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

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 not recover 51 % Of these companies close within 2 years of the data loss 43 % Of these companies do not reopen again 2014, Disaster recovery preparedness council 4

5,000,000$ 5

HELLO! We We are re team am Hero do dotus us We are here to provide the solution of a storage system for time series data. 6

HERO How does it work? 7

Key feature ∎ Sc Scalab abilit ility ∎ Availabi lability lity ∎ Write Throu oughput ghput / Fast st Persist istency ency ∎ Clou oud-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

Collect lecting ing data 9

Collect lecting ing data 1 second 10

Scala lability lity Availa lability lity Write te Throug oughput / 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 ormance Better r User r Experi rience Collect lecting ing data What t is happeni ening in a second? nd? ∎ Millions ons of datapoints ints ∎ Millise iseco cond d resolu olution on ∎ 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 or 11

Scala lability lity Availa lability lity Write te Throug oughput / 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 ormance Better r User r Experi rience Docker er based d conta taine ineri rizat zation on Contain taineriza rization on Why? ∎ Light weight ∎ Rapid deploy oymen ent 12

Scala lability lity Availa lability lity Write te Throug oughput / 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 ormance Better r User r Experi rience Load-ba bala lance ce with HA Proxy Load bala lancing Why? ∎ Zero-dow ownti time e maintena enance ∎ Event driven en archi hite tecture cture 13

Scala lability lity Availa lability lity Write te Throug oughput / 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 ormance 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 orm ∎ High availab abil ility ∎ Data compres ression on 14

Scala lability lity Availa lability lity Write te Throug oughput / 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 ormance Better r User r Experi rience Read and visualize lize data Why? Visual sualizatio zation ∎ Direct ct plugin in to Influ luxDB DB 15

Architecture 16

DEM O 17