP,
can speak
whether it'
orite environ
NA. This is
native, we
ANA.
things that
23
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ness Suite,
NA. Lots of i
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vironment o
HANA. So
ices. Gatew
d the Applic
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as an effici
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functionalit
alability of
independe
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ozilla, I thin
thorization
hat you nee
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e HANA Cl
ycle manag
amazing. S
his is that w
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ith the HA
SQL, MDX,
a .NET, Cl
ment is the
native integ
have the lea
e have talk
t me chang
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nnovation th
le shared m
f ABAP that
the new HA
y. The Gat
ation servic
native appli
iency mech
ot become a
could run.
ANA, we d
t of the kind
already in
. So there's
managemen
d to manag
pretty amazi
oud platform
e your appli
basically,
e have three
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rated open.
st cost of o
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the color o
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ation servic
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of services
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JavaScript.
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user sessi
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ations,
hen you thi
categories
tor environ
form. And t
, OData,
, Heroku, Fo
ite their cod
nership that
en you look
this guy to
ent, CRM, a
by our appl
ed for HAN
atform is run
s are now in
amazing c
es inside H
and so a se
anymore, a
that are run
s means a l
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ns and me
entire abilit
nk about HA
of applicatio
ent. There i
ere is the o
rce.com, Py
e in, could i
you can run
at this prod
hite,
d so on.
ication
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ning on
side HANA.
pability of
NA. So the
arate tier
nd therefore
ing inside
nguage
App
ory and
to build,
NA, the
s that run
s what we
en,
thon, PHP,
tegrate
the entire
ct portfolio
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and its ev
is start to
on HANA
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started to
people ar
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down that
that's wh
showed u
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when you
this devic
everythin
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iteration
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process; t
realtime;
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that's wh
Our team
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, but it is inc
ur team did
look at wha
e running on
consumes t
list of most
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ases, the M
think about,
e, these des
was made
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nd replace t
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ch processe
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work on Ap
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well as in E
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24
g that we h
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explosion in
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verything a
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run somewh
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nufactures
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always for
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by realtime
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versions, p
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erience, wit
clipse. And
e developm
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with HAN
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ollaborate
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HANA.
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can we
n offline
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wimming in
t itself. And
ample.
amazing
the HANA
the team
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responsiv
realtime c
that we h
and the e
it is very
Every sin
eness, the a
ollaboration
ve to rethin
perience of
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bility to test
integrated
k software d
software de
rd.
we do is b
25
code inline
ith Jam, an
evelopment
velopment.
ing rethoug
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so forth. It'
itself
o when we
t and refact
just an extr
think about
ored on the
aordinary o
the opportu
HANA platfo
portunity
ity at SAP,
rm.
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UNIT 5: SA
00:00:00
00:00:24
00:00:35
00:00:48
00:00:58
00:01:06
00:01:15
00:01:23
00:01:33
00:01:45
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P HANA in
And one
Thomas
thinking
- let me sbeen one
is new cu
organizati
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world for
and doing
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where we
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things lik
a really in
Abdul's te
and using
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and deter
one parti
And ever day for s
With HAN
are the ki
In Health
Barbara
from the
research
on runnin
to hundre
to preven
armband
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monitor t
once som
patient m
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not possi
Practice an
rea that I'm
orf and the
itch this toof the most
tom applic
on with Abd
gned a very
hings like T
logistics an
culations, fo
can run thi
o after reall
that. We've
spirational p
am, as a pa
signals to fi
300 people
mining som
ular signal t
day, it costven months
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ds of extra
are, for exa
tortz in rethi
redictive st
orgs around
g the gene t
ds of times f
ive medicin
that people
ds of signal
eir own hea
ebody ends
nagement
an incredibl
le before.
d Summary
especially e
eam from A
black — theinspiring an
tions. And
ul's team, thi
large deal
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recasting of
gs tens of t
amazing pr
been doing
roject that T
rt of Abdul's
gure out the
, analysts, in
things of i
at went un
eBay betwe. And one p
ble to find t
rdinary achi
mple, we ha
nking the en
ff with geno
the world in
eatment thi
aster with H
, where we
wear,
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lth; to the ac
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ystem.
exciting ar
26
xcited about
bdul's organ
power of de most extra
ith the rece
is gives us a
ith CMA, on
and Asset
lations, and
utilization of
ousands of
oblems in b
a project wi
homas Torf
team, on ca
health of w
eBay who
pact to eBa
etected for
en 1.5 to 2.rson manu
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ve a great p
tire healthc
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NA.
can analyz
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tual reactiv
spital, the r
a, to open
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sign thinkinordinary asp
t merger of
tremendou
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stuff like thi
containers;
times faster.
nks, for exa
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and Priya an
lculating sig
at the CFO
ork on anal
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even mont
million dolllly found thi
al automati
at we can g
oject going
re experien
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at Stanford
ment, and v
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mple realtim
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conomy.
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dollars a
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h, dozens
es in; from
dies to
system, the
ere frankly
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In the oil i
explorato
They spe
analysis f
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incurred t
and stuff l
we have t
after thes
And this i
think abo
Think ab
question,
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amazing,
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entire por
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imaginati
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amazing
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thinking b
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solutions
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Portugal
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ndustry, for
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d hundreds
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e a drill gets
o remove th
ike this. So
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the rate of c
ur imaginati
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when we lo
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n that is ou
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pplications,
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, we have b
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is a ton of a
have a thri
around this,
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e deploym
elecom, an
cently start
ieve is the c
application
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.
example, wit
big oil comp
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stuck, there
drill,
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ond rethinki
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with HANA
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27
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f our future
x applicatio
deployed a
ation of oil,
ry year on d
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blems of ou
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le, and viabl
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and you c
that when
you have
it and the
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precalculon the fly.
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rain start
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k that with H
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ith our hand
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00:13:51
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the best i
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So all the
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for all the b
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