columnar data structure and analysis
TRANSCRIPT
-
8/18/2019 Columnar Data Structure and Analysis
1/3
Events Search/ Elastic Search DB [Data Model 1]
1. App Data
a. Code
b. Name
c. Platormd. !ersion
e. "pdate #o$
. #o$o
$. "%#s
&. "ser Data
a. 'd
b. Platorm()ni*)e(id +s)ch as email or $sheet,
c. Platorm +mobile- tablet- laptop/destop,
. Sheet Data
a. "ser id
b. "%#c. Name
d. Platorm()ni*)e(id
0. "sa$e
a. Appcode
b. "ser'd
c. sheet'D
d. Events S2Ns or comp)ted data- col)mnar inormation- vie3s and
selections
Mi4 Panel [Data Model &]
1. Appcode
&. Sheed'D
. "ser'D
0. Events
5. 'p6loc
7. Bro3ser
8. Platorm
9. 2thers
'deall: 3e 3ant to p)ll in as m)ch data as possible rom the elastics search db into
mi4 panel so 3e onl: )se
DataStore [Data Model ]
[;a
-
8/18/2019 Columnar Data Structure and Analysis
2/3
correlated(col)mn(r=[.7-6.5-.]
metrics=
orm)la
interpretation
)nit
directionmin- ma4- mean- etc
is(approved
oicial(name
description
domain
cate$or: +)nctional,
s)bcat 1-&
ta$s
?
-
8/18/2019 Columnar Data Structure and Analysis
3/3
o Dos or Anit
1. rite all events 3e can capt)re rom app and mi4panel on an e4cel
&. o thro)$h datamodel 1 and see ho3 it can be combine into mi4panel so 3e
onl: need to loo at mi4panel to st)d: )ser metrics
. Data model 10. Set)p Elastic Search
5. E4pose AP's
7. oo$le Anal:tics App 6F connect Mi4Panel and DataModel 1
8. et Mi4Panel dashboard )p and r)nnin$
Data Model ass
1. rite 3hat can 3e e4tract rom col)mn names in )ser data +Po3erB' G
$oo$leSheet,
&. Str)ct)re AP's/ scripts or p)llin$ c)stomer data and send to dataStoreHHH. Ii4 DataModel
0. Set)p Elastic Search on Data model
5. E4pose AP's
6. %ona 6F ActiveMeta