Hero Timeseries Historian We are living in the world of Data 2 - - PowerPoint PPT Presentation

▶
hero
SMART_READER_LITE
LIVE PREVIEW

Hero Timeseries Historian We are living in the world of Data 2 - - PowerPoint PPT Presentation

Oct 31, 2022 396 likes •585 views

Hero Timeseries Historian We are living in the world of Data 2 Without a systematic way to start and keep data clean, bad data will happen. Donato Diorio, Principal Consultant, DataZ 3 94 % Of companies experience severe data loss do

slide-1
SLIDE 1

Hero

Timeseries Historian

slide-2
SLIDE 2

We are living in the world

  • f Data
2
slide-3
SLIDE 3

Without a systematic way to start and keep data clean, bad data will happen.

— Donato Diorio, Principal Consultant, DataZ 3
slide-4
SLIDE 4

94 %

Of companies experience severe data loss do not recover

43%

Of these companies do not reopen again

51 %

Of these companies close within 2 years of the data loss 4 2014, Disaster recovery preparedness council
slide-5
SLIDE 5

5,000,000$

5
slide-6
SLIDE 6

HELLO!

We We are re team am Hero

We are here to provide the solution of a storage system for time series data. 6

do dotus us

slide-7
SLIDE 7

HERO

How does it work?

7
slide-8
SLIDE 8

Key feature

∎ Sc

Scalab abilit ility

∎ Availabi lability lity ∎ Write Throu
  • ughput
ghput / Fast st Persist istency ency ∎ Clou
  • ud-Inde
Independen ndence ce ∎ Ef

Efficien ent Storage ge Consum nsumptio tion

∎ High Read Query Perform rmance ance ∎ Better r User r Experience ence 8
slide-9
SLIDE 9 9 Collect lecting ing data
slide-10
SLIDE 10 10 Collect lecting ing data

1 second

slide-11
SLIDE 11 11

What t is happeni ening in a second? nd?

∎ Millions
  • ns of datapoints

ints

∎ Millise iseco cond d resolu
  • lution
  • n
∎ Teraby bytes es of data ∎ 67 67% % of data loss s is cause sed d by hardwa ware re or system em failure re ∎ 14 14% of data loss s is cause sed d by human an error
  • r
Scala lability lity Availa lability lity Write te Throug
  • ughput / Fast
st Pers rsist stenc ncy Cloud ud- Independ ndenc nce Effici cient nt Stora rage Consu sumptio mption High Read Query ry Perf rform
  • rmance
Better r User r Experi rience Collect lecting ing data
slide-12
SLIDE 12 12 Scala lability lity Availa lability lity Write te Throug
  • ughput / Fast
st Pers rsist stenc ncy Cloud ud- Independ ndenc nce Effici cient nt Stora rage Consu sumptio mption High Read Query ry Perf rform
  • rmance
Better r User r Experi rience Docker er based d conta taine ineri rizat zation
  • n
Contain taineriza rization
  • n

Why?

∎ Light weight ∎ Rapid deploy
  • ymen
ent
slide-13
SLIDE 13 13 Scala lability lity Availa lability lity Write te Throug
  • ughput / Fast
st Pers rsist stenc ncy Cloud ud- Independ ndenc nce Effici cient nt Stora rage Consu sumptio mption High Read Query ry Perf rform
  • rmance
Better r User r Experi rience Load-ba bala lance ce with HA Proxy Load bala lancing

Why?

∎ Zero-dow
  • wnti
time e maintena enance ∎ Event driven en archi hite tecture cture
slide-14
SLIDE 14 14 Scala lability lity Availa lability lity Write te Throug
  • ughput / Fast
st Pers rsist stenc ncy Cloud ud- Independ ndenc nce Effici cient nt Stora rage Consu sumptio mption High Read Query ry Perf rform
  • rmance
Better r User r Experi rience Intro roduce duce a right database ase to system em Database se

Why?

∎ Time-se series ries data platfor
  • rm
∎ High availab abil ility ∎ Data compres ression
  • n
slide-15
SLIDE 15 15 Scala lability lity Availa lability lity Write te Throug
  • ughput / Fast
st Pers rsist stenc ncy Cloud ud- Independ ndenc nce Effici cient nt Stora rage Consu sumptio mption High Read Query ry Perf rform
  • rmance
Better r User r Experi rience Read and visualize lize data Visual sualizatio zation

Why?

∎ Direct ct plugin in to Influ luxDB DB
slide-16
SLIDE 16 16

Architecture

slide-17
SLIDE 17 17

DEM O