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Eric Kavanagh: Senyores i senyors, ha arribat el moment de fer-se prudent! És el moment de TechWise, un nou espectacle! Em dic Eric Kavanagh. Vaig a ser la vostra moderadora per al nostre episodi inaugural de TechWise. És exactament. Aquesta és una associació de Techopedia i el grup Bloor, per descomptat, de fama de Inside Analysis.
Em dic Eric Kavanagh. Moderarà aquest esdeveniment realment interessant i implicat, persones. Anem a cavar profundament al teixit per entendre què passa amb aquesta gran cosa que es diu Hadoop. Què és l'elefant de l'habitació? Es diu Hadoop. Intentarem esbrinar què significa i què passa amb això.
En primer lloc, moltes gràcies als nostres patrocinadors, GridGain, Actian, Zettaset i DataTorrent. A prop del final d’aquest esdeveniment, obtindrem unes breus paraules de cadascuna d’elles. També tindrem una pregunta i una pregunta, així que no siguis tímid: envieu les vostres preguntes en qualsevol moment.
Anem a aprofundir en els detalls i a llançar les nostres preguntes als nostres experts. I parlant dels experts, heu, hi són. Així doncs, estarem sentint a conèixer del nostre propi doctor Robin Bloor, i la gent, estic molt emocionada de comptar amb el llegendari Ray Wang, analista principal i fundador de Constellation Research. Avui està en línia per pensar-nos i és com Robin, que és increïblement divers i realment se centra en moltes àrees diferents i que té la capacitat de sintetitzar-les i d’entendre realment què hi ha en tot aquest camp de la tecnologia de la informació. i gestió de dades.
Per tant, hi ha aquell elefant tan bonic. Com es veu, a l'inici del camí. Tot just comença, és una cosa que comença, tota aquesta cosa Hadoop. Per descomptat, el 2006 o el 2007, suposo, és quan es va publicar a la comunitat de codi obert, però hi ha hagut moltes coses, persones. Hi ha hagut una gran evolució. De fet, vull presentar la història, així que faré un compartit d'escriptori ràpid, almenys crec que ho sóc. Fem un compartiment d'escriptori ràpid.
Estic mostrant aquesta gent meravellosa i boja de la història. Per tant, Intel va invertir 740 milions de dòlars per comprar el 18 per cent de Cloudera. Vaig pensar i sóc com "Sant Nadal!" Vaig començar a fer matemàtiques i és com "és una valoració de 4.100 milions de dòlars". Pensem en això durant un segon. Vull dir, si WhatsApp val 2.000 milions de dòlars, suposo que Cloudera també podria valer 4.100 milions de dòlars, oi? Vull dir, per què no? Alguns d'aquests números són avui a la finestra, persones. Vull dir que, normalment, en termes d’inversions, teniu EBITDA i tots aquests altres mecanismes diversos, múltiples d’ingressos i així successivament. Doncs bé, serà una quantitat màxima d’ingressos fins a arribar a 4.100 milions de dòlars per Cloudera, que és una empresa fantàstica. No m'equivoquisqueu: hi ha gent molt, molt intel·ligent, inclòs el tipus que ha començat tota la mania de Hadoop, Doug Cutting, ell és allà; hi ha molta gent molt intel·ligent que està fent molt de debò. coses fantàstiques, però el que és bàsic és que els 4.100 milions de dòlars, que són molts diners.
Així que aquí té una mena de captiu moment evident per passar pel meu cap ara mateix, que és un xip, Intel. Els seus dissenyadors de xip porten a veure algun xip optimitzat per a Hadoop. Això és només la meva suposició. No és més que una remor, si vols, però té sentit. I què vol dir tot això?
Heus aquí la meva teoria. Que està passant? Moltes coses no són noves. El processament massiu paral·lel no és tremendament nou. El processament paral·lel segur que no és nou. Fa temps que estic al món de la supercomputació. Moltes coses que estan passant no són noves, però hi ha una mena de consciència general que hi ha una nova manera d’atacar algun d’aquests problemes. El que veig que està passant, si ens fixem en alguns dels grans venedors de Cloudera o Hortonworks i alguns d’aquests altres, el que fan realment si ho redueixes fins al nivell més destil·lat és el desenvolupament d’aplicacions. Això ho fan.
Dissenyen noves aplicacions, algunes d’elles inclouen analítica empresarial; Alguns només inclouen sistemes de sobrealimentació. Un dels nostres venedors que han parlat d’això, fa aquest tipus de coses durant tot el dia d'avui. Però si és terriblement nova, de nou la resposta és "no realment", però hi ha grans coses i, personalment, crec que el que passa amb Intel fent aquesta enorme inversió és un canvi de mercat. Miren el món actual i veuen que avui dia és una mena de monopoli. Hi ha Facebook i han tret els detalls al pobre MySpace. LinkedIn ha apallissat el pobre Who's Who. Així que mireu al nostre voltant i és un servei que domina tots aquests espais diferents al nostre món actual, i crec que la idea és que Intel vagi a llençar tots els seus xips a Cloudera i intentarà elevar-lo a la part superior de la pila. la meva teoria.
Així, doncs, com ja he dit, tindrem una llarga sessió de Q&A, així que no siguis tímid. Envieu les vostres preguntes en qualsevol moment. Podeu fer-ho amb aquest component de Q&A de la consola de transmissió web. I amb això vull arribar al nostre contingut perquè tenim moltes coses per fer-ho.
Per tant, Robin Bloor, deixa'm lliurar les claus i el pis és teu.
Robin Bloor: D'acord, Eric, gràcies per això. Agafem els elefants que ballen. És una cosa curiosa, de fet, que els elefants són els únics mamífers terrestres que realment no poden saltar. Tots aquests elefants d’aquest gràfic concret tenen almenys un peu a terra, de manera que suposo que és factible, però fins a cert punt, es tracta, evidentment, d’elefants Hadoop, tan molt, molt capaços.
La pregunta, realment, que crec que s’ha de discutir i s’ha de discutir amb tota honestedat. S'ha de discutir abans de passar a qualsevol altre lloc, que és començar a parlar realment del que és Hadoop.
Una de les coses que en resulta absolutament de la base del joc és el magatzem de valor clau. Teníem botigues de valor clau. Les teníem al mainframe IBM. Els teníem als minicomputadors; DEC VAX tenia fitxers IMS. Hi havia capacitats ISAM que eren gairebé tots els minicomputadors en els quals podeu posar les mans. Però, a la fi dels anys 80, va entrar Unix i Unix no tenia ni un magatzem de valor clau. Quan Unix la va desenvolupar, es van desenvolupar molt ràpidament. El que va passar realment va ser que els venedors de bases de dades, en particular Oracle, van entrar al vapor i van vendre les vostres bases de dades per tenir cura de les dades que us agradaria gestionar a Unix. Windows i Linux van resultar iguals. Així, la indústria va passar durant la millor part de vint anys sense una botiga de valor clau de propòsit general. Bé, ja ha tornat. No només ha tornat, sinó que es pot escalar.
Ara, crec que realment és el fonament del que és realment Hadoop i, fins a cert punt, determina cap a on va. Què ens agrada de les botigues de valor clau? Els que sou tan vells com jo i recordeu en realitat treballar amb botigues de valor clau s’adonen que podríeu utilitzar-los pràcticament per configurar de manera informal una base de dades, però només de manera informal. Sabeu que les metadades valoren ràpidament els magatzems en el codi del programa, però realment podríeu fer que fos un fitxer extern i podríeu si voleu començar a tractar una botiga de valor de clau com una base de dades. Però, per descomptat, no tenia tota la capacitat de recuperació que té una base de dades i no tenia moltes coses que tenen actualment les bases de dades, però era una funció molt útil per als desenvolupadors i aquesta és una de les raons per les quals crec. que Hadoop ha resultat tan popular, simplement perquè han estat codificadors, programadors i desenvolupadors que s’afanyen. Es van adonar que no només és un valor clau de la botiga, sinó que és una tenda de valor de claus. Fa una escala pràcticament indefinida. He enviat aquestes escales a milers de servidors, així que això és realment important amb Hadoop.
A més, té MapReduce, que és un algoritme de paral·lelització, però realment, segons la meva opinió, no és important. Així que, ja ho sabeu, Hadoop és un camaleó. No es tracta només d’un sistema d’arxius. He vist diversos tipus de reclamacions per a Hadoop: és una base de dades secreta; no és cap base de dades secreta; és una botiga comuna; és una caixa d’eines analítica; és un entorn ELT; és l'eina de neteja de dades; és un magatzem de dades de plataformes de streaming; és una botiga d’arxiu; és una cura per al càncer, etc. La majoria d’aquestes coses no són certes per a la vainilla Hadoop. Hadoop és probablement un prototipat - certament és un entorn de prototipat per a una base de dades SQL, però realment no ho és, si poseu espai d’edat amb el catàleg d’edats sobre Hadoop, teniu una cosa que sembla una base de dades, però no ho és realment. allò que qualsevol podria anomenar una base de dades en termes de capacitat. Moltes d’aquestes capacitats, segur que les podeu aconseguir a Hadoop. Hi ha, certament, molts. De fet, podeu obtenir alguna font d’Hadoop, però Hadoop no és el que jo anomenaria endurit operativament i, per tant, l’acord sobre Hadoop, realment no estaria en cap altra cosa, és que necessiteu tenir un tercer. -partits de productes per millorar-lo.
De manera que, parlant de tu només pots llançar unes quantes línies, ja que parlo de la superació Hadoop. En primer lloc, la capacitat de consulta en temps real, ja sabeu que en temps real és un tipus de temps de negoci, realment, gairebé sempre és crític amb el rendiment. Vull dir, per què feries enginyeria en temps real? Hadoop no ho fa realment. Fa una cosa propera a temps real, però realment no fa coses en temps real. Fa streaming, però no fa streaming de manera que jo diria que poden fer plataformes de streaming d’aplicacions de tipus realment crític per a missions. Hi ha una diferència entre una base de dades i una botiga esborrable. Sincronitzar-lo amb Hadoop us proporciona un magatzem de dades esborrable. És així com una base de dades, però no és el mateix que una base de dades. Hadoop, en la seva forma nativa, segons la meva opinió, realment no és qualificable com a base de dades, ja que és poc prou algunes de les bases de dades. Hadoop fa molt, però no ho fa especialment bé. Un cop més, hi ha la capacitat, però estem lluny de tenir una capacitat ràpida en totes aquestes àrees.
L’altra cosa que s’ha d’entendre sobre Hadoop és que ha sortit un llarg camí des que es va desenvolupar. Es va desenvolupar als primers dies; es va desenvolupar quan teníem servidors que en realitat només tenien un processador per servidor. Mai vam tenir processadors multi-core i es va crear per controlar les reixetes, llançar reixes i filtres. Un dels objectius de disseny d’Hadoop era no perdre mai la feina. I això es referia realment a la fallada del disc, perquè si teniu centenars de servidors, és probable que, si teniu discos en els servidors, la probabilitat és que tingueu una disponibilitat a l’altura d’alguna cosa com 99.8. D'aquesta manera, tindreu com a mínim un error d'un d'aquests servidors un cop cada 300 o 350 dies, un dia a l'any. Així que si en tingueu centenars, la probabilitat que hi hagués algun error de servidor es produís un error.
Hadoop es va crear específicament per solucionar el problema, de manera que, en cas que alguna cosa fallés, es realitzin instantànies de tot el que passa, a cada servidor en concret i es pugui recuperar la tasca per lots que s’executa. I això va ser tot el que en realitat va treballar en Hadoop: feines per lots i això és una capacitat realment útil. Algunes de les tasques que s’estaven gestant, sobretot a Yahoo, on crec que Hadoop va néixer, tindrien una durada de tres o tres dies i, si fallava al cap d’un dia, realment no voldríeu perdre la feina. això s’havia fet. De manera que aquest va ser el punt de disseny de la disponibilitat a Hadoop. No anomenaríeu aquesta alta disponibilitat, però podríeu anomenar-la d'alta disponibilitat per a treballs per lots de sèrie. Probablement és la manera de mirar-ho. L’alta disponibilitat sempre es configura segons les característiques de la línia de treball. De moment, Hadoop només es pot configurar per a tasques de lots de sèrie realment pel que fa a aquest tipus de recuperació. Probablement es pensi millor en una alta disponibilitat empresarial en termes de LLP transaccional. Crec que si no ho veieu com una cosa real, Hadoop encara no ho fa. És probable que estigui molt lluny de fer això.
Però aquí teniu el bonic d’Hadoop. El gràfic de la part dreta que té una llista de venedors al marge i totes les línies que hi ha a ella indiquen connexions entre aquests venedors i altres productes de l'ecosistema Hadoop. Si t'ho fixes, és un ecosistema increïblement impressionant. És força notable. Evidentment, parlem amb molts venedors quant a les seves capacitats. Entre els venedors amb els que he parlat, hi ha algunes funcions realment extraordinàries d’utilitzar Hadoop i la memòria, la manera d’utilitzar Hadoop com a arxiu comprimit, d’utilitzar Hadoop com a entorn ETL, etcètera. Però realment, si afegeix el producte a Hadoop mateix, funciona molt bé en un espai determinat. Així que, mentre jo sóc crític amb Hadoop nadiu, jo no sóc crític amb Hadoop quan realment hi afegiu una mica de poder. Segons la meva opinió, la popularitat de Hadoop garanteix el seu futur. Amb això, tot i que cada línia de codi escrita fins ara a Hadoop desapareix, no crec que desaparegui l’API HDFS. Dit d'una altra manera, crec que el sistema de fitxers, API, és aquí per quedar-se i, possiblement, YARN, el planificador que mira per sobre.
Quan realment ho mireu, això és una capacitat molt important i en faré una mica de cera sobre això en un minut, però l’altra cosa que sigui, diguem-ne, la gent emocionant sobre Hadoop és tota la imatge de codi obert. Per tant, val la pena explorar quina és la imatge de codi obert en termes del que considero una capacitat real. Si bé Hadoop i tots els seus components, certament, poden fer allò que anomenem longituds de dades, o com prefereixo anomenar-ho, reservori de dades, és sens dubte una molt bona zona de posada en escena de dades a l’organització o de recopilar dades de l’organització. per a caixes de sorra i per a les dades de pesca. Està molt bé com a plataforma de desenvolupament de prototips que podríeu implementar al final del dia, però ja sabeu com a entorn de desenvolupament tot el que voleu. Com a botiga d’arxiu, té tot el que necessites i, per descomptat, no és car. No crec que ens haguéssim de divorciar d’alguna de les dues coses d’Hadoop tot i que no són formals, si voleu, components d’Hadoop. La falca en línia ha aportat una gran quantitat d’analítiques al món de codi obert i ara s’executa una gran quantitat d’analítiques a Hadoop perquè això us proporciona un entorn convenient en el qual realment podeu prendre moltes dades externes i començar a jugar. en una caixa analítica.
I, a continuació, teniu les capacitats de codi obert, que són l'aprenentatge automàtic. Els dos són extremadament potents en el sentit que implementen algoritmes analítics potents. Si ajuntes aquestes coses, obtindràs els nuclis d’alguna capacitat molt important, que és d’una manera o d’una altra molt probable, tant si es desenvolupa per si mateixa com si venen venedors per emplenar les peces que falten. és molt probable que continuï durant molt de temps i, certament, crec que l’aprenentatge automàtic ja està tenint un impacte molt gran en el món.
L’evolució d’Hadoop, YARN va canviar tot. El que havia passat és que MapReduce estava bastant soldat amb el sistema de fitxers HDFS inicial. Quan es va introduir YARN, va crear una capacitat de programació en el seu primer llançament. No espereu la programació extremadament sofisticada des del primer llançament, però significava que ara ja no era necessàriament un entorn remens. Era un entorn en el qual es podien programar múltiples feines. Tan aviat això passava, hi havia tota una sèrie de venedors que s’havien allunyat d’Hadoop: només entraven i es connectaven a ell perquè aleshores només podien mirar-lo com l’entorn de programació d’un sistema d’arxius i podrien dirigir-se a coses ella Fins i tot hi ha proveïdors de bases de dades que han implementat les seves bases de dades en HDFS, perquè només agafen el motor i només el posen a HDFS. Amb cascada i amb YARN, es converteix en un entorn molt interessant, ja que podeu crear fluxos de treball complexos mitjançant HDFS i això significa realment que podeu començar a pensar-hi com una plataforma que pot executar diversos treballs simultàniament i s’està pressionant cap al punt de fer coses relacionades amb la missió Si voleu fer-ho, probablement haureu de comprar components de tercers com la seguretat, etcètera, que Hadoop en realitat no té un compte d'auditoria per emplenar els buits, però arribar fins al punt on, fins i tot amb un codi obert natiu, pots fer algunes coses interessants.
Pel que fa a on penso que Hadoop realment anirà, crec personalment que HDFS es convertirà en un sistema de fitxers de escala per defecte i, per tant, es convertirà en el sistema operatiu, el sistema operatiu, per a la graella per al flux de dades. Crec que té un futur enorme en això i no crec que s’aturi allà. I crec que de fet l'ecosistema només ajuda perquè gairebé tots, tots els venedors de l'espai, estan integrant Hadoop d'una manera o altra i ho permeten. Pel que fa a un altre punt que val la pena, en termes d’ova Hadoop, és que no és una plataforma gaire bona més la paral·lelització. Si realment veieu el que fa, el que realment fa és fer una instantània regularment a tots els servidors, ja que està executant els seus treballs MapReduce. Si haguéssiu de dissenyar una paral·lelització realment ràpida, no faríeu res així. De fet, probablement no utilitzeu MapReduce pel seu compte. MapReduce és només el que diria mig capaç de paral·lelisme.
Hi ha dos enfocaments del paral·lelisme: un és mitjançant processos de canalització i l’altre és dividint les dades MapReduce i fa la divisió de dades, de manera que hi ha moltes feines on MapReduce no seria en realitat la manera més ràpida de fer-ho, però sí donar-li paral·lelisme i no hi ha res. Quan tens moltes dades, aquest tipus de potència no sol ser tan útil. YARN, com ja he dit, és una capacitat de programació molt jove.
Hadoop és, una mena de dibuixar la línia a la sorra, Hadoop no és un magatzem de dades. És tan lluny de ser un magatzem de dades que és gairebé un suggeriment absurd dir que ho és. En aquest diagrama, el que mostro a la part superior és un tipus de flux de dades, que va des d’un dipòsit de dades Hadoop fins a una base de dades d’escala magnífica, que és el que realment farem, un magatzem de dades empresarial. Estic mostrant bases de dades antigues, introducció de dades al magatzem de dades i activitat de descàrrega creant bases de dades de descàrrega des del magatzem de dades, però realment és una imatge que començo a aparèixer, i diria que és com la primera generació de què passa amb el magatzem de dades amb Hadoop. Però si ens fixem en el magatzem de dades, t’adones que a sota del magatzem de dades tens un optimitzador. Teniu distribuïdors de consulta distribuïts en molts processos asseguts en molts nombrosos discos. Això és el que passa en un magatzem de dades. En realitat, aquest tipus d’arquitectura està construïda per a un magatzem de dades i es necessita molt temps per crear una cosa així, i Hadoop no té res d’això. Així doncs, Hadoop no és un magatzem de dades i no es convertirà en un, al meu parer, aviat.
Té un reservori de dades relatiu i té una aparença interessant si només mireu el món com una sèrie d'esdeveniments que flueixen a l'organització. Això és el que mostro a la part esquerra d’aquest diagrama. Si el que passa és filtrar i encaminar les funcions i les coses necessàries per a la transmissió en streaming, es desprèn de les aplicacions de streaming i tota la resta entra directament al dipòsit de dades on es prepara i neteja, i passa a ETL a una sola dada. magatzem o un magatzem de dades lògic format per múltiples motors. Aquesta és, al meu parer, una línia de desenvolupament natural per a Hadoop.
Pel que fa a la ETW, una de les coses que convé destacar és que el magatzem de dades es va mudar realment, no és el que era. Certament, avui dia, espereu que hi hagi una capacitat jeràrquica per dades jeràrquiques del que persones, o algunes persones, anomenen els documents al magatzem de dades. Això és JSON. Possiblement, les consultes de xarxa són bases de dades gràfiques, possiblement analítiques. Aleshores, el que ens dirigim és un ETW que realment té una càrrega de treball més complexa que els que estem acostumats. Així que és interessant perquè, en certa manera, significa que el magatzem de dades s’està sofisticant i, per tant, encara passarà més temps abans que Hadoop s’hi apropi. El significat de magatzem de dades s’estén, però encara inclou l’optimització. Heu de tenir una capacitat d’optimització, no només sobre les consultes ara, sinó sobre totes aquestes activitats.
Això és realment. Això és tot el que volia dir sobre Hadoop. Crec que puc lliurar a Ray, que no té cap diapositiva, però sempre és bo per parlar.
Eric Kavanagh: Agafaré les diapositives. Hi ha el nostre amic, Ray Wang. Ray, què penses en tot això?
Ray Wang: Ara, crec que probablement va ser una de les històries més sucintes i més importants de les botigues de valor clau i on Hadoop ha estat en relació amb empreses que no estan fora de casa, així que sempre aprenc molt quan escolto Robin.
De fet, tinc una diapositiva. Puc aparèixer una diapositiva aquí.
Eric Kavanagh: només cal que endavant i feu clic a, feu clic a Iniciar i aneu a compartir l'escriptori.
Ray Wang: Ho tens, hi vas? En realitat compartiré Podeu veure l'aplicació en si. Vegem com va
Tota aquesta xerrada sobre Hadoop i, a continuació, aprofundim en la conversa sobre les tecnologies que hi ha i cap a Hadoop, i moltes vegades només m’agrada tornar a tenir una discussió empresarial. Moltes de les coses que passen pel costat de la tecnologia són realment aquesta peça on hem estat parlant sobre magatzems de dades, gestió de la informació, qualitat de les dades, dominar aquestes dades i, per tant, solem veure-ho. Per tant, si mireu aquest gràfic aquí a la part inferior, és molt interessant que els tipus d’individus en què parlem d’Hadoop. Tenim els tecnòlegs i els científics de dades que fan geologia, que fan molta il·lusió, i normalment es tracta de fonts de dades, oi? Com dominem les fonts de dades? Com podem aconseguir això en els nivells adequats de qualitat? Què fem sobre la governança? Què podem fer per combinar diferents tipus de fonts? Com mantenim el llinatge? I tot aquest tipus de discussió. I com podem treure més SQL del nostre Hadoop? Així, aquesta part està passant a aquest nivell.
Aleshores, des del punt d'informació i orquestració, és aquí on és interessant. Comencem a relacionar els resultats d’aquest coneixement que estem aconseguint o els estem tirant d’esquena als processos empresarials? Com podem relacionar-la amb qualsevol tipus de models de metadades? Estem connectant els punts entre objectes? I així els nous verbs i discussions sobre com fem servir aquestes dades, passant del que tradicionalment som en un món de CRUD: crear, llegir, actualitzar, suprimir, a un món que està discutint sobre com ens comprometem o compartim o col·laborem o agradar o tirar alguna cosa.
És aquí on comencem a veure molta il·lusió i innovació, sobretot sobre com treure aquesta informació i posar-la en valor. Aquesta és la discussió basada en la tecnologia per sota de la línia vermella. Per sobre d'aquesta línia vermella, arribem a les mateixes preguntes que sempre hem volgut fer i una d'elles que sempre plantegem és com, per exemple, potser la pregunta al detall al vostre detall és com: "Per què es venen millors jerseis vermells? a Alabama que a suèteres blaves a Michigan? " Hi podríeu pensar i dir: "Això és interessant." Ja veieu aquest patró. Ens fem aquesta pregunta i ens preguntem: "Ei, què fem?" Potser es tracta d’escoles estatals: Michigan vers Alabama. D'acord, ho aconsegueixo, veig cap a on anem. Així doncs, comencem a situar-nos en el negoci de la casa, persones en finances, persones que tenen capacitats tradicionals de BI, persones en màrqueting i persones en RRHH que diuen: "On són els meus patrons?" Com podem arribar a aquests patrons? I veiem una altra manera d’innovar per part de l’Hadoop. Es tracta, realment, de com actualitzem la superfície amb més rapidesa. Com fem aquest tipus de connexions? Va de la mateixa manera que la gent que fa, com ara: tecnologia ad: que bàsicament tracta de connectar anuncis i contingut rellevant des de qualsevol cosa, des de xarxes d’ofertes en temps real fins a anuncis contextuals i col·locació d’anuncis i fent això sobre la marxa.
Així que és interessant. Veieu la progressió de Hadoop a "Hey, aquí és la solució tecnològica. Aquí és el que hem de fer per donar aquesta informació a la gent". Aleshores, a mesura que passa la línia de negoci, és aquí on resulta interessant. És el coneixement. On és l’actuació? On és la deducció? Com predim les coses? Com intervenim? I, a continuació, portem-ho fins a aquest darrer nivell on realment veiem un altre conjunt d’innovacions Hadoop que estan passant entorn dels sistemes i accions de decisió. Quina serà la següent millor acció? Ja sabeu que els jerseis blaus es venen millor a Michigan. Estàs assegut en un munt de jerseis blaus a Alabama. El que és obvi és: "Sí, bé, anem enviats per allà". Com ho fem? Quin és el següent pas? Com enllaçem això? Potser la següent millor acció, potser és un suggeriment, potser és quelcom que us ajudi a prevenir un problema, potser tampoc és cap acció, que és una acció en si mateixa. Comencem a veure que sorgeixen aquest tipus de patrons. I la bellesa d’això en què dius sobre les botigues de valor clau, Robin, és que està passant tan de pressa. S’està passant de la manera en què no hi hem estat pensant.
Probablement diria que en els darrers cinc anys vam recollir-la. Comencem a pensar en com podem tornar a aprofitar les botigues de valor clau, però és justament en els últims cinc anys, la gent ho veu molt diferent i és com si els cicles tecnològics es repeteixin en els patrons de 40 anys. una cosa curiosa on estem mirant núvol i jo sóc com un temps de temps compartit. Estem mirant a Hadoop i com el magatzem de valor clau (potser és una data mart, menys que un magatzem de dades), i així tornem a veure aquests patrons. El que estic intentant fer ara és pensar què feien les persones ara fa 40 anys? Quins enfocaments i tècniques i metodologies s’estaven aplicant que eren limitades per les tecnologies que la gent tenia? Aquesta és la forma d’impulsar aquest procés de pensament. Així, mentre passem per la imatge més gran d’Hadoop com a eina, quan tornem enrere i pensem en les implicacions empresarials, aquest és el tipus de camí que recorrem normalment les persones perquè pugueu veure quines peces, quines parts hi ha a les dades. ruta de decisions. És només una cosa que volia compartir. És una mena de pensament que hem estat fent servir internament i que esperem afegir a la discussió. Així que us ho torno a passar, Eric.
Eric Kavanagh: Això és fantàstic. Si podeu mantenir alguna pregunta i pregunta. Però em va agradar que el vau tornar a portar al nivell empresarial, perquè al final, tot tracta tot el negoci. Es tracta d’aconseguir fer les coses i assegurar-vos que esteu gastant diners amb prudència i aquesta és una de les preguntes que ja vaig veure, així que els altaveus potser voldreu pensar què és el TCL d’anar a la ruta Hadoop. Hi ha algun lloc dolç entre els altres, per exemple, utilitzar eines de la prestatgeria de l’oficina per fer coses d’alguna manera tradicional i fer servir els nous conjunts d’eines, perquè, de nou, penseu-hi, moltes coses no són noves, sinó una mena de La coalició d'una nova manera és, suposo, la millor manera de dir-ho.
Així que anem endavant i presentem la nostra amiga Nikita Ivanov. És el fundador i conseller delegat de GridGain. Nikita, vaig a avançar-vos i lliurar-vos les claus i crec que sou fora. Em pots sentir Nikita?
Nikita Ivanov: Sí, estic aquí.
Eric Kavanagh: Excel·lent. Així que el pis és teu. Feu clic a aquesta diapositiva. Feu servir la fletxa cap avall i traieu-la. Cinc minuts.
Nikita Ivanov: quina diapositiva faig clic?
Eric Kavanagh: simplement feu clic en qualsevol lloc de la diapositiva i, seguidament, feu servir la fletxa cap avall del teclat per moure-us. Només cal fer clic a la diapositiva en si i fer servir la fletxa cap avall.
Nikita Ivanov: Molt bé algunes diapositives ràpides sobre GridGain. Què fem en el context d’aquesta conversa? GridGain produeix bàsicament un programari informàtic en memòria i part de la plataforma que hem desenvolupat és l’accelerador Hadoop en memòria. Pel que fa a Hadoop, solem pensar en nosaltres mateixos com a especialistes en rendiment Hadoop. El que fem, fonamentalment, a la part superior de la nostra plataforma informàtica de memòria integrada que consisteix en tecnologies com la graella de dades, la transmissió de memòria i les xarxes de càlcul, podríem connectar l’accelerador Hadoop. És molt senzill. Estaria bé que puguem desenvolupar algun tipus de solució plug-and-play que es pugui instal·lar directament a la instal·lació de Hadoop. Si vostè, el desenvolupador de MapReduce, necessita un impuls sense necessitat d’escriure cap nou programari o canvi de codi o canvi, o bàsicament té un canvi de configuració mínim al clúster de Hadoop. Això és el que vam desenvolupar.
Fonamentalment, l’accelerador Hadoop en memòria es basa en l’optimització de dos components en l’ecosistema Hadoop. Si penseu en Hadoop, es basa principalment en HDFS, que és el sistema d’arxius. El MapReduce, que és el marc per executar les competicions en paral·lel a la part superior del sistema d'arxius. Per optimitzar l’Hadoop, optimitzem tots dos sistemes. Hem desenvolupat un sistema de fitxers en memòria que és completament compatible, 100% compatible amb plug-and-play, amb HDFS. Podeu executar en lloc de HDFS, podeu executar-lo a sobre de HDFS. I també vam desenvolupar MapReduce a la memòria que és compatible amb plug-in-play amb Hadoop MapReduce, però hi ha moltes optimitzacions sobre com funciona el flux de MapReduce i com funciona el calendari del MapReduce.
Si us fixem, per exemple en aquesta diapositiva, on mostrem el tipus de pila de duplicacions. A la part esquerra, teniu el sistema operatiu típic amb GDM i, a la part superior d’aquest diagrama, teniu el centre d’aplicacions. Al centre teniu l'Hadoop. I Hadoop torna a basar-se en HDFS i MapReduce. De manera que això representa en aquest diagrama, que és el que som de la incrustació a la pila Hadoop. De nou, és plug-and-play; no heu de canviar cap codi. Només funciona de la mateixa manera. A la diapositiva següent, vam mostrar essencialment com optimitzem el flux de treball MapReduce. Probablement aquesta és la part més interessant perquè us proporciona l’avantatge al executar els treballs MapReduce.
El típic MapReduce, quan envieu el treball, i a la part esquerra hi ha un diagrama, hi ha l'aplicació habitual. Així, normalment envieu la feina i la tasca es dirigeix a un rastrejador de treballs. 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. Gràcies.
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.
Thanks.
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. Anem a veure. 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? Amb un gran poder ve una gran responsabilitat. 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. That's about it. 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.
Amb això, us acomiadarem, amics. 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. Adeu.
