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Eric Kavanagh: Dame i gospodo, vrijeme je da se napunite! Vrijeme je za TechWise, potpuno novi show! Moje ime je Eric Kavanagh. Bit ću vam moderator za našu nastupnu epizodu TechWise. Upravo tako. Ovo je partnerstvo kompanija Techopedia i Bloor Group, naravno slave Inside Analysis.
Moje ime je Eric Kavanagh. Moderirat ću ovaj zaista zanimljiv i uključen događaj, narode. Kopaćemo duboko u tkanju da bismo razumjeli što se događa s ovom velikom stvari koja se zove Hadoop. Što je slon u sobi? Zove se Hadoop. Pokušat ćemo shvatiti što to znači i što se događa s tim.
Prije svega, velika hvala našim sponzorima, GridGain, Actian, Zettaset i DataTorrent. Doći ćemo do kratkih nekoliko riječi o njima na kraju ovog događaja. I mi ćemo imati pitanja i pitanja, pa nemojte biti sramežljivi - u bilo kojem trenutku pošaljite svoja pitanja.
Kopati ćemo u pojedinosti i postavljati teška pitanja našim stručnjacima. A kad govorimo o stručnjacima, hej, tu su oni. Dakle, slušat ćemo od našeg vlastitog dr. Robina Bloora, i ljudi, vrlo sam uzbuđen što imamo legendarnog Raya Wanga, glavnog analitičara i osnivača Constellation Research. On je danas na mreži, kako bi nam dao svoja razmišljanja, a on je poput Robina da je nevjerojatno raznolik i da se stvarno fokusira na mnogo različitih područja i ima sposobnost sintetizirati ih i stvarno razumjeti što se tamo događa u cijelom tom polju informacijske tehnologije i upravljanje podacima.
Dakle, tu je onaj mali slatki slon. Kao što vidite, on je na početku puta. Sad tek počinje, tek je neka vrsta započinjanja, čitava ova Hadoop stvar. Naravno, pretpostavljam davne 2006. Ili 2007., Kada je puštena u open-source zajednicu, ali događalo se mnogo toga, narode. Došlo je do ogromnog razvoja. U stvari, želim ispričati priču, pa ću napraviti brzu dijeljenje radne površine, barem mislim da jesam. Brzo dijelimo radnu površinu.
Prikazujem vam ove samo lude, lude ljude. Tako je Intel uložio 740 milijuna dolara za kupnju 18 posto Cloudere. Pomislila sam i ja sam kao: "Sveti Božić!" Počeo sam se baviti matematikom i to je: "To je vrijednost od 4, 1 milijarde dolara." Razmislimo o tome na trenutak. Mislim, ako WhatsApp vrijedi dvije milijarde dolara, pretpostavljam da bi i Cloudera mogla vrijediti 4, 1 milijardu dolara, zar ne? Mislim, zašto ne? Neki od ovih brojeva ovih dana su upravo kroz prozor, narode. Mislim, tipično u smislu ulaganja imate EBITDA i sve ove druge različite mehanizme, višestruke prihode i tako dalje. Pa, to će biti jedan hek od višestrukih prihoda da dođe do 4, 1 milijarde dolara za Cloudera, koja je sjajna tvrtka. Nemojte me krivo shvatiti - tamo ima nekih vrlo, vrlo pametnih ljudi, uključujući momka koji je pokrenuo cijelo ludost Hadoopa, Douga Cuttinga, on je tamo - puno vrlo inteligentnih ljudi koji rade puno stvarno, stvarno cool stvari, ali suština je da je 4, 1 milijarda dolara, to je mnogo novca.
Dakle, ovo je svojevrsni očiti trenutak prolaska kroz moju glavu, koji je čip, Intel. Njihovi dizajneri čipova donose neke čipove optimizirane od Hadoopa - moram tako misliti, ljudi. To je samo moja pretpostavka. To je samo glasina, koja dolazi od mene, ako hoćeš, ali nekako ima smisla. I što to sve znači?
Dakle, evo moje teorije. Što se događa? Mnogo toga nije novo. Masivna paralelna obrada nije strašno nova. Paralelna obrada sigurno nije novo. Već neko vrijeme sam u svijetu superračunala. Mnogo tih stvari koje se događaju nisu nove, ali postoji vrsta opće svijesti da postoji novi način napada nekih od tih problema. Ono što se meni događa, ako pogledate neke od velikih dobavljača Cloudere ili Hortonworks i nekolicinu drugih ljudi, ono što oni stvarno rade ako ih svedete na najcrnjiviju destiliranu razinu jest razvoj aplikacija. To rade.
Oni dizajniraju nove aplikacije - neke od njih uključuju poslovnu analitiku; neki od njih samo uključuju sustave za punjenje. Jedan od naših dobavljača koji je o tome razgovarao, rade takve stvari cijeli dan, i to na današnjoj izložbi. No, ako je užasno nov, opet je odgovor "nije baš", ali događaju se velike stvari i osobno, mislim da je ono što se događa s Intelom koji radi ovako ogromna investicija potez koji donosi tržište. Oni danas gledaju svijet i vide da je to danas vrsta monopola. Tu je Facebook i pretukli su samo smrad iz lošeg MySpacea. LinkedIn je pretukao njušku od siromašnog Tko je tko. Dakle, osvrnite se oko sebe i to je jedna usluga koja danas dominira svim tim različitim prostorima u našem svijetu, a mislim da je ideja da će Intel baciti sve svoje čipove na Cloudera i pokušati ga podići na vrh snopa - to je jednostavno moja teorija.
Dakle, ljudi, kao što sam rekao, imat ćemo dugu sesiju pitanja i pitanja, tako da nemojte biti stidljivi. Pošaljite svoja pitanja u bilo kojem trenutku. To možete učiniti pomoću komponente Q&A na vašoj webcast konzoli. I s tim želim doći do našeg sadržaja jer imamo puno stvari za prolaziti.
Dakle, Robin Bloor, daj da ti predam ključeve, a pod je tvoj.
Robin Bloor: Dobro, Eric, hvala na tome. Dovedimo slonove koji plešu. Zapravo je zanimljiva činjenica da su slonovi jedini kopneni sisavci koji zapravo ne mogu skakati. Svi ovi slonovi u ovoj grafičkoj slici imaju barem jednu nogu na zemlji, pa pretpostavljam da je to izvedivo, ali u određenoj mjeri to su očito Hadoop slonovi, tako vrlo, vrlo sposobni.
Pitanje, zaista, mislim da se o njemu mora raspravljati i mora se iskreno raspravljati. O tome se mora razgovarati prije nego što krenete bilo gdje drugo, a to je da stvarno počnete govoriti o tome što je Hadoop zapravo.
Jedna od stvari koja apsolutno potječe od čovjekove igre je trgovina ključnim vrijednostima. Imali smo trgovine s ključnom vrijednošću. Nekada smo ih imali u IBM mainframeu. Imali smo ih na miniračunalima; DEC VAX imao je IMS datoteke. Bilo je ISAM mogućnosti koje su bile na gotovo svakom miniračunalu koje možete dobiti. Ali negdje oko kasnih 80-ih, Unix je došao i Unix zapravo nije imao nijednu trgovinu ključ-vrijednost. Kad ga je Unix razvio, razvijali su se vrlo brzo. Doista se dogodilo da su dobavljači baza podataka, posebno Oracle, tamo otišli parati i prodali vaše baze podataka kako bi se brinuli o svim podacima kojima želite upravljati na Unixu. Pokazalo se da su Windows i Linux isti. Dakle, industrija je prošla najbolji dio 20 godina bez trgovine ključnim vrijednostima opće namjene. Eto, sad se vratio. Ne samo da je natrag, već je i skalabilan.
Mislim da je zapravo temelj onoga što Hadoop doista i u određenoj mjeri određuje kamo će ići. Što volimo u trgovinama s ključnom vrijednošću? Oni od vas koji su stari koliko i ja, a zapravo se sjećam da rade s trgovinama ključeva vrijednosti shvaćaju da biste ih mogli prilično iskoristiti za neformalno postavljanje baze podataka, ali samo neformalno. Znate da metapodaci brzo vrednuju spremišta u programskom kodu, ali zapravo to možete napraviti od vanjske datoteke, a mogli biste i ako želite započeti s obradom spremišta ključeva i vrijednosti pomalo poput baze podataka. Ali, naravno, nije imala svu sposobnost oporavka koju ima baza podataka i nije imala grozno stvari koje baze podataka imaju sada, ali to je bila zaista korisna značajka za programere i to je jedan od razloga zašto da se Hadoop pokazao toliko popularnim - naprosto zato što su brzi koderi, programeri, programeri. Shvatili su da trgovina nije samo ključna vrijednost, već je i ključ-vrijednost trgovine. To se mjeri prilično neograničeno. Poslao sam ove ljestvice na tisuće poslužitelja, tako da je to uistinu velika stvar o Hadoopu, to je to.
Uz to ima i MapReduce, što je algoritam paralelizacije, ali zapravo to, po mom mišljenju, nije važno. Znači, Hadoop je kameleon. To nije samo datotečni sustav. Vidio sam razne vrste zahtjeva za Hadoop: to je tajna baza podataka; to nije tajna baza podataka; to je uobičajena trgovina; to je analitički alatni okvir; to je ELT okruženje; to je alat za čišćenje podataka; to je skladište podataka za streaming platforme; to je arhivska trgovina; to je lijek protiv raka, i tako dalje. Većina ovih stvari zaista nije istinita za Hadoop vanilije. Hadoop je vjerojatno prototipiranje - to je sigurno okruženje za izradu prototipa za SQL bazu podataka, ali zapravo nema, ako starosni prostor s katalogom starosne dobi postavite preko Hadoopa, dobili ste nešto što liči na bazu podataka, ali zapravo nije što bi bilo tko nazvao bazom podataka u smislu sposobnosti. Puno tih mogućnosti, sigurno ih možete dobiti na Hadoopu. Sigurno ih je puno. U stvari, možete dobiti neki izvor Hadoopa, ali sam Hadoop nije ono što bih nazvao operativno ojačan i zato je posao oko Hadoopa, stvarno ne bih bio ni na čemu drugom, taj da biste trebali imati treće mjesto -partyni proizvodi za poboljšanje.
Dakle, pričanje o vama može se predstaviti u samo nekoliko redaka dok govorim o Hadoopu prekoračenju. Prije svega, sposobnost upita u stvarnom vremenu, pa znate da je u stvarnom vremenu vrsta poslovnog vremena, zapravo, gotovo uvijek je kritična izvedba u suprotnom. Mislim, zašto bi inženjer u stvarnom vremenu? Hadoop to stvarno ne radi. Čini nešto što je gotovo u stvarnom vremenu, ali zapravo ne čini stvari u stvarnom vremenu. Streaming radi, ali ne čini streaming na način na koji bih nazvao doista kritične misije tipa platforme za strujanje aplikacija. Postoji razlika između baze podataka i prodajnog prostora. Sinkronizirajte ga s Hadoop-om omogućava vam spremanje podataka. To je poput baze podataka, ali nije isto kao baza podataka. Hadoop u svom izvornom obliku, po mom mišljenju, uopće nije kvalificiran kao baza podataka jer nedostaje nekoliko stvari koje bi baza podataka trebala imati. Hadoop čini puno, ali to ne čini posebno dobro. Opet, sposobnost postoji, ali mi smo daleko od toga da zapravo imamo brzu sposobnost na svim tim područjima.
Druga stvar koju treba shvatiti o Hadoopu jest da je pomalo priješao put od njegovog razvoja. Razvijen je u ranim danima; razvijen je kada smo imali poslužitelje koji su zapravo imali samo jedan procesor na poslužitelju. Nikad nismo imali višejezgrene procesore, a izgrađen je da bi pokrenuo mreže, pokretao mreže i severs. Jedan od dizajnerskih ciljeva Hadoopa bio je da nikad ne izgubi posao. I zapravo se radilo o kvaru diska, jer ako imate stotine poslužitelja, vjerojatnost je da ako imate diskove na poslužiteljima, vjerojatnost je da ćete dobiti dostupnost za neograničeno vrijeme kao što je 99.8. To znači da ćete u prosjeku dobiti kvar jednog od tih poslužitelja jednom svakih 300 ili 350 dana, jedan dan u godini. Dakle, ako ih imate na stotine, vjerojatnost je da ćete bilo kojeg dana u godini doći do kvara na poslužitelju.
Hadoop je stvoren posebno za rješavanje tog problema - tako da, u slučaju da ništa ne uspije, pravi snimke svega što se događa, na svakom pojedinom poslužitelju i može oporaviti skupni posao koji se izvodi. A to je sve što se zapravo ikad dogodilo na Hadoopu, bilo je skupa radna mjesta i to je stvarno korisna sposobnost, mora se reći. Neki od skupnih poslova koji su se izvodili - posebice u Yahoo-u, za koji mislim da je Hadoop bio rođen - pokrenuli bi se dva ili tri dana, a ako ne uspije nakon jednog dana, stvarno niste željeli izgubiti posao to je bilo učinjeno. Dakle, to je bila točka dizajna iza dostupnosti na Hadoopu. Ne biste zvali tako visoku dostupnost, ali mogli biste je nazvati i velikom dostupnošću za serijske serijske zadatke. To je vjerojatno način na koji to treba gledati. Visoka raspoloživost uvijek se konfigurira prema karakteristikama radne linije. U ovom trenutku, Hadoop se može konfigurirati samo za stvarno serijske serijske poslove što se tiče takve vrste oporavka. Poduzetnička visoka dostupnost vjerojatno se najbolje misli u pogledu transakcijskog cjeloživotnog učenja. Vjerujem da Hadoop, ako na to ne gledate kao na stvarnu stvar, to još ne čini. Vjerojatno je daleko od toga.
Ali evo lijepe stvari o Hadoopu. Grafička slika s desne strane koja ima popis dobavljača oko ruba i svi crti na njemu označavaju veze između tih dobavljača i drugih proizvoda u Hadoop ekosustavu. Ako pogledate to, to je nevjerojatno impresivan ekosustav. To je prilično izvanredno. Očito razgovaramo s puno dobavljača u smislu njihovih mogućnosti. Među dobavljačima s kojima sam razgovarao, postoje neke zaista izvanredne mogućnosti korištenja Hadoopa i memorije, načina korištenja Hadoopa kao komprimirane arhive, korištenja Hadoopa kao ETL okruženja, i tako dalje, i tako dalje. Ali doista, ako proizvod dodate samom Hadoopu, on djeluje izuzetno dobro u određenom prostoru. Dakle, iako sam kritičan prema rodnom Hadoopu, nisam kritičan prema Hadoopu kada mu zapravo dodate malo snage. Po mom mišljenju, Hadoop-ova popularnost jamči njegovu budućnost. Pod tim mislim, čak i ako nestane svaka linija koda napisana na Hadoopu, ne vjerujem da će nestati i HDFS API. Drugim riječima, mislim da je datotečni sustav, API ovdje da ostane, a možda i PREVOZENO, planer koji se nadgleda.
Kad to stvarno pogledate, to je vrlo važna sposobnost, a ja ću o tome nešto razmišljati u minutu, ali druga stvar koja je, recimo, uzbudljivi ljudi o Hadoopu je cijela slika otvorenog koda. Dakle, vrijedi istražiti što je slika otvorenog koda u smislu onoga što ja smatram stvarnom sposobnošću. Iako Hadoop i sve njegove komponente sigurno mogu raditi ono što nazivamo duljinom podataka - ili kako ga radije nazivam, spremnik podataka - sigurno je vrlo dobro mjesto za prikazivanje za unošenje podataka u organizaciju ili za prikupljanje podataka u organizaciji - izuzetno dobro za kutije s pijeskom i za ribolovne podatke. Vrlo je dobar kao razvojna platforma za izradu prototipa koju biste mogli implementirati na kraju dana, ali znate kao razvojno okruženje gotovo sve što želite. Kao arhivska trgovina prilično ima sve što vam treba, a naravno da nije skupo. Mislim da se ne bismo trebali razvesti od ove dvije stvari od Hadoopa, iako one nisu formalno, ako želite, komponente Hadoopa. Internetski klin donio je ogromnu količinu analitike u svijet otvorenog koda i puno se te analitike sada vodi na Hadoopu, jer vam daje pogodno okruženje u kojem zapravo možete uzeti puno vanjskih podataka i samo početi igrati na analitičkom pijesku.
I onda ste dobili otvorene mogućnosti, obje su strojno učenje. Oboje su izuzetno moćni u smislu da implementiraju moćne analitičke algoritme. Ako ove stvari sastavite, dobit ćete jezgre neke vrlo, vrlo važne sposobnosti, koja je na ovaj ili onaj način vrlo vjerovatna - da li se ona razvija samostalno ili dolaze li dobavljači da popune nedostajuće dijelove - vrlo je vjerojatno da će se tako nastaviti još dugo i sigurno mislim da strojno učenje već ima vrlo velik utjecaj na svijet.
Evolucija Hadoopa, YARN promijenila je sve. Dogodilo se da je MapReduce bio prilično zavaren za rani datotečni sustav HDFS. Kada je YARN predstavljen, u prvom je izdanju stvorio sposobnost za planiranje. Ne očekujete izuzetno sofisticirano zakazivanje od prvog izdanja, ali to je značilo da to više nije nužno okruženje zakrpa. Bilo je to okruženje u kojem je bilo moguće zakazati više radnih mjesta. Čim se to dogodilo, postojao je čitav niz dobavljača koji su se držali podalje od Hadoopa - upravo su ušli i povezali se s njim, jer su tada mogli gledati kao okruženje zakazivanja u datotečnom sustavu i mogli su obraćati stvari na to. Postoje čak i dobavljači baza podataka koji su implementirali svoje baze podataka na HDFS, jer oni samo uzimaju motor i jednostavno ga stavljaju na HDFS. Kaskadno i s YARN-om postaje vrlo zanimljivo okruženje jer možete stvoriti složene radne tokove preko HDFS-a, a to stvarno znači da možete početi razmišljati o tome kao o stvarno platformi koja istodobno može izvoditi više poslova i koja se gura prema točki raditi kritične stvari. Ako ćete to učiniti, vjerojatno ćete trebati kupiti neke komponente drugih proizvođača poput sigurnosti i tako dalje, i tako dalje, što Hadoop zapravo nema račun za reviziju da bi popunio praznine, ali vi dođite do točke gdje čak i sa izvornim otvorenim kodom možete učiniti neke zanimljive stvari.
U smislu kuda mislim da će Hadoop zapravo krenuti, osobno vjerujem da će HDFS postati zadani datotečni sustav za razmjenu razmjera i stoga će postati OS, operativni sustav, za mrežu za protok podataka. Mislim da u tome ima ogromnu budućnost i mislim da se tamo neće zaustaviti. I zapravo mislim da ekosustav samo pomaže, jer gotovo svi, svi dobavljači u svemiru, zapravo integriraju Hadoop na ovaj ili onaj način i to samo omogućuju. U smislu još jedne točke koju vrijedi naglasiti, u smislu Hadoopove prekomjerne vrijednosti, nije li to baš dobra platforma plus paralelizacija. Ako zapravo pogledate što radi, ono što zapravo radi jest to da redovito snima fotografije na svakom poslužitelju dok izvršava svoje MapReduce zadatke. Ako biste planirali stvarno brzu paralelizaciju, ne biste radili ništa slično. Zapravo, vjerovatno ne biste samostalno koristili MapReduce. MapReduce je samo ono što bih rekao pola sposobno za paralelizam.
Postoje dva pristupa paralelizmu: jedan je putem cjevovoda, a drugi dijeljenjem podataka MapReduce i obavlja podjelu podataka, tako da postoji puno poslova na kojima MapReduce zapravo ne bi bio najbrži način da to učinite, ali hoće dajte vam paralelizam i od toga nema oduzimanja. Kad imate puno podataka, ta vrsta moći obično nije toliko korisna. PRIJEVOZ, kao što sam već rekao, vrlo je mlada sposobnost zakazivanja.
Hadoop je, neka vrsta crtanja crte u pijesku, Hadoop nije skladište podataka. To je tako daleko od skladišta podataka da je gotovo apsurdan prijedlog reći da jest. U ovom dijagramu, ono što prikazujem na vrhu je svojevrsni protok podataka, koji iz spremnika podataka Hadoop prelaze u baganu bazu podataka razmjera razmjera, što ćemo u stvari učiniti, skladište podataka u poduzeću. Prikazujem naslijeđene baze podataka, unosim podatke u skladište podataka i radnju učitavanja stvarajući nepodnošljive baze podataka iz skladišta podataka, ali to je zapravo slika koju počinjem vidjeti, i rekao bih da je to kao prva generacija što se događa s skladištem podataka s Hadoop-om. Ali ako sami pogledate skladište podataka, shvatite da ispod skladišta podataka imate optimizator. Rasprostranjeni ste upiti radnika u vrlo mnogo procesa koji sjede na možda vrlo velikom broju diskova. To se događa u skladištu podataka. To je zapravo vrsta arhitekture koja je stvorena za skladište podataka i potrebno je dugo vremena da se nešto takvo izgradi, a Hadoop uopće nema to. Dakle, Hadoop nije skladište podataka i po mom mišljenju to neće postati uskoro.
On ima taj relativni rezervoar podataka, a nekako izgleda zanimljivo ako na svijet gledate samo kao na niz događaja koji se slijevaju u organizaciju. To je ono što prikazuje na lijevoj strani ovog dijagrama. Kada prođe kroz mogućnost filtriranja i usmjeravanja i stvari koje trebaju ići za streaming otimaju se od aplikacija za streaming, a sve ostalo ide izravno u spremnik podataka gdje se priprema i čisti, a zatim preko ETL-a prosljeđuje bilo jednim podacima skladište ili skladište logičkih podataka koje se sastoji od više motora. Ovo je, po mom mišljenju, prirodna linija razvoja za Hadoop.
Što se tiče ETW-a, jedna od stvari koju valja istaknuti je da je zapravo skladište podataka premješteno - nije ono što je bilo. Svakako, danas očekujete da postoji hijerarhijska sposobnost hijerarhijskih podataka o tome što ljudi ili neki ljudi nazivaju dokumente u skladištu podataka. To je JSON. Moguće je da su mrežni upiti to baze podataka grafikona, možda i analitika. Dakle, ono čemu se krećemo je ETW koji zapravo ima složenije radno opterećenje od onih na koje smo navikli. To je nekako zanimljivo, jer na neki način to znači da je skladište podataka sve sofisticiranije, a zbog toga će proći još duže vrijeme dok Hadoop dođe bilo gdje blizu. Značenje skladišta podataka se širi, ali svejedno uključuje optimizaciju. Morate imati mogućnost optimizacije, ne samo nad upitima već i tijekom svih ovih aktivnosti.
To je stvarno. To je sve što sam htio reći o Hadoopu. Mislim da mogu predati Rayu koji nema slajdove, ali uvijek je dobar u razgovoru.
Eric Kavanagh: Ja ću uzeti slajdove. Tu je naš prijatelj, Ray Wang. Pa, Ray, što misliš o svemu ovome?
Ray Wang: Mislim da je to vjerojatno bila jedna od najsurovijih i velikih povijesti trgovina ključnim vrijednostima i gdje je Hadoop otišao u vezu s poduzećima koja su nestala, tako da uvijek puno učim kad slušam Robina.
Zapravo imam jedan slajd. Ovdje mogu poskočiti jedan slajd.
Eric Kavanagh: Samo naprijed i kliknite na, kliknite start i idite na dijeljenje radne površine.
Ray Wang: Shvatio sam, evo. Zapravo ću podijeliti. Možete vidjeti i samu aplikaciju. Da vidimo kako to ide.
Sav ovaj razgovor o Hadoopu, a zatim ulazimo duboko u razgovor o tehnologijama koje postoje i kamo Hadoop vodi, i puno puta bih ga volio ponovo preuzeti da bismo zaista imali poslovnu raspravu. Mnogo stvari koje se događaju s tehnološke strane zaista je ovaj komad gdje smo razgovarali o skladištima podataka, upravljanju informacijama, kvaliteti podataka, savladavanju tih podataka i tako smo skloni tome vidjeti. Ako pogledate ovaj grafikon ovdje na samom dnu, to je vrlo zanimljivo da vrste pojedinaca na koje naletimo na razgovor o Hadoopu. Imamo tehnologe i znanstvenike koji se bave istraživanjem, koji imaju puno uzbuđenja, i obično se radi o izvorima podataka, zar ne? Kako savladati izvore podataka? Kako to postići na prave razine kvalitete? Što mi radimo u vezi s upravljanjem? Što mi možemo učiniti da se podudaramo s različitim vrstama izvora? Kako čuvamo lozu? I sve takve rasprave. I kako da iz našeg Hadoopa izvučemo više SQL-a? Dakle, taj se dio događa na ovoj razini.
Zatim na strani informiranja i orkestracije, ovdje postaje zanimljivo. Počinjemo vezati rezultate ovog uvida koji dobivamo ili ga povlačimo iz natrag u poslovne procese? Kako to možemo povezati s bilo kojom vrstom metapodataka? Povezujemo li točkice između objekata? I tako novi glagoli i rasprave o tome kako koristimo te podatke krećući se od onoga što tradicionalno nalazimo u svijetu CRUD-a: stvaramo, čitamo, ažuriramo, brišemo, do svijeta u kojem se raspravlja o tome kako se bavimo, dijelimo ili surađujemo ili voljeti ili povući nešto.
Tu počinjemo vidjeti puno uzbuđenja i inovacija, posebno o tome kako privući ove informacije i upotrijebiti ih. To je tehnološki rasprava ispod crvene linije. Iznad te crvene linije dobivamo vrlo pitanja koja smo uvijek željeli postaviti, a jedno od njih koje uvijek postavljamo je poput, na primjer, možda je pitanje u maloprodaji za vas poput: "Zašto se crveni džemperi prodaju bolje u Alabami nego plavi džemperi u Michiganu? " Možete razmišljati o tome i reći: "To je nekako zanimljivo." Vidite taj obrazac. Postavljamo to pitanje i pitamo se: "Hej, što radimo?" Možda se radi o državnim školama - Michigan nasuprot Alabami. OK, shvaćam ovo, vidim kamo idemo. I tako počinjemo dobivati poslovnu stranu kuće, ljude u financijama, ljude koji imaju tradicionalne mogućnosti BI, ljude u marketingu i ljude iz HR-a koji govore: "Gdje su moji obrasci?" Kako dolazimo do tih obrazaca? I tako na Hadoop strani vidimo drugi način inovacije. Zapravo o tome kako brže ažuriramo uvide. Kako uspostaviti ove vrste veza? Sve ide ljudima koji rade poput: ad: tech koji uglavnom pokušavaju povezati oglase i relevantne sadržaje od bilo čega, od mreža za nadmetanje u stvarnom vremenu, do kontekstualnih oglasa i smještaja oglasa i to u pokretu.
Dakle, zanimljivo je. Vidite napredak Hadoopa iz, "Hej, evo tehnološkog rješenja. Evo što moramo učiniti kako bismo ove informacije iznijeli ljudima." Onda kad pređe preko linije poslovanja, ovo postaje zanimljivo. To je uvid. Gdje je izvedba? Gdje je odbitak? Kako predvidimo stvari? Kako ćemo utjecati? I onda to dovedite na onu zadnju razinu gdje zapravo vidimo još jedan niz Hadoop inovacija koje se događaju oko sustava odlučivanja i radnji. Koja je sljedeća najbolja akcija? Dakle, znate da se plavi džemperi bolje prodaju u Michiganu. Sjediš na tonu plavih džempera u Alabami. Očigledna stvar je: "Da, hajde da se ovo pošalju vani." Kako to možemo učiniti? Koji je sljedeći korak? Kako da to svežemo natrag? Možda je sljedeća najbolja radnja, možda je to prijedlog, možda je to nešto što vam pomaže u sprečavanju problema, a možda i nije akcija, što je samo po sebi djelovanje. Tako počinjemo sa pojavom ovakvih obrazaca. A ljepota ovog što se spominjete u trgovinama s ključnom vrijednošću, Robin, je u tome što se događa tako brzo. To se događa na način da mi nismo razmišljali o ovome na ovaj način.
Vjerojatno bih rekao u zadnjih pet godina kad smo pokupili. Počeli smo razmišljati u smislu kako možemo ponovo iskoristiti prodavaonice ključnih vrijednosti, ali tek u posljednjih pet godina ljudi na to gledaju drugačije i to je kao da se tehnološki ciklusi ponavljaju u 40-godišnjim obrascima, tako da je ovo vrsta od smiješne stvari gdje gledamo oblak i baš kao da dijelim vrijeme s mainframeom. Gledamo u Hadoop i volimo ključ-vrijednost trgovine - možda je to podatkovna marka, manje od skladišta podataka - i ponovo počinjemo vidjeti ove obrasce. Ono što trenutno pokušavam učiniti je razmisliti o tome što su ljudi radili prije 40 godina? Koji su se pristupi, tehnike i metodologije primjenjivali, a koje su ograničavale tehnologije koje su ljudi imali? To je vrsta pokretanja ovog procesa razmišljanja. Dakle, kako prolazimo kroz širu sliku Hadoopa kao alata, kad se vratimo natrag i razmišljamo o poslovnim implikacijama, ovo je vrsta puta kojim ljudi obično prolazimo tako da možete vidjeti koje dijelove, koji su dijelovi u podacima put odluka. To je samo nešto što sam želio podijeliti. To je vrsta razmišljanja koju smo koristili interno i nadamo se da dodaje u raspravu. Pa ću ti to vratiti, Eric.
Eric Kavanagh: To je fantastično. Ako možete samo malo pitanja. Ali svidjelo mi se što ste ga vratili na poslovnu razinu, jer na kraju dana sve je stvar u poslu. Sve je u tome da popravite stvari i osigurate da mudro trošite novac, a to je jedno od pitanja koje sam već vidio, pa će možda govornici htjeti razmisliti o tome koji je TCL prelazak Hadoop rute. Između njih postoji neko slatko mjesto, na primjer, korištenje alata za uredske police da biste radili stvari na neki tradicionalan način i koristeći nove setove alata, jer opet, razmislite, mnogo toga nije novo, već je samo vrsta koaliranje na novi način je, valjda, najbolji način da se to postavi.
Pa idemo naprijed i predstavimo našeg prijatelja Nikita Ivanova. Osnivač je i izvršni direktor GridGaina. Nikita, nastavit ću ti predati ključeve i vjerujem da si vani. Možete li me čuti Nikita?
Nikita Ivanov: Da, tu sam.
Eric Kavanagh: Odlično. Znači, pod je tvoj. Kliknite na taj slajd. Upotrijebite strelicu prema dolje i odnesite je. Pet minuta.
Nikita Ivanov: Na koji slajd kliknem?
Eric Kavanagh: Samo kliknite bilo gdje na tom klizaču, a zatim se za pomicanje koristite strelicom prema dolje na tipkovnici. Samo kliknite na sam klizač i upotrijebite strelicu prema dolje.
Nikita Ivanov: U redu, tako da samo nekoliko brzih prezentacija o GridGainu. Što radimo u kontekstu ovog razgovora? GridGain u osnovi proizvodi računalni softver za memoriju, a dio platforme koju smo razvili je memorijski Hadoop akcelerator. U smislu Hadoopa, skloni smo razmišljati o sebi kao stručnjacima za Hadoop performanse. Ono što radimo, povrh naše osnovne računalne platforme za memoriju koja se sastoji od tehnologija poput mrežnog prijenosa podataka, protoka memorije i računarske mreže, moglo bi uključiti Hadoop akcelerator. To je vrlo jednostavno. Bilo bi lijepo ako možemo razviti nekakvo plug-and-play rješenje koje se može instalirati pravo u instalaciju Hadoop. Ako vi, programer MapReduce-a, trebate pojačati bez potrebe za pisanjem novog softvera ili promjenom koda ili promjenom ili u osnovi imate minimalnu promjenu konfiguracije u Hadoop grupi. To smo razvili.
U osnovi, memorijski akcelerator Hadoop zasnovan je na optimiziranju dviju komponenti u Hadoop ekosustavu. Ako mislite na Hadoop, on se pretežno temelji na HDFS-u, koji je datotečni sustav. MapReduce, što je okvir za paralelno vođenje natjecanja na vrhu datotečnog sustava. Da bismo optimizirali Hadoop, optimiziramo oba ova sustava. Razvili smo datotečni sustav u memoriji koji je u potpunosti kompatibilan, 100% kompatibilan plug-and-play, sa HDFS-om. Možete se pokretati umjesto HDFS, možete pokrenuti i na vrhu HDFS-a. Također smo razvili in-memory MapReduce koji je plug-and-play kompatibilan s Hadoop MapReduce, ali postoji puno optimizacija o tome kako tijek rada MapReduce i kako radi raspored na MapReduceu.
Ako pogledate, na primjer, ovaj slajd, gdje prikazujemo vrstu snopa duplikata. Na lijevoj strani imate svoj tipični operativni sustav s GDM-om, a na vrhu ovog dijagrama imate aplikacijski centar. U sredini imate Hadoop. I Hadoop se opet temelji na HDFS-u i MapReduceu. Dakle, na ovom dijagramu to i predstavlja, ono što mi nekako ugrađujemo u Hadoop niz. Opet, to je plug-and-play; ne morate mijenjati nijedan kod. Jednostavno djeluje na isti način. Na sljedećem dijapozitivu pokazali smo u osnovi kako smo optimizirali tijek rada MapReduce. To je vjerojatno najzanimljiviji dio jer vam daje najviše prednosti kada pokrećete zadatke MapReduce.
Uobičajeni MapReduce, kad predajete posao, a na lijevoj strani je dijagram, uobičajena aplikacija. Znači obično predajete posao, a posao ide tragaču posla. 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. Hvala vam.
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.
Hvala.
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. Let's see. 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? S velikom moći dolazi velika odgovornost. 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. To je otprilike to. 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.
S tim ćemo se pozdraviti, ljudi. 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. Doviđenja.
