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Eric Kavanagh: Mine damer og herrer, det er tid til at blive klog! Det er tid til TechWise, et helt nyt show! Jeg hedder Eric Kavanagh. Jeg bliver din moderator for vores indledende episode af TechWise. Det er nøjagtigt rigtigt. Dette er et partnerskab mellem Techopedia og Bloor Group, naturligvis, inden for Analyse-berømmelse.
Jeg hedder Eric Kavanagh. Jeg vil moderere denne virkelig interessante og involverede begivenhed, folkens. Vi vil grave dybt ned i væven for at forstå, hvad der foregår med denne store ting, der hedder Hadoop. Hvad er elefanten i rummet? Det hedder Hadoop. Vi vil prøve at finde ud af, hvad det betyder, og hvad der sker med det.
Først og fremmest en stor tak til vores sponsorer, GridGain, Actian, Zettaset og DataTorrent. Vi får et par få ord fra hver af dem i slutningen af denne begivenhed. Vi har også et spørgsmål og spørgsmål, så vær ikke sky - send dine spørgsmål til enhver tid.
Vi graver ned i detaljerne og kaster de hårde spørgsmål til vores eksperter. Og når vi taler om eksperterne, hej, der er de. Så vi hører fra vores helt egen Dr. Robin Bloor, og folk, jeg er meget spændt over at have den legendariske Ray Wang, hovedanalytiker og grundlægger af Constellation Research. Han er online i dag for at give os sine tanker, og han er ligesom Robin, at han er utroligt mangfoldig og virkelig fokuserer på en masse forskellige områder og har evnen til at syntetisere dem og virkelig forstå, hvad der foregår derude i hele dette felt af informationsteknologi og datastyring.
Så der er den lille søde elefant. Han er i begyndelsen af vejen, som du kan se. Det begynder lige nu, det er bare en slags start, hele denne Hadoop-ting. Naturligvis, tilbage i 2006 eller 2007, formoder jeg, det var, da det blev frigivet til open source-samfundet, men der har været en masse ting, der sker, folkens. Der har været en enorm udvikling. Faktisk vil jeg bringe historien op, så jeg vil gøre en hurtig desktop-andel, i det mindste tror jeg, at jeg er det. Lad os gøre en hurtig desktop-deling.
Jeg viser dig dette bare skøre, skøre historie folk. Så Intel investerede 740 millioner dollars for at købe 18 procent af Cloudera. Jeg tænkte, og jeg kan lide "Holy Christmas!" Jeg begyndte at lave matematik, og det er som "Det er en værdiansættelse på $ 4, 1 milliarder." Lad os tænke over dette et øjeblik. Jeg mener, hvis WhatsApp er værd $ 2 milliarder, antager jeg, at Cloudera lige så godt kan være værd $ 4, 1 milliarder, ikke? Jeg mener, hvorfor ikke? Nogle af disse numre er lige ud af vinduet i disse dage, folkens. Jeg mener, typisk hvad angår investering, har du EBITDA og alle disse andre forskellige mekanismer, multipla af indtægter og så videre. Nå, det vil være et ørken af et antal indtægter at komme til $ 4, 1 milliarder for Cloudera, som er en fantastisk virksomhed. Misforstå mig ikke - der er nogle meget, meget smarte mennesker derovre inklusive den fyr, der startede hele Hadoop-dille, Doug Cutting, han er derovre - en masse meget intelligente mennesker, der gør en masse virkelig, virkelig seje ting, men bunden er, at $ 4, 1 milliarder, det er en masse penge.
Så her er et slags indfanget indlysende øjeblik at gå gennem mit hoved lige nu, som er en chip, Intel. Deres chipdesignere bringer for at se nogle Hadoop-optimerede chip - jeg må tro det, folkens. Det er bare mit gæt. Det er bare et rygte, der kommer fra mig, hvis du vil, men det giver en slags mening. Og hvad betyder alt dette?
Så her er min teori. Hvad sker der? Meget af dette er ikke nyt. Massiv parallelbehandling er ikke meget nyt. Parallel behandling er sikker ikke ny. Jeg har været i supercomputers verden i et stykke tid. Mange af disse ting, der sker, er ikke nye, men der er en slags generel bevidsthed om, at der er en ny måde at angribe nogle af disse problemer på. Hvad jeg ser ske, hvis du ser på nogle af de store leverandører af Cloudera eller Hortonworks og nogle af disse andre fyre, hvad de laver virkelig, hvis du koger det ned til det mest kornede destillerede niveau er applikationsudvikling. Det er hvad de laver.
De designer nye applikationer - nogle af dem involverer forretningsanalyse; nogle af dem involverer bare overladningssystemer. En af vores leverandører, der har talt om det, de laver den slags ting hele dagen på showet i dag. Men hvis det er frygteligt nyt, er svaret igen "ikke rigtigt", men der er store ting, der sker, og personligt tror jeg, hvad der sker med Intel, der foretager denne enorme investering, er et markedsskabende træk. De ser på verden i dag og ser, at det er en slags monopolverden i dag. Der er Facebook, og de har slået snørken ud af dårlige MySpace. LinkedIn har slået snørken ud af den fattige Who's Who. Så du ser dig omkring, og det er en tjeneste, der dominerer alle disse forskellige rum i vores verden i dag, og jeg tror, ideen er, at Intel vil kaste alle deres chips på Cloudera og prøve at løfte det til toppen af stakken - det er bare min teori.
Så folk, som jeg sagde, vil vi have en lang Q & A-session, så vær ikke sky. Send dine spørgsmål til enhver tid. Du kan gøre det ved hjælp af denne spørgsmål og svar på din webcast-konsol. Og med det vil jeg komme til vores indhold, fordi vi har mange ting at komme igennem.
Så Robin Bloor, lad mig give nøglerne til dig, og gulvet er dit.
Robin Bloor: OK, Eric, tak for det. Lad os tage de dansende elefanter på. Det er faktisk en underlig ting, at elefanter er de eneste landpattedyr, der faktisk ikke kan hoppe. Alle disse elefanter i denne særlige grafik har mindst en fod på jorden, så jeg formoder, at det er muligt, men til en vis grad er disse åbenlyse Hadoop-elefanter, så meget, meget dygtige.
Spørgsmålet, som jeg tror, skal diskuteres og skal drøftes i al ærlighed. Det skal diskuteres, før du rejser andre steder, som virkelig begynder at tale om, hvad Hadoop faktisk er.
En af de ting, det absolut er fra man-play-basis, er butikken med nøgleværdier. Vi plejede at have butikker med nøgleværdier. Vi plejede at have dem på IBM mainframe. Vi havde dem på minicomputere; DEC VAX havde IMS-filer. Der var ISAM-kapaciteter, der var på stort set hver minicomputer, du kan få dine hænder på. Men engang omkring slutningen af 80'erne kom Unix ind, og Unix havde faktisk ikke nogen nøgleværdi butik på det. Da Unix udviklede det, udviklede de sig meget hurtigt. Hvad der skete virkelig var, at databaseleverandørerne, især Oracle, gik dampende ind der, og de solgte dine databaser for at passe alle data, du har lyst til at administrere på Unix. Windows og Linux viste sig at være det samme. Så industrien gik den bedste del af 20 år uden en almindelig butik med nøgleværdier. Nå, det er tilbage nu. Det er ikke kun tilbage, det er skalerbart.
Nu tror jeg virkelig, at det er grundlaget for, hvad Hadoop virkelig er, og i en vis grad bestemmer det, hvor det skal hen. Hvad kan vi lide ved butikker med nøgleværdier? De af jer, der er så gamle som jeg, og som faktisk husker at have arbejdet med butikker med nøgleværdier, er klar over, at du stort set kunne bruge dem til uformelt at oprette en database, men kun uformelt. Du kender, at metadataene hurtigt gemmer værdier i programkoden, men du kan faktisk oprette den til en ekstern fil, og du kunne, hvis du ville begynde at behandle en nøgleværdielager lidt som en database. Men selvfølgelig havde den ikke al den gendannelsesevne, som en database har, og den havde ikke en frygtelig masse ting, databaserne nu har fået, men det var en rigtig nyttig funktion for udviklere, og det er en af grundene til, at jeg tror at Hadoop har vist sig så populært - simpelthen fordi det har været kodere, programmerere, udviklere, der er hurtige til. De indså, at ikke kun er en nøgleværdi af butikken, men det er en skala ud nøgleværdi butik. Det skalerer stort set på ubestemt tid. Jeg sendte disse skalaer ud i tusinder af servere, så det er den rigtig store ting ved Hadoop, det er, hvad det er.
Den har også oven på det MapReduce, som er en paralleliseringsalgoritme, men det er faktisk efter min mening ikke vigtigt. Så du ved, Hadoop er en kamæleon. Det er ikke kun et filsystem. Jeg har set forskellige slags krav fremsat for Hadoop: det er en hemmelig database; det er ingen hemmelig database; det er en fælles butik; det er en analytisk værktøjskasse; det er et ELT-miljø; det er datarensningsværktøj; Det er et datalager med streamingplatforme; det er et arkivlager; det er en kur mod kræft, og så videre. De fleste af disse ting er virkelig ikke sandt for vanilje Hadoop. Hadoop er sandsynligvis en prototype - det er bestemt et prototypemiljø for en SQL-database, men den har ikke rigtig, hvis du lægger aldersrum med alderskatalog over Hadoop, har du noget, der ligner en database, men det er ikke rigtig hvad nogen kalder en database med hensyn til kapacitet. En masse af disse muligheder, du kan helt sikkert få dem på Hadoop. Der er bestemt mange af dem. Faktisk kan du få en kilde til Hadoop, men Hadoop i sig selv er ikke det, jeg vil kalde operationelt hærdet, og derfor er aftalen om Hadoop, virkelig ikke ville jeg være på noget andet, at du slags har brug for at have en tredje -partsprodukter for at forbedre det.
Så hvis du taler om dig, kan du kun kaste et par linjer, når jeg taler Hadoop overreach. Først og fremmest, forespørgsel i realtid, og du ved, at realtid er en slags forretningstid, virkelig næsten altid ydelseskritisk ellers. Jeg mener, hvorfor skulle du konstruere i realtid? Hadoop gør ikke rigtig dette. Det gør noget, der er nær realtid, men det gør ikke rigtig realtids ting. Det gør streaming, men det gør ikke streaming på en måde, jeg vil kalde virkelig missionskritisk type applikations-streaming platforme kan gøre. Der er forskel mellem en database og en spaltelig butik. Synkronisering af det til over Hadoop giver dig en spaltelig datalager. Det er ligesom en database, men det er ikke det samme som en database. Hadoop i sin oprindelige form kvalificerer efter min mening ikke rigtig som en database overhovedet, fordi det mangler ganske mange ting, en database skal have. Hadoop gør meget, men det gør det ikke særlig godt. Igen, kapaciteten er der, men vi er en måder væk fra faktisk at have en hurtig kapacitet i alle disse områder.
Den anden ting at forstå om Hadoop er, at det er lidt kommet langt siden det blev udviklet. Det blev udviklet i de tidlige dage; det blev udviklet, da vi havde servere, der faktisk kun havde en processor pr. server. Vi havde aldrig multi-core processorer, og det var bygget til at køre over gitter, lanceringsnet og skiver. Et af Hadoops designmål var aldrig at miste arbejdet. Og det handlede virkelig om diskfejl, for hvis du har fået hundredvis af servere, så er sandsynligheden for, at hvis du har diske på serverne, er sandsynligheden for, at du får en oppetidstilgængelighed på noget som 99.8. Det betyder, at du i gennemsnit får en fiasko på en af disse servere en gang hver 300 eller 350 dage, en dag om året. Så hvis du havde hundredvis af disse, ville sandsynligheden være på en hvilken som helst dag i året, at du får en serverfejl.
Hadoop blev bygget specifikt til at løse dette problem - så i tilfælde af, at noget mislykkedes, tager det snapshots af alt, hvad der foregår, på hver enkelt server, og det kan gendanne det batchjob, der kører. Og det var alt, hvad der nogensinde løb på Hadoop, var batchjob, og det er en virkelig nyttig evne, skal det siges. Nogle af de batchjobs, der blev kørt - især hos Yahoo, hvor jeg tror Hadoop var slags født - ville køre i to eller tre dage, og hvis det mislykkedes efter en dag, ville du virkelig ikke miste arbejdet det var gjort. Så det var designpunktet bag tilgængeligheden på Hadoop. Du ville ikke kalde den høje tilgængelighed, men du kan kalde det høj tilgængelighed for serielle batchjob. Det er sandsynligvis måden at se på det. Høj tilgængelighed konfigureres altid i henhold til arbejdslinjekarakteristika. I øjeblikket kan Hadoop kun konfigureres til virkelig serielle batchjobs med hensyn til den slags gendannelse. Enterprise høj tilgængelighed er sandsynligvis bedst tænkt med hensyn til transaktionelle LLP. Jeg tror, at hvis du ikke ser på det som en ting i realtid, gør Hadoop det ikke endnu. Det er sandsynligvis langt fra at gøre det.
Men her er den smukke ting ved Hadoop. Denne grafik i højre side, der har en liste over leverandører rundt om kanten, og alle linjerne på det, viser forbindelser mellem disse leverandører og andre produkter i Hadoop-økosystemet. Hvis du ser på det, er det et utroligt imponerende økosystem. Det er ganske bemærkelsesværdigt. Vi taler selvfølgelig med mange leverandører med hensyn til deres muligheder. Blandt de leverandører, jeg har talt med, er der nogle virkelig ekstraordinære muligheder for at bruge Hadoop og in-memory, måde at bruge Hadoop som et komprimeret arkiv, at bruge Hadoop som et ETL-miljø, og så videre og så videre. Men hvis du tilføjer produktet til Hadoop selv, fungerer det meget godt i et bestemt rum. Så mens jeg er kritisk over for indfødte Hadoop, er jeg ikke kritisk over for Hadoop, når du faktisk tilføjer noget magt til det. Efter min mening garanterer Hadoops popularitet en slags fremtid. Med det mener jeg, selvom hver linje med kode, der er skrevet indtil videre på Hadoop, forsvinder, tror jeg ikke, at HDFS API vil forsvinde. Med andre ord, jeg tror, at filsystemet, API, er her for at blive, og muligvis YARN, den planlægning, der ser over det.
Når du faktisk ser på det, er det en meget vigtig kapacitet, og jeg vil slags voks med det om et øjeblik, men den anden ting, der er, lad os sige, spændende mennesker om Hadoop er hele open source-billedet. Så det er værd at gennemgå, hvad open source-billedet er med hensyn til hvad jeg betragter som reel kapacitet. Mens Hadoop og alle dets komponenter bestemt kan gøre, hvad vi kalder datalængder - eller som jeg foretrækker at kalde det, et datareservoir - er det bestemt et meget godt iscenesættelsesområde at slippe data ind i organisationen eller indsamle data i organisationen - ekstremt godt til sandkasser og til vinkeldata. Det er meget godt som en prototype udviklingsplatform, som du muligvis implementerer i slutningen af dagen, men du ved som et udviklingsmiljø stort set alt hvad du ønsker er der. Som arkivbutik har det stort set alt hvad du har brug for, og det er selvfølgelig ikke dyrt. Jeg synes ikke, vi skal skille nogen af disse to ting fra Hadoop, selvom de ikke formelt, hvis du vil, komponenter af Hadoop. Onlinekilen har bragt en enorm mængde analytics ind i open source-verdenen, og en masse af denne analyse køres nu på Hadoop, fordi det giver dig et praktisk miljø, hvor du faktisk kan tage en masse eksterne data og bare begynde at spille ved en analytisk sandkasse.
Og så har du open source-mulighederne, som begge er maskinlæring. Begge disse er ekstremt magtfulde i den forstand, at de implementerer kraftige analytiske algoritmer. Hvis du sætter disse ting sammen, har du kerne med en meget, meget vigtig kapacitet, som det på en eller anden måde meget sandsynligt er - uanset om det udvikler sig på egen hånd eller om leverandører kommer ind for at udfylde de manglende brikker - Det er meget sandsynligt, at det fortsætter i lang tid, og bestemt tror jeg, at maskinindlæringen allerede har en meget stor indflydelse på verden.
Udviklingen af Hadoop, YARN ændrede alt. Hvad der var sket var MapReduce temmelig meget svejset til det tidlige filsystem HDFS. Da YARN blev introduceret, skabte det en planlægningsmulighed i sin første udgivelse. Du forventer ikke den ekstremt sofistikerede planlægning fra første udgivelse, men det betød, at det nu ikke længere nødvendigvis var et patch-miljø. Det var et miljø, hvor flere job kunne planlægges. Så snart det skete, var der en hel række leverandører, der havde holdt sig væk fra Hadoop - de kom lige ind og tilsluttede det, for så kunne de bare se på det som planlægningsmiljøet over et filsystem, og de kunne adressere ting til det. Der er endda databaseleverandører, der har implementeret deres databaser på HDFS, fordi de bare tager motoren og bare lægger den over på HDFS. Med kaskadearbejde og med YARN bliver det et meget interessant miljø, fordi du kan oprette komplekse arbejdsgange over HDFS, og det betyder virkelig, at du kan begynde at tænke på det som virkelig en platform, der kan køre flere job samtidigt og skubbe sig selv mod punktet om gør missionskritiske ting. Hvis du vil gøre det, bliver du sandsynligvis nødt til at købe nogle tredjepartskomponenter som sikkerhed osv., Som Hadoop faktisk ikke har en revisionskonto til at udfylde hullerne, men du komme ind på det punkt, hvor du selv med indbygget open source kan gøre nogle interessante ting.
Med hensyn til, hvor jeg tror Hadoop faktisk vil hen, tror jeg personligt, at HDFS vil blive et standard-skala-out-filsystem og derfor vil blive OS, operativsystemet, til nettet til dataflyt. Jeg tror, det har en enorm fremtid i det, og jeg tror ikke, det vil stoppe der. Og jeg tror faktisk, at økosystemet bare hjælper, fordi stort set alle, alle leverandører i rummet, faktisk integrerer Hadoop på en eller anden måde, og de aktiverer det bare. Hvad angår et andet punkt, der er værd at gøre, hvad angår Hadoop-overskud, er det ikke en særlig god platform plus paralleliseringen. Hvis du rent faktisk ser på, hvad det laver, er det, hvad det rent faktisk gør, at tage et øjebliksbillede regelmæssigt på hver server, da det udfører sine MapReduce-job. Hvis du skulle designe til virkelig hurtig parallelisering, ville du ikke gøre noget lignende. Faktisk ville du sandsynligvis ikke bruge MapReduce alene. MapReduce er kun det, jeg vil sige halvt i stand til parallelisme.
Der er to tilgange til parallelisme: den ene er ved pipelining-processer, og den anden er ved at dele data MapReduce, og det gør delingen af data, så der er mange job, hvor MapReduce faktisk ikke ville være den hurtigste måde at gøre det på, men det vil giver dig parallelitet, og der er ingen fjernelse fra det. Når du har en masse data, er den slags magt normalt ikke så nyttig. Garn, som jeg allerede har sagt, er en meget ung planlægningsmulighed.
Hadoop er, slags at tegne stregen i sandet her, Hadoop er ikke et datavarehus. Det er så langt fra at være et datavarehus, at det næsten er et absurd forslag at sige, at det er. I dette diagram er det, jeg viser øverst, en slags dataflyt, der går fra et Hadoop-datareservoir til et gigantisk udskiftningsdatabase, hvilket er, hvad vi rent faktisk vil gøre, et virksomhedsdatarager. Jeg viser ældre databaser, indlæser data i datavarehuset og offload-aktivitet, skaber offload-databaser fra datavarehuset, men det er faktisk et billede, som jeg begynder at se dukke op, og jeg vil sige, at dette er som den første generation af hvad der sker med datalageret med Hadoop. Men hvis du selv ser på datavarehuset, er du klar over, at under datalageret har du en optimizer. Du har distribueret forespørgselsarbejdere over meget mange processer, der sidder over måske meget mange store diske. Det er hvad der sker i et datavarehus. Det er faktisk en slags arkitektur, der er bygget til et datavarehus, og det tager lang tid at bygge noget lignende, og Hadoop har overhovedet ikke noget af det. Så Hadoop er ikke et datalager, og det vil efter min mening ikke blive et hvilket som helst tidspunkt snart.
Det har dette relative datareservoir, og det ser slags interessant ud, hvis du bare ser på verden som en række begivenheder, der strømmer ind i organisationen. Det er det, jeg viser på venstre side af dette diagram. At have det gå gennem en filtrering og routing kapacitet, og de ting, der skal til for streaming bliver forsvundet fra streaming-apps, og alt andet går lige ind i datareservoiret, hvor det er klargjort og renset, og derefter sendt af ETL til enten en enkelt data lager eller et logisk datalager bestående af flere motorer. Dette er efter min mening en naturlig udviklingslinje for Hadoop.
Med hensyn til ETW er en af de ting, der er værd at påpege, at datalageret faktisk blev flyttet - det er ikke, hvad det var. Bestemt, i dag, forventer du, at der er en hierarkisk kapacitet pr. Hierarkiske data om, hvad folk eller nogle mennesker kalder dokumenterne i datavarehuset. Det er JSON. Netværksforespørgsler, der er grafiske databaser, muligvis analyse. Så det, vi bevæger os mod, er en ETW, der faktisk har en mere kompleks arbejdsbyrde end dem, vi er vant til. Så det er lidt interessant, fordi det på en måde betyder, at datavarehuset bliver endnu mere sofistikeret, og på grund af det vil det være endnu længere tid, før Hadoop kommer et sted tæt på det. Betydningen af datavarehus udvides, men det inkluderer stadig optimering. Du skal have en optimeringsfunktion, ikke bare over forespørgsler nu, men over alle disse aktiviteter.
Det er det virkelig. Det var alt, hvad jeg ville sige om Hadoop. Jeg tror, jeg kan give Ray, som ikke har nogen lysbilleder, men han er altid god til at tale.
Eric Kavanagh: Jeg tager lysbillederne. Der er vores ven, Ray Wang. Så Ray, hvad er dine tanker om alt dette?
Ray Wang: Nu tror jeg, det var sandsynligvis en af de mest kortfattede og store historier i butikker med nøgleværdier, og hvor Hadoop er gået i forhold til de virksomheder, der er ude, så jeg lærer altid meget, når jeg lytter til Robin.
Faktisk har jeg et lysbillede. Jeg kan dukke op et lysbillede her.
Eric Kavanagh: Bare gå videre og klik på, klik på start og gå for at dele dit skrivebord.
Ray Wang: Okay, der går du. Jeg deler faktisk. Du kan se selve appen. Lad os se, hvordan det går.
Alt dette taler om Hadoop, og så går vi dybt ind i samtalen om de teknologier, der er der, og hvor Hadoop er på vej, og mange gange vil jeg bare gerne tage det op igen for virkelig at have forretningsdiskussion. Meget af det, der sker på teknologisiden, er virkelig dette stykke, hvor vi har talt om datalager, informationsstyring, datakvalitet, mestring af disse data, og så vi har en tendens til at se dette. Så hvis du ser på denne graf her helt nede, er det meget interessant, at de typer individer, vi støder på, taler om Hadoop. Vi har de teknologer og datavidenskabsmændene, der søger ude og har masser af spænding, og det handler typisk om datakilder, ikke? Hvordan mestrer vi datakilderne? Hvordan får vi dette til de rigtige kvalitetsniveauer? Hvad gør vi ved styringen? Hvad kan vi gøre for at matche forskellige typer kilder? Hvordan holder vi afstamning? Og al den slags diskussion. Og hvordan får vi mere SQL ud af vores Hadoop? Så den del sker på dette niveau.
På informations- og orkestrationssiden er det her, hvor det bliver interessant. Vi begynder at binde output fra denne indsigt, som vi får, eller trækker vi den tilbage til forretningsprocesser? Hvordan binder vi det tilbage til nogen form for metadatamodeller? Forbinder vi prikkerne mellem objekter? Og så de nye verb og diskussioner om, hvordan vi bruger disse data, bevæger os fra det, vi traditionelt er i en verden af CRUD: oprette, læse, opdatere, slette, til en verden, der diskuterer, hvordan vi engagerer eller deler eller samarbejder eller kan lide eller trække noget.
Det er her vi begynder at se en masse af spændingen og innovationen, især om, hvordan man trækker denne information og bringer den til værdi. Det er den teknologidrevne diskussion under den røde linje. Over den røde linje får vi de helt spørgsmål, som vi altid har ønsket at stille, og et af dem, som vi altid bringer op, er som f.eks. Spørgsmålet i detailhandlen for dig er som "Hvorfor sælger røde trøjer bedre i Alabama end blå trøjer i Michigan? " Du kunne tænke over det og sige, "Det er lidt interessant." Du ser det mønster. Vi stiller det spørgsmål, og vi spekulerer på, "Hej, hvad laver vi?" Det handler måske om statslige skoler - Michigan versus Alabama. OK, jeg får dette, jeg kan se, hvor vi skal hen. Og så begynder vi at få den forretningsmæssige side af huset, folk i finans, mennesker, der har traditionelle BI-kapaciteter, folk i marketing og folk i HR, der siger: "Hvor er mine mønstre?" Hvordan kommer vi til disse mønstre? Og så ser vi en anden måde til innovation på Hadoop-siden. Det handler virkelig om, hvordan vi overflader opdaterer indsigt hurtigere. Hvordan opretter vi denne slags forbindelser? Det går hele vejen til de mennesker, der klarer sig, annonce: tech, der dybest set forsøger at forbinde annoncer og relevant indhold fra alt fra realtidsbudnetværk til kontekstuelle annoncer og placering af annoncer og gøre det på farten.
Så det er interessant at. Du ser udviklingen af Hadoop fra, "Hej, her er teknologiløsningen. Her er hvad vi skal gøre for at få disse oplysninger ud til folk." Når det derefter krydser forretningsområdet, er det her det bliver interessant. Det er indsigten. Hvor er forestillingen? Hvor er fradraget? Hvordan forudsiger vi ting? Hvordan får vi indflydelse? Og så bring det til det sidste niveau, hvor vi faktisk ser et andet sæt Hadoop-innovationer, der sker omkring beslutningssystemer og handlinger. Hvad er den næste bedste handling? Så du ved, at blå trøjer sælger bedre i Michigan. Du sidder på et ton blå trøjer i Alabama. Den åbenlyse ting er, "Ja, lad os få dette sendt derude." Hvordan gør vi det? Hvad er det næste trin? Hvordan binder vi det tilbage? Måske er den næste bedste handling, måske er det et forslag, måske er det noget, der hjælper dig med at forhindre et problem, måske er det heller ingen handling, som er en handling i sig selv. Så vi begynder at se denne type mønstre dukke op. Og det smukke ved dette tilbage til det, du siger om butikker med nøgleværdier, Robin, er, at det sker så hurtigt. Det sker på den måde, at vi ikke har tænkt på det på denne måde.
Sandsynligvis vil jeg sige i de sidste fem år, vi hentede. Vi begyndte at tænke på, hvordan vi kan udnytte forretninger med nøgleværdier igen, men det er bare i de sidste fem år, folk ser på dette meget anderledes, og det er som om teknologicyklusser gentager sig selv i 40-årige mønstre, så dette er venligt af en sjov ting, hvor vi ser på sky, og jeg er ligesom mainframe-deling af tid. Vi ser på Hadoop og kan godt lide butik med nøgleværdier - måske er det et datamart, mindre end et datavarehus - og så begynder vi at se disse mønstre igen. Det, jeg prøver at gøre lige nu, er at tænke over, hvad folk gjorde for 40 år siden? Hvilke tilgange og teknikker og metoder blev anvendt, der var begrænset af de teknologier, folk havde? Det er slags, der driver denne tankeproces. Så når vi gennemgår det større billede af Hadoop som et værktøj, når vi går tilbage og tænker på de forretningsmæssige implikationer, er dette slags den sti, som vi normalt fører folk igennem, så du kan se, hvilke stykker, hvilke dele der er i dataene beslutningsvej. Det var bare noget, jeg ville dele. Det er en slags tankegang, som vi har brugt internt og forhåbentlig tilføjer til diskussionen. Så jeg vender det tilbage til dig, Eric.
Eric Kavanagh: Det er fantastisk. Hvis du kan holde dig omkring for nogle spørgsmål og spørgsmål. Men jeg kunne godt lide, at du tog det tilbage til forretningsniveauet, fordi det i slutningen af dagen handler om forretningen. Det handler om at få tingene gjort og sikre dig, at du bruger penge klogt, og det er et af de spørgsmål, jeg allerede har set, så højttalere måske vil tænke over, hvad er TCL ved at gå Hadoop-ruten. Der er nogle søde pletter imellem, for eksempel at bruge kontorhyldeværktøjer til at gøre tingene på en eller anden traditionel måde og bruge de nye sæt værktøjer, for igen, tænk over det, en masse af disse ting er ikke nyt, det er bare slags sammenkæling på en ny måde er, antager jeg, den bedste måde at sige det på.
Så lad os gå foran og introducere vores ven, Nikita Ivanov. Han er grundlægger og CEO af GridGain. Nikita, jeg vil gå foran og overlevere nøglerne til dig, og jeg tror, du er derude. Kan du høre mig Nikita?
Nikita Ivanov: Ja, jeg er her.
Eric Kavanagh: Fremragende. Så ordet er dit. Klik på det lysbillede. Brug pil ned, og tag den væk. Fem minutter.
Nikita Ivanov: Hvilket lysbillede klikker jeg på?
Eric Kavanagh: Klik bare hvor som helst på dias, og så bruger du pil ned på tastaturet til at flytte. Klik bare på selve diaset og brug pilen ned.
Nikita Ivanov: Okay, så bare et par hurtige lysbilleder om GridGain. Hvad gør vi i forbindelse med denne samtale? GridGain producerer dybest set en software til hukommelse i computeren, og en del af den platform, vi har udviklet, er Hadoop-accelerator i hukommelsen. Med hensyn til Hadoop har vi en tendens til at tænke på os selv som Hadoop-performance-specialister. Hvad vi gør, i det væsentlige oven på vores centrale in-memory computing-platform, der består af teknologier som datanet, hukommelsesstrømning og beregningsnet, ville være i stand til at plug-and-play Hadoop-accelerator. Det er meget enkelt. Det ville være rart, hvis vi kan udvikle en slags plug-and-play-løsning, der kan installeres lige i Hadoop-installationen. Hvis du, udvikleren af MapReduce, har brug for et løft uden behov for at skrive nogen ny software eller ændring af kode eller ændring, eller har dybest set en helt minimal konfigurationsændring i Hadoop-klyngen. Det er hvad vi udviklede.
Grundlæggende er Hadoop-acceleratoren i hukommelsen baseret på at optimere to komponenter i Hadoop-økosystemet. Hvis du tænker på Hadoop, er det overvejende baseret på HDFS, som er filsystemet. MapReduce, som er rammen for at køre konkurrencerne parallelt oven på filsystemet. For at optimere Hadoop optimerer vi begge disse systemer. Vi har udviklet et hukommelsesfilsystem, der er fuldstændigt kompatibelt, 100% kompatibelt plug-and-play, med HDFS. Du kan køre i stedet for HDFS, du kan køre på toppen af HDFS. Og vi har også udviklet MapReduce i hukommelsen, der er plug-and-play-kompatibel med Hadoop MapReduce, men der er mange optimeringer af, hvordan arbejdsgangen til MapReduce og hvordan tidsplanen for MapReduce fungerer.
Hvis du for eksempel ser på dette lysbillede, hvor vi viser typen af duplikatstabel. På venstre side har du dit typiske operativsystem med GDM, og oven på dette diagram har du applikationscentret. I midten har du Hadoop. Og Hadoop er igen baseret på HDFS og MapReduce. Så dette repræsenterer på dette diagram, at hvad er hvad vi slags indlejrer i Hadoop-stakken. Igen er det plug-and-play; du behøver ikke at ændre nogen kode. Det fungerer bare på samme måde. På det næste lysbillede viste vi i det væsentlige, hvordan vi optimerede MapReduce-arbejdsgangen. Det er sandsynligvis den mest interessante del, fordi det giver dig den mest fordel, når du kører MapReduce-job.
Den typiske MapReduce, når du sender jobbet, og til venstre er der diagram, der er sædvanlig anvendelse. Så typisk indsender du jobbet, og jobbet går til en job tracker. It interacts with the Hadoop name node and the name node is actually the piece of software that manages the interaction with the digital files, and kind of keeps the directory of files and then the job tracker interacts with the task tracker on each individual node and the task tracker interacts with a Hadoop data node to get data from. So that's basically a very kind of high-level overview of how your MapReduce job gets in the computers. As you can see what we do with our in-memory, Hadoop MapReduce will already completely bypass all this complex scheduling that takes a lot of time off your execution and go directly from client to GridGain data node and GridGain data node keeps all that e-memory for a blatantly fast, fast execution.
So all in all basically, we allow it to get anywhere from 5x up all the way to 100x performance increase on certain types of loads, especially for short leaf payloads where you literally measure every second. We can give you a dramatic boost in performance with literally no core change.
Alright, that's all for me.
Eric Kavanagh: Yes, stick around for the Q&A. No doubt about it.
Let me hand it off to John Santaferraro. John, just click on that slide. Use the down arrow to move on.
John Santaferraro: Alright. Thanks a lot, Eric.
My perspective and Actian's perspective really is that Hadoop is really about creating value and so this is an example from digital media. A lot of the data that is pumping into Hadoop right now has to do with digital media, digital marketing, and customer, so there is great opportunity - 226 billion dollars of retail purchases will be made online next year. Big data and Hadoop is about capturing new data to give you insight to get your share of that. How do you drive 14% higher marketing return and profits based on figuring out the right medium X and the right channels and the right digital marketing plan? How do you improve overall return on marketing investment? By the way, in 2017, what we ought to be thinking about when we look at Hadoop is the fact that CMO, chief marketing officer, spending in 2017 will outpace that of IT spending, and so it really is about driving value. Our view is that there are all kinds of noise being made on the left-hand side of this diagram, the data pouring into Hadoop.
Ultimately, our customers are wanting to create customer delight, competitive advantage, world-class risk management, disruptive new business models, and to do all of that to deliver transformational value. They are looking to capture all of this data in Hadoop and be able to do best-in-class kinds of things like discovery on that data without any limitations, no latency at any scale of the data that lives in there - moving from reactive to predictive kinds of analytics and doing everything dynamically instead of looking at data just as static. What pours into Hadoop? How do you analyze it when it arrives? Where do you put it to get the high-performance analytics? And ultimately moving everything down to a segment of one.
So what we've done at Actian in the Actian Analytics Platform, we have built an exoskeleton around Hadoop to give it all of these capabilities that you need so you are able to connect to any data source bringing it into Hadoop, delivering it as a data service wherever you need it. We have libraries of analytics and data blending and data enrichment kinds of operators that you literally drag and drop them so that you can build out these data and analytic workflows, and without ever doing any programming, we will push that workload via YARN right down to the Hadoop nodes so you can do high-performance data science natively on Hadoop. So all of your data prep, all of your data science happening on Hadoop highly parallelized, highly optimized, highly performance and then when you need to, you move it to the right via a high-speed connection over to our high-performance analytic engine, where you can do super-low latency kinds of analytics, and all of that delivering out these real-time kinds of analytics to users, machine-to-machine kinds of communication, and betting those on analytics and business processes, feeding big data apps or applications.
This is an example of telco churn, where at the top of this chart if you're just building telco churn for example, where you have captured one kind of data and poured that into Hadoop, I'd be able to identify about 5% of your potential churn audience. As you move down this chart and add additional kinds of data sources, you do more complex kinds of analytics in the center column there. It allows you to act against that churn in a way that allows you to identify. You move from 5% identification up to 70% identification. So for telecommunications companies, for retail organizations, for any of the fast providers, anybody that has a customer base where there is a fear and a damage that is caused by churn.
This kind of analytics running on top of that exoskeleton-enabled version of Hadoop is what drives real value. What you can see here is that kind of value. This is an example taken from off of the annual report of a telecommunications company that shows their actual total subscribers, 32 million. Their existing churn rate which every telco reports 1.14, 4.3 million subscribers lost every year, costing them 1.14 billion dollars as well as 2.1 billion in revenue. This is a very modest example of how you generate value out of your data that lives in Hadoop, where you can see the potential cost of reacquisition where the potential here is to use Hadoop with the exoskeleton running analytics to basically help this telecommunications company save 160 million dollars as well as avoid 294 million in loss. That's the kind of example that we think is driving Hadoop forward.
Eric Kavangh: Alright, fantastic. And Jim, let me go ahead and give the keys to you. So, Jim Vogt. If you would click on that slide and use the down arrow in your keyboard.
Jim Vogt: I got it. Great picture. OK, thank you very much. I'll tell a little bit about Zettaset. We've been talking about Hadoop all afternoon here. What's interesting about our company is that we basically spend our careers hardening new technology for the enterprise - being able to plug the gaps, if you will, in our new technology to allow it to be widely deployed within our enterprise operational environment. There are a couple of things happening in the market right now. It's kind of like a big open pool party, right? But now the parents have come home. And basically we're trying to bring this thing back to some sense of reality in terms of how you build a real infrastructure piece here that can be scalable, repeatable, non-resource intensive, and secure, most importantly secure. In the marketplace today, most people are still checking the tires on Hadoop. The main reason is, there is a couple of things. One is that within the open source itself, although it does some very useful things in terms of being able to blend data sources, being able to find structure data and very useful data sources, it really lacks for a lot of the hardening and enterprise features around security, higher availability and repeatability that people need to deploy not just a 10- or 20-node cluster, but a 2, 000- and 20, 000-node cluster - there are multiple clusters. What has been monetized in the last two years has been mainly pro-services around setting up these eval clusters. So there is a not a repeatable software process to actually actively deploy this into the marketplace.
So what we built in our software is a couple of things. We're actually transparent into the distributions. At the end of the day, we don't care if it's CVH or HDP, it's all open source. If you look at the raw Apache components that built those distributions, there is really no reason why you have to lock yourself into any one distribution. And so, we work across distributions.
The other thing is that we fill in the gaps transparently in terms of some of the things that are missing within the code itself, the open source. So we talked about HA. HA is great in terms of making no failover, but what happens if any of the active processes that you're putting on these clusters fail? That could take it down or create a security hole, if you will. When we built software components into our solution, they all fall under an HA umbrella where we're actively monitoring all the processes running on the cluster. If code roles goes down, you take the cluster down, so basically, meaning no failover is great, unless you're actively monitoring all the processes running on the cluster, you don't have true HA. And so that's essential of what we developed here at Zettaset. And in a way that we've actually got a patent that has been issued on this and granted last November around this HA approach which is just quite novel and different from the open-source version and is much more hardened for the enterprise.
The second piece is being able to do real RBAC. People are talking about RBAC. They talk about other open-source projects. Why should you have to recreate all those entries and all those users and roles when they already exist in LDAP or in active directory? So we link those transparently and we fold all our processes not only under this RBAC umbrella, but also under the HA umbrella. They start to layer into this infrastructure encryption, encryption at data rest, state of motion, all the hardened security pieces that you really need to secure the information.
What is really driving this is our industries, which I have on the next slide, which profit finance and healthcare and have our compliances. You have to be able to protect this sets of data and you have to be able to do it on a very dynamic fashion because this data can be sitting anywhere across these parallel nodes and clusters and it can be duplicated and so forth, so essentially that's the big umbrella that we built. The last piece that people need is they need to be able to put the pieces together. So having the analytics that John talked to and being able to get value out of data and do that through an open interface tapped into this infrastructure, that's what we built in our software.
So the three cases that I had in here, and you guys are popping me along here were really around finance, healthcare and also cloud, where you're having to deal with multi-tenant environments and essentially have to separate people's sensitive data, so security and performance are key to this type of application whether its cloud or in a sensitive data environment.
The last slide here really talks to this infrastructure that we put together as a company is not just specific to Hadoop. It's something that we can equally apply to other NoSQL technologies and that's where we're taking our company forward. And then we're also going to pull in other open-source components, HBase and so forth, and secure those within that infrastructure in a way that you're not tied to any one distribution. It's like you truly have an open, secure and robust infrastructure for the enterprise. So that's what we're about and that's what we're doing to basically accelerate adoption of Hadoop so people get away from sending twenty-node clusters and actually have the confidence to employ a much larger environment that is more eyes on Hadoop and speeds the market along. Tak skal du have.
Eric Kavanagh: That's fantastic, great. Stick around for the Q&A. Finally, last but not the least, we've got Phu Hoang, CEO of DataTorrent. Let me go ahead and hand the keys to you. The keys are now yours. Click anywhere on that slide, use the down arrow on your keyboard to move them along.
Phu Hoang: Thank you so much.
So yes, I'm here to talk about DataTorrent and I actually think the story of DataTorrent is a great example of what Robin and Ray have been talking about through this session where they say that Hadoop is a great body of work, a great foundation. But it has a lot of goals. But the future is bright because the Hadoop ecosystem where more players are coming in are able to build and add value on top of that foundation to really bring it from storage to insights to action, and really that's the story of DataTorrent.
What I'm going to talk about today is really about real-time big data screening processing. What you see, as I'm interacting with customers, I've never met a single customer that says to me, "Hey, my goal is to take action hours or days after my business events arrive." In fact, they all say they want to take action immediately after the events occur. The problem with the delay is that, that is what Hadoop is today with its MapReduce paradigm. To understand why, it's worth revisiting the history of Hadoop.
I was leading much of Yahoo engineering when we hired Doug Cutting, the creator of Hadoop, and assigned over a hundred engineers to build out Hadoop to power our web search, advertising and data science processing. But Hadoop was built really as a back system to read and write and process these very large files. So while it's great disruptive technology because of its massive scalability and high ability at no cost, it has a hole in that there is a lot of latency to process these large files. Now, it is fair to say that Hadoop is now becoming the plateau operating system that is truly computing and is gaining wide adoption across many enterprises. They are still using that same process of collecting events into large files, running these batch Hadoop jobs to get there inside the next day. What enterprise customers now want is that they want those exact same insights but they want to build to get these insights much earlier, and this will enable them to really act on these events as the event happens, not after maybe hours later after it has been back processed.
Eric Kavanagh: Do you want to be moving your slides forward, just out of curiosity?
Phu Hoang: Yeah it's coming now. Let me illustrate that one example. In this example, using Hadoop in back-slope where you're constantly engaging with files, first an organization might accumulate all the events for the full day, 24 hours' worth of data. And then they batch process it, which may take another eight hours using MapReduce, and so now there is 32 hours of elapsed time before they get any insight. But with real-time stream processing, the events are coming in and are getting processed immediately, there is no accumulation time. Because we do all this processing, all in memory, the in-memory processing is also sub-second. All the time, you are reducing the elapsed time on 30 hours plus to something that is very small. If you're reducing 30 hours to 10 hours, that's valuable but if we can reduce it to a second, something profound happens. You can now act on your event while the event is still happening, and this gives enterprises the ability to understand what their products are doing, what their business is doing, what their users are doing in real time and react to it.
Let's take a look at how this happens. Really, a combination of market forces and technology has enabled a solution like DataTorrent to come together, so from a market perspective, Hadoop is really becoming the de facto big data architecture as we said, right? In an IDC study in 2013, they say that by the end of this year, two-thirds of enterprises would have deployed Hadoop and for DataTorrent, whether that's Apache Hadoop or any of our certified partners like Cloudera or Hortonworks, Hadoop is really clearly the choice for enterprise. From a technology perspective, and I think Robin and Ray alluded to this, Hadoop 2.0 was created to really enable Hadoop to extend to much more general cases than the batch MapReduce paradigm, and my co-founder, Amal, who was at Yahoo leading the development of Hadoop 2.0 really allows this layer of OS to have many more computation paradigms on top of it and real-time streaming is what we chose. By putting this layer of real-time streaming on top of YARN, you can really think of DataTorrent as the real-time equivalent of MapReduce. Whatever you can do in batch with MapReduce, you can now do in streaming with DataTorrent and we can process massive amount of data. We can slice and dice data in multiple dimensions. We have distributed computing and use YARN to give us resources. We have the full ecosystem of the open source Hadoop to enable fast application development.
Let me talk a little bit about the active capabilities of DataTorrent. In five minutes, it is hard for me to kind of give to you much in detail, but let me just discuss and re-differentiate it. First of all, sub-second scalable ingestions, right? This refers to DataTorrent's platform to be able to take that in real-time from hundreds of data sources and begin to process them immediately. This is in direct contact to the back processing of MapReduce that is in Hadoop 1.0 and events can vary in size. They may be as simple as a line in the log file or they may be much more complex like CDR, call data record in the telcom industry. DataTorrent is able to scale the ingestion dynamically up or down depending on the incoming load, and we can deal with tens of millions of incoming events per second. The other major thing here, of course, is the processing itself which is in real-time ETL logic. So once the data is in motion, it is going to go into the ETL logic where you are doing a stack transform and load, and so on. And the logic is really executed by combining a series of what we call operators connected together in a data flow grab. We have open source of over 400 operators today to allow you to build applications very quickly. And they cover everything from input connectors to all kinds of message process to database drivers and connectors where you are to load to all kinds of information to unstream.
The combination of doing all these in memory and building the scale across hundreds of nodes really drive the superior performance. DataTorrent is able to process billions of events per second with sub-second latency.
The last piece that I'd like to highlight is the high-availability architecture. DataTorrent's platform is fully post knowledge; that means that the platform automatically buffers the event and regularly checkpoints the state of the operators on the disk to ensure that there is possibly no problem. The applications can tell you in seconds with no data log and no human intervention. Simply put, data form processes billions of events and allots in data in seconds, it runs 24/7 and it never, ever goes down. The capabilities really set DataTorrent apart from the market and really make it the leading mission-critical, real-time analytics platform for enterprise. With that, we invite you to come visit our website and check us out.
Tak.
Eric Kavanagh: Yeah, thank you so much. I'll throw a question over to you, really a comment, and let you kind of expound upon it. I really think you're on the ball here with this concept of turning over these operators and letting people use these operators almost like Legos to build big data applications. Can you kind of talk about what goes into the process of taking these operators and stitching them together, how do you actually do that?
Phu Hoang: That's a great question. So first of all, these operators are in your standard application Java Logic. We supply 400 of them. They do all kinds of processing and so to build your application, you really are just connecting operators together into a data flow graph. In our customers, we find that they use a number of operators that we have in our library as well as they take their own job of custom logic and make it an operator so that they can substantiate that into a graph.
Eric Kavanagh: OK, good. I think it's a good segue to bring in John Santaferraro from Actian because you guys have a slightly similar approach, it seems to me, in opening up a sort of management layer to be able to play around with different operators. Can you talk about what you do with respect to what tools we're just talking about, John?
John Santaferraro: Yeah, exactly. We have a library of analytics operators as well as transformational operators, operators for blending and enriching data and it is very similar. You use a drag-and-drop interface to be able to stitch together these data flows or work flows, and even analytic workflows. So it's everything from being able to connect to data, to be able to blend and enrich data, to be able to run data science or machine learning algorithms and then even being able to push that into a high-performance low-latency analytic engine. What we find is that it's all built on the open-source nine project. So we capture a lot of the operators that they are developing and then we take all of that, and via YARN, very similar to what Phu described at DataTorrent, we push that down so that it is parallelized against all of the nodes in a Hadoop cluster. A lot of it is about making the data in Hadoop much more accessible to business users and less-skilled workers, somebody besides a data scientist.
Eric Kavanagh: OK, let me go bring in Nikita once again. I'm going to throw your five up as well. Can you kind of talk about how you approach this solution vis-à-vis what these two gentlemen just talked about? How does someone actually put this stuff together and make use from GridGain?
Nikita Ivanov: Well, I think the biggest difference between us and from practically the rest of them is we don't require you to do any recording - you don't have to do anything, it's a plug-and-play. If you have an application today, it's going to work faster. You don't have to change code; you don't have to do anything; you just have to install GridGain along the side of Hadoop cluster and that's it. So that's the biggest difference and we talked to our customers. There are different myriad of solutions today that ask you to change something: programming, doing your API, using your interfaces and whatnot. Ours is very simple. You don't need to invest a lot of time into the Hadoop ecosystem, and whatever you used to do, the MapReduce or any of the tools continue to use. With GridGain, you don't have to change any single line of code, it's just going to work faster. That's the biggest difference and that's the biggest message for us.
Eric Kavanagh: Let's get Jim back in here too. Jim, your quote is killing me. I had to write it down in between that. I'll put it into some kind of deck, but the Hadoop ecosystem right now is like a pool party and the parents just came home. That is funny stuff man; that is brilliant. Can you kind of talk about how you guys come onto the scene? How do you actually implement this? How long does that take? How does all that work?
Jim Kaskade: Yes. So there are a couple of varieties depending on the target customer, but typically these days, you see evaluations where security is factored in, in some of these hardening requirements that I talked about. What has happened in some other cases, and especially last year where people had big plans to deploy, is that there was kind of a science project, if you will, or somebody was playing with the technology and had a cluster up and working and was working with it but then the security guy shows up, and if it is going to go on a live data center, it has to basically comply with the same requirements that we have for other equipment running in the data center, if it is going to be an infrastructure that we build out. Last year, we had even some banks that told us they were going to deploy 400 to 1, 000 nodes last year and they're still sitting on a 20-node cluster mainly because now a security person has been plugged in. They've got to be worried about financial compliance, about sets of information that is sitting on a cluster, and so forth. It varies by customer, but typically this is kind of what elongates the cycles and this is typical of a new technology where if you really want to deploy this in production environment, it really has to have some of these other pieces including the very valuable open-source pieces, right?
Eric Kavanagh: OK, good. Lad os se. I'm going to bring Phu back into the equation here. We've got a good question for you. One of the attendees is asking how is DataTorrent different from Storm or Kafka or the Redis infrastructure. Phu, are you out there? Hey, Phu, can you hear me? Maybe I'm mute.
Let's bring Ray Wang back into this. Ray, you've seen a lot of these technologies and looked at how they worked. I really love this concept of turning over control or giving control to end users of the operators. I like to think of them as like really powerful Legos that they can use to kind of build some of these applications. Can you comment on that? What do you think about all that?
Ray Wang: Coming from my technical background, I'd say I'm scared - I was scared shitless! But honestly, I think it's important, I mean, in order to get scale. There's no way you can only put so many requests. Think about the old way we did data warehousing. In the business I had to file the request for a report so that they could match all the schemes. I mean, it's ridiculous. So we do have to get to a way for the business side of the house and definitely become data jocks. We actually think that in this world, we're going to see more digital artists and people that have the right skills, but also understand how to take that data and translate that into business value. And so these digital artisans, data artisans depending on how you look at this, are going to need both really by first having the curiosity and the right set of questions, but also the knowledge to know when the data set stinks. If I'm getting a false positive or a false negative, why is that happening?
I think a basic level of stats, a basic level of analytics, understanding that there's going to be some training required. But I don't think it's going to be too hard. I think if you get the right folks that should be able to happen. You can't democratize the whole decision-making process. I see that happening. We see that in a lot of companies. Some are financial services clients are doing that. Some of our retail folks are doing that, especially in the razor-thin margins that you are seeing in retail. I was definitely seeing that in high tech just around here in the valley. That's just kind of how people are. It's emerging that way but it's going to take some time because these basic data skills are still lacking. And I think we need to combine that with some of the stuff that some of these guys are doing here on this webinar.
Eric Kavanagh: Well, you bring up a really good point. Like how many controls you want to give to the average end user. You don't want to give an airplane cockpit to someone who's driving a car for the first time. You want to be able to closely control what they have control over. I guess my excitement kind of stems around being able to do things yourself, but the key is you got to put the right person in that cockpit. You got to have someone who really knows what they're doing. No matter what you hear from the vendor community folks, when somebody's more powerful tools are extremely complex, I mean if you are talking about putting together a string of 13, 14, 15 operators to do a particular type of transformation on your data, there are not many people who could do that well. I think we're going to have many, many more people who do that well because the tools are out there now and you can play with the stuff, and there is going to be a drive to be able to perfect that process or at least get good at it.
We did actually lose Phu, but he's back on the line now. So, Phu, the question for you is how is DataTorrent different from, like, Storm or Kafka or Redis or some of these others?
Phu Hoang: I think that's a great question. So, Redis of course is really an in-memory data store and we connect to Redis. We see ourselves as really a processing engine of data, of streaming data. Kafka again is a great bus messaging bus we use. It's actually one of our favorite messaging bus, but someone has to do the big data processing across hundreds of nodes that is fault tolerant, that is scalable, and I repeat that as the job that we play. So, yes, we are similar to Storm, but I think that Storm is really developed a long time ago even before Hadoop, and it doesn't have the enterprise-level thinking about scalability to the hundreds and millions, now even billions of events, nor does it really have the HA capability that I think enterprise requires.
Eric Kavanagh: Great. And you know, speaking of HA, I'll use that as an excuse to bring Robin Bloor back into the conversation. We just talked about this yesterday. What do you mean by high availability? What do you mean by fault tolerance? What do you mean by real time, for example? These are terms that can be bent. We see this all time in the world of enterprise technology. It's a good term that other people kind of glom onto and use and co-opt and move around and then suddenly things don't mean quite what they used to. You know, Robin, one of my pet peeves is this whole universe of VOIP. It's like "Why would we go down in quality? Isn't it important to understand what people say to you and why that matters?" But I'll just ask you to kind of comment on what you think. I'm still laughing about Ray's comment that he's scared shitless about giving these people. What do you think about that?
Ray Wang: Oh, I think it's a Spider-man problem, isn't it? Med store magtbeføjelser følger store forpligtigelser. You really, in terms of the capabilities out there, I mean it changed me actually a long time ago. You know, I would give my ITs some of the capabilities that they have gotten now. We used to do it extraordinary amounts of what I would say was grunt work that the machines do right now and do it in parallel. They do things that we could never have imagined. I mean we would have understood mathematically, but we could never imagine doing. But there is some people understand data and Ray is completely right about this. The reason to be scared is that people will actually start getting wrong conclusions, that they will wrangle with the data and they will apply something extremely powerful and it will appear to suggest something and they will believe it without actually even being able to do anything as simple as have somebody doing audit on whether their result is actually a valid result. We used to do this all the time in the insurance company I used to work for. If anybody did any work, somebody always checks. Everything was checked by at least one person against the person who did it. These environments, the software is extremely strong but you got to have the discipline around it to use it properly. Otherwise, there'll be tears before bedtime, won't there?
Eric Kavanagh: I love that quote, that's awesome. Let me see. I'm going to go ahead and throw just for this slide up here from GridGain, can you talk about, Nikita, when you come in to play, how do you actually get these application super charged? I mean, I understand what you are doing, but what does the process look like to actually get you embedded, to get you woven in and to get all that stuff running?
Nikita Ivanov: Well, the process is relatively simple. You essentially just need to install GridGain and make a small configuration change, just to let Hadoop know that there is now the HDFS if you want to use HDFS and you have to set up which way you want to use it. You can get it from BigTop, by the way. It's probably the easiest way to install it if you're using the Hadoop. Det handler om det. With the new versions coming up, a little in about few weeks from now, by the end of May, we're going to have even more simplified process for this. So the whole point of the in-memory Hadoop accelerator is to, do not code. Do not make any changes to your code. The only that you need to do is install it and have enough RAM in the cluster and off you go, so the process is very simple.
Eric Kavanagh: Let me bring John Santaferraro back in. We'll take a couple more questions here. You know, John, you guys, we've been watching you from various perspectives of course. You were over at PEAR Excel; that got folded into Actian. Of course, Actian used to be called Ingres and you guys made a couple of other acquisitions. How are you stitching all of that stuff together? I realize you might not want to get too technical with this, but you guys have a lot of stuff now. You've got Data Rush. I'm not sure if it's still the same name, but you got a whole bunch of different products that have been kind of woven together to create this platform. Talk about what's going on there and how that's coming along.
John Santaferraro: The good news is, Eric, that separately in the companies that we're acquired Pervasive, PEAR Excel and even when Actian had developed, everybody developed their product with very similar architectures. Number one, they were open with regards to data and interacting with other platforms. Number two, everything was parallelized to run in a distributed environment. Number three, everything was highly optimized. What that allowed us to do is to very quickly make integration points, so that you can be creating these data flows already today. We have established the integration, so you create the data flows. You do your data blending and enriching right on Hadoop, everything parallelized, everything optimized. When you want, you move that over into our high-performance engines. Then, there's already a high-performance connection between Hadoop and our massively parallel analytic engine that does these super-low-latency things like helping a bank recalculate and recast their entire risk portfolio every two minutes and feeding that into our real-time trading system or feeding it into some kind of a desktop for the wealth manager so they can respond to the most valuable customers for the bank.
We have already put those pieces together. There's additional integration to be done. But today, we have the Actian Analytics Platform as our offering because a lot of that integration was ready to go. It has already been accomplished, so we're stitching those pieces together to drive this entire analytic value chain from connecting the data, all of the processing that you do of it, any kind of analytics you want to run, and then using it to feed into these automated business processes so that you're actually improving that activity over time. It's all about this end-to-end platform that already exists today.
Eric Kavanagh: That's pretty good stuff. And I guess, Jim, I'll bring you back in for another couple of comments, and Robin, I want to bring you in for just one big question, I suppose. Folks, we will keep all these questions - we do pass them on to the people who participated in the event today. If you ever feel a question you asked was not answered, feel free to email yours truly. You should have some information on me and how to get ahold from me. Also, just now I put a link to the full deck with slides from non-sponsoring vendors. So we put the word out to all the vendors out there in the whole Hadoop space. We said, "Tell us what your story is; tell us what's going on." It's a huge file. It's about 40-plus megabytes.
But Jim, let me bring you back in and just kind of talk about - again, I love this concept - where you're talking about the pool party that comes to an end. Could you talk about how it is that you manage to stay on top on what's happening in the open-source community? Because it's a very fast-moving environment. But I think you guys have a pretty clever strategy of serving this sort of enterprise-hardening vendor that sits on top or kind of around that. Can you talk about your development cycles and how you stay on top of what's happening?
Jim Vogt: Sure. It is pretty fast moving in terms of if you look at just a snapshot updates, but what we're shipping in functionality today is about a year to a year and a half ahead of what we can get on security capabilities out to the community today. It's not that they're not going to get there; it just takes time. It's a different process, it has contributors and so forth, and it just takes time. When we go to a customer, we need to be very well versed in the open source and very well versed in mainly the security things that we're bringing. The reason that we're actually issuing patents and submitting patents is that there is some real value in IP, intellectual property, around hardening these open-source components. When we support a customer, we have to support all the varying open-source components and all the varying distributions as we do, and we also need to have the expertise around the specific features that we're adding to that open source to create the solution that we create. As a company, although we don't want the customer to be a Hadoop expert, we don't think you need to be a mechanic to drive the car. We need to be a mechanic that understands the car and how it works and understand what's happening between our code and the open source code.
Eric Kavanagh: That's great. Phu, I'll give you one last question. Then Robin, I have one question for you and then we'll wrap up, folks. We will archive this webcast. As I suggested, we'll be up on insideanalysis.com. We'll also go ahead and have some stuff up on Techopedia. A big thank you to those folks for partnering with us to create this cool new series.
But Phu … I remember watching the demo of the stuff and I was just frankly stunned at what you guys have done. Can you explain how it is that you can achieve that level of no failover?
Phu Hoang: Sure, I think it's a great question. Really, the problem for us had three components. Number one is, you can't lose the events that are moving from operator to operator in the Hadoop cluster. So we have to have event buffering. But even more importantly, inside your operators, you may have states that you're calculating. Let's say you're actually counting money. There's a subtotal in there, so if that node goes down and it's in memory, that number is gone, and you can't start from some point. Where would you start from?
So today, you have to actually do a regular checkpoint of your operator state down to this. You put that interval so it does not become a big overhead, but when a node goes down, it can come back up and be able to go back to exactly the right state where you last checkpointed and be able to bring in the events starting from that state. That allows you to therefore continue as if the event actually has never happened. Of course, the last one is to make sure that your application manager is also fault tolerant so that doesn't go down. So all three factors need to be in place for you to say that you're fully fault tolerant.
Eric Kavanagh: Yeah, that's great. Let me go ahead and throw one last question over to Robin Bloor. So one of the attendees is asking, does anyone think that Hortonworks or another will get soaked up/invested in by a major player like Intel? I don't think there's any doubt about that. I'm not surprised, but I'm fascinated, I guess, that Intel jumped in before like an IBM or an Oracle, but I guess maybe the guys at IBM and Oracle think they've already got it covered by just co-opting what comes out of the open-source movement. What do you think about that?
Robin Bloor: It's a very curious move. We should see in light of the fact that Intel already had its own Hadoop distribution and what it has effectively done is just passed that over to Cloudera. There aren't many powers in the industry as large as Intel and it is difficult to know what your business model actually is if you have a Hadoop distribution, because it is difficult to know exactly what it is going to be used for in the future. In other words, we don't know where the revenue streams are necessarily coming from.
With somebody like Intel, they just want a lot of processes to be solved. It is going to support their main business plan the more that Hadoop is used. It's kind of easy to have a simplistic explanation of what Intel are up to. It's not so easy to guess what they might choose to do in terms of putting code on chips. I'm not 100% certain whether they're going to do that. I mean, it's a very difficult thing to call that. Their next move at the hardware level, I think, is the system on a chip. When we go to the system on a chip, you may actually want to put some basic software on the chip, so to speak. So putting HDFS on there; that might make some sense. But I don't think that that was what that money investment was about. I think all that money investment was about was just making sure that Intel had a hand in the game and is actually going forward.
In terms of who else is going to buy, that is also difficult to say. I mean, certainly the SAPs and Oracles of this world have got enough money to buy into this or IBM has got enough money to buy into it. But, you know, this is all open source. IBM never bought a Linux distribution, even though they plowed a lot of money into Linux. It didn't break their hearts that they didn't actually have a Linux distribution. They're very happy to cooperate with Red Hat. I would say maybe Red Hat will buy one of these distributions, because they know how to make that business model work, but it's difficult to say.
Eric Kavanagh: Yeah, great point. So folks, I'm going to go ahead and just share my desktop one last time here and just show you a couple of things. So after the event, check out Techopedia - you can see that on the left-hand side. Here's a story that yours truly wrote, I guess a couple of months ago or a month and a half ago, I suppose. It really kind of spun out of a lot of the experience that we had talking with various vendors and trying to dig in to understanding what exactly is going on with the space because sometimes it can be kind of difficult to navigate the buzz words and the hype and the terminology and so forth.
Also a very big thank you to all of those who have been Tweeting. We had one heck of a Tweet stream here going today. So, thank you, all of you. You see that it just goes on and on and on. A lot of great Tweets on TechWise today.
This is the first of our new series, folks. Thank you so much for tuning in. We will let you know what's going on for the next series sometime soon. I think we're going to focus on analytics probably in June sometime. And folks, with that, I think we're going to go ahead and close up our event. We will email you tomorrow with a link to the slides from today and we're also going to email you the link to that full deck, which is a huge deck. We've got about twenty different vendors with their Hadoop story. We're really trying to give you a sort of compendium of content around a particular topic. So for bedtime reading or whenever you're interested, you can kind of dive in and try to get that strategic view of what's going on here in the industry.
Med det vil vi byde dig farvel, folkens. Thank you again so much. Go to insideanalysis.com and Techopedia to find more information about all this in the future and we'll catch up to you next time. Hej hej.
