Opomba urednika: To je prepis spletnega prenosa v živo. Spletno oddajo si lahko v celoti ogledate tukaj.
Eric Kavanagh: Dame in gospod, čas je, da se preudarite! Čas je za TechWise, popolnoma novo oddajo! Ime mi je Eric Kavanagh. Bom vaš moderator za našo uvodno epizodo TechWise. Točno tako. To je partnerstvo Techopedia in Bloor Group, seveda slave Inside Analysis.
Ime mi je Eric Kavanagh. Moderiral bom ta res zanimiv in vpleten dogodek, ljudje. Kopali bomo globoko v tkanju, da bomo razumeli, kaj se dogaja s to veliko stvarjo, imenovano Hadoop. Kaj je slon v sobi? Imenuje se Hadoop. Poskušali bomo ugotoviti, kaj to pomeni in kaj se dogaja z njim.
Najprej hvala našim sponzorjem, GridGain, Actian, Zettaset in DataTorrent. Ob koncu tega dogodka bomo od vsake od njih dobili nekaj kratkih besed. Imeli bomo tudi vprašanja in vprašanja, zato ne bodite sramežljivi - kadarkoli pošljite svoja vprašanja.
Pokopali se bomo v podrobnosti in trdih vprašanj postavili našim strokovnjakom. In če govorimo o strokovnjakih, hej, tam so. Torej, slišali bomo od našega lastnega dr. Robina Bloorja in ljudje, zelo sem navdušen nad legendarnim Ray Wangom, glavnim analitikom in ustanoviteljem Constellation Research. Danes je na spletu, da nam predstavi svoje misli in je podoben Robinu, da je neverjetno raznolik in se resnično osredotoča na veliko različnih področij in jih ima sposobnost sintetizirati ter resnično razumeti, kaj se dogaja tam na vsem tem področju informacijske tehnologije in upravljanje podatkov.
Torej, tam je tisti mali simpatični slon. Kot vidite, je na začetku poti. Zdaj se šele začne, samo nekako se začne, celotna Hadoop stvar. Seveda v letih 2006 ali 2007, ko je bil sproščen v odprtokodno skupnost, se je dogajalo veliko, ljudje. Zgodilo se je ogromno. Pravzaprav bi rad pripovedoval zgodbo, tako da bom hitro delil namizje, vsaj mislim, da sem. Naredimo hitro deljenje namizja.
Prikažem vam to samo nore, nore zgodbe. Tako je Intel vložil 740 milijonov dolarjev za nakup 18 odstotkov Cloudere. Mislil sem si in sem kot: "sveti božič!" Začel sem se ukvarjati z matematiko in takole je: "Vrednost je 4, 1 milijarde dolarjev." Razmislimo o tem za sekundo. Mislim, če je WhatsApp vreden dve milijardi dolarjev, predvidevam, da bi bil Cloudera vreden 4, 1 milijarde dolarjev, kajne? Mislim, zakaj pa ne? Nekatere od teh številk so ravno danes skozi okno, ljudje. Mislim, navadno glede naložb imate EBITDA in vse te druge različne mehanizme, več prihodkov in tako naprej. No, nekaj hektarjev več prihodkov bo doseglo 4, 1 milijarde dolarjev za Cloudero, ki je super podjetje. Ne razumite me narobe - tam je nekaj zelo, zelo pametnih ljudi, vključno s fantom, ki je začel celotno norost Hadoop, Doug Cutting, tam je - veliko zelo inteligentnih ljudi, ki delajo veliko resnično, resnično kul stvari, a bistvo je, da je 4, 1 milijarde dolarjev, to je veliko denarja.
Torej, tu je nekakšen očiten očiten trenutek, ko mi gre zdaj skozi glavo, kar je čip, Intel. Njihovi oblikovalci čipov prinašajo ogled čipa, optimiziranega za Hadoop - tako si moram misliti, ljudje. To je samo moje ugibanje. To je samo govorica, ki prihaja od mene, če hočete, ampak nekako ima smisel. In kaj vse to pomeni?
Torej, tukaj je moja teorija. Kaj se dogaja? Veliko teh stvari ni novo. Masivna vzporedna obdelava ni grozno nova. Vzporedna obdelava zagotovo ni nova. Že nekaj časa sem v svetu superračunalništva. Veliko teh stvari, ki se dogajajo, ni novo, vendar obstaja vrsta splošnega zavedanja, da obstaja nov način za napad na nekatere od teh težav. Kar se mi dogaja, če pogledate nekaj velikih prodajalcev Cloudere ali Hortonworks in nekaterih drugih fantov, kaj počnejo v resnici, če jih spustite do najbolj natančnega destiliranega nivoja, je razvoj aplikacij. To počnejo.
Oblikujejo nove aplikacije - nekatere vključujejo poslovno analitiko; nekateri vključujejo sisteme za polnjenje. Eden od naših prodajalcev, ki je govoril o tem, tovrstne stvari počne ves dan, danes na razstavi. Če je grozno nov, je odgovor spet "ne v resnici", vendar se dogajajo velike stvari in osebno se mi zdi, da se dogaja, če bo Intel naredil to ogromno naložbo. Danes gledajo na svet in vidijo, da je danes nekakšen monopolni svet. Tam je Facebook in so iz slabega MySpacea premagali samo smrček. LinkedIn je izlulil smreko revnih Kdo je kdo. Torej si oglejte okoli sebe in to je ena storitev, ki danes prevladuje v vseh teh različnih prostorih v našem svetu in mislim, da je Intel vrgel vse svoje čipe na Cloudero in jo poskusil povzdigniti na vrh zložbe - to je samo moja teorija.
Tako, kot sem rekel, ljudje bomo imeli dolgo sejo vprašanj, zato ne bodite sramežljivi. Vsa vprašanja lahko kadar koli pošljete. To lahko storite s to komponento Q&A na konzoli za spletno oddajanje. In s tem želim priti do naše vsebine, ker imamo veliko stvari, da se prebijemo.
Torej, Robin Bloor, naj ti predam ključe in tla so tvoja.
Robin Bloor: V redu, Eric, hvala za to. Pripeljimo plešeče slone. Pravzaprav je zanimiva stvar, da so sloni edini kopenski sesalci, ki dejansko ne morejo skočiti. Vsi ti sloni v tej konkretni grafiki imajo vsaj eno nogo na tleh, zato mislim, da je to izvedljivo, toda do neke mere so to očitno Hadoop sloni, tako zelo, zelo sposobni.
Resnično vprašanje, za katero menim, da ga je treba razpravljati in ga je treba pošteno obravnavati. Treba je razpravljati, preden greste kam drugam, to je, da se začnete resnično pogovarjati o tem, kaj pravzaprav je Hadoop.
Ena od stvari, ki je absolutno na osnovi človeške igre, je trgovina s ključnimi vrednostmi. Včasih smo imeli trgovine s ključnimi vrednostmi. Včasih smo jih imeli v IBM-ovem osnovnem okviru. Imeli smo jih na miniračunalnikih; DEC VAX je imel datoteke IMS. ISAM zmogljivosti so bile na skoraj vsakem miniračunalniku, ki ga lahko dobite. Toda nekje okoli poznih osemdesetih je prišel Unix in Unix v resnici ni imel shranjevanja ključa in vrednosti. Ko ga je razvil Unix, so se razvili zelo hitro. V resnici se je zgodilo, da so tam prodajalci baz podatkov, zlasti Oracle, prodali vaše baze podatkov, da so skrbeli za vse podatke, ki jih želite upravljati na Unixu. Izkazalo se je, da sta Windows in Linux enaka. Torej, industrija je bila najboljši del 20 let brez splošne namenske ključ-vrednosti. No, zdaj je spet. Ne samo, da je nazaj, ampak je prilagodljiv.
Zdaj mislim, da je res temelj tega, kar Hadoop v resnici je in do neke mere določa, kam gre. Kaj nam je všeč v trgovinah s ključnimi vrednostmi? Tisti, ki ste stari toliko kot jaz in se pravzaprav spomnite, da delate s trgovinami s ključnimi vrednostmi, se zavedate, da bi jih lahko precej uporabljali za neformalno postavitev baze podatkov, vendar le neformalno. Veste, da metapodatki hitro vrednotijo shranjevanje v programski kodi, toda dejansko bi lahko naredili to zunanjo datoteko in lahko, če bi želeli začeti obravnavati shrambo ključ-vrednost, nekoliko podobno kot bazo podatkov. Seveda seveda ni imel vse tiste možnosti za obnovitev, ki jo ima baza podatkov, in baz podatkov ni imel prav veliko stvari, vendar je bila to razvijalna uporabna funkcija za razvijalce in to je eden od razlogov, da mislim da se je Hadoop izkazal za tako priljubljenega - preprosto zato, ker so ga hitri koderi, programerji in razvijalci. Spoznali so, da trgovina ni samo ključ-vrednost, ampak je trgovina s ključnimi vrednostmi. Veliko lepi v nedogled. Te lestvice sem poslal na tisoče strežnikov, tako da je to res velika stvar pri Hadoopu, to je tisto, kar je.
Povrhu ima tudi MapReduce, ki je algoritem za paralelizacijo, vendar to po mojem mnenju ni pomembno. Torej, veste, Hadoop je kameleon. Ne gre samo za datotečni sistem. Videla sem različne vrste zahtevkov za Hadoop: gre za tajno bazo podatkov; ni skrivna baza podatkov; je običajna trgovina; to je analitično orodje; gre za okolje ELT; je orodje za čiščenje podatkov; je skladišče podatkov za pretočne platforme; gre za arhivsko trgovino; je zdravilo za raka in tako naprej. Večina teh stvari res ne drži za vaniljo Hadoop. Hadoop je verjetno prototipiranje - zagotovo je okolje za izdelavo prototipov za bazo podatkov SQL, vendar ga v resnici nima, če postavite starostni prostor s starostnim katalogom prek Hadoopa, imate nekaj, kar je videti kot baza podatkov, vendar v resnici ni kar bi kdo poklical v bazo podatkov glede na sposobnost. Veliko teh zmogljivosti, zagotovo jih lahko dobite na Hadoopu. Gotovo jih je veliko. Pravzaprav lahko najdete nek vir Hadoopa, sam Hadoop pa ni tisto, kar bi poimenoval operativno kaljen, zato je posel s Hadoopom resnično ne bi šlo za nič drugega, kot da bi morali imeti tretjega -party izdelke za izboljšanje.
Torej, ko govorim o tebi, lahko vržeš le nekaj vrstic, ko govorim o Hadoopu pretiravanju. Prvič, sposobnost poizvedb v realnem času, dobro veste, da je v realnem času vrsta poslovnega časa, v resnici pa je skoraj vedno uspešnost v nasprotnem primeru. Mislim, zakaj bi inženir v realnem času? Hadoop tega res ne počne. Naredi nekaj, kar je blizu v realnem času, vendar v resnici ne počne stvari. Streaming sicer deluje, vendar toka ne počne tako, kot bi lahko rekel, da lahko to storijo resnično kritične vrste aplikacijskih platform. Obstaja razlika med bazo podatkov in shrambo, ki jo je mogoče odstraniti. Če ga sinhronizirate s Hadoop-om, dobite shranjeno zbirko podatkov. To je nekako kot baza podatkov, vendar ni isto kot baza podatkov. Po mojem mnenju Hadoop v svoji izvorni obliki v resnici sploh ne velja za bazo podatkov, ker primanjkuje kar nekaj stvari, ki bi jih morala imeti baza podatkov. Hadoop naredi veliko, vendar to ne počne posebej dobro. Spet je sposobnost tam, vendar smo na daleč od tega, da bi dejansko imeli hitro zmogljivost na vseh teh področjih.
Druga stvar, ki jo je treba razumeti o Hadoopu, je, da je nekako daleč, odkar je bil razvit. Razvili so ga že v zgodnjih dneh; razvili so ga, ko smo imeli strežnike, ki so imeli dejansko samo en procesor na strežniku. Nikoli nismo imeli večjedrnih procesorjev in je bil zgrajen za zagon omrežij, lansirne mreže in severs. Eden izmed oblikovalskih ciljev Hadoopa je bil, da dela nikoli ne izgubijo. In res je šlo za okvaro diska, saj če imate na stotine strežnikov, potem obstaja verjetnost, da če imate diske na strežnikih, obstaja verjetnost, da boste imeli na voljo nekaj časa, kot je 99.8. To pomeni, da boste v povprečju odpovedali enega od teh strežnikov enkrat na 300 ali 350 dni, en dan v letu. Če bi jih imeli na stotine, bi bila verjetnost, da bi kateri koli dan v letu prišli do okvare strežnika.
Hadoop je bil izdelan posebej za reševanje te težave - tako da v primeru, da kar koli ne uspe, na vsakem določenem strežniku naredi posnetke vsega, kar se dogaja, in lahko povrne paketno opravilo, ki se izvaja. In to je bilo vse, kar se je v Hadoopu dejansko kdajkoli dogajalo, in sicer je bila serijska opravila in to je res uporabna sposobnost, je treba reči. Nekatera skupna opravila, ki so se izvajala - zlasti pri Yahoo-u, kjer se mi je Hadoop nekako rodil - bi kandidirala dva ali tri dni, in če ne bi uspela po enem dnevu, resnično niste želeli izgubiti dela to je bilo storjeno. To je bila oblikovalska točka za razpoložljivostjo na Hadoopu. Ne bi imenovali tako visoke razpoložljivosti, lahko pa bi jo imenovali velika razpoložljivost za serijska opravila paketov. Verjetno je tako videti. Visoka razpoložljivost je vedno konfigurirana glede na značilnosti delovnih linij. Trenutno je Hadoop mogoče konfigurirati samo za res serijska serijska opravila glede tovrstne obnovitve. Podjetništvo o visoki razpoložljivosti je verjetno najbolje razmišljati v smislu transakcijskega vseživljenjskega učenja. Verjamem, da Hadoop, če na to ne gledate kot na stvar v resničnem času, tega še ne počne. Verjetno je daleč od tega.
Ampak tukaj je Hadoop lepa stvar. Grafika na desni strani, ki ima seznam roba prodajalcev, in vse vrstice na njej kažejo povezave med temi prodajalci in drugimi izdelki v ekosistemu Hadoop. Če pogledate na to, je to izjemno impresiven ekosistem. Precej izjemno. Očitno se pogovarjamo z veliko prodajalci glede na njihove zmožnosti. Med prodajalci, s katerimi sem govoril, je nekaj res izjemnih zmogljivosti uporabe Hadoopa in spomina, načina uporabe Hadoopa kot stisnjenega arhiva, uporabe Hadoopa kot okolja ETL in tako naprej. Ampak res, če izdelek dodate v sam Hadoop, v določenem prostoru deluje izjemno dobro. Čeprav sem kritičen do rodnega Hadoopa, do Hadoopa nisem kritičen, ko mu dejansko dodate nekaj moči. Po mojem mnenju Hadoopova priljubljenost zagotavlja njegovo prihodnost. S tem mislim, tudi če vsaka vrstica kode, napisana do zdaj na Hadoopu, izgine, ne verjamem, da bo izginil API HDFS. Z drugimi besedami, mislim, da je datotečni sistem, API, tu ostal in po možnosti tudi PRI, planer, ki ga pregleduje.
Ko to resnično pogledate, je to zelo pomembna sposobnost, in to bom nekako poskušal v hipu, toda druga stvar, ki je, recimo, navdušuje ljudi o Hadoopu, je celotna odprtokodna slika. Torej je vredno preučiti, kakšna je odprtokodna slika v smislu tega, kar menim kot resnično sposobnost. Medtem ko lahko Hadoop in vse njegove komponente zagotovo počnejo to, kar imenujemo dolžine podatkov - ali kot raje poimenujem, rezervoar podatkov - je zagotovo zelo dobro območje za prikazovanje podatkov v organizacijo ali za zbiranje podatkov v organizaciji - izjemno dobro za peskovnike in za ribolov podatkov. Zelo dober je kot razvojna platforma za izdelavo prototipov, ki bi jo lahko uvedli konec dneva, vendar kot razvojno okolje veste, da je tam vse, kar želite. Kot arhivska trgovina ima precej vse, kar potrebujete, in seveda ni drago. Mislim, da se od Hadoopa ne bi smeli ločiti nobene od teh dveh stvari, čeprav niso formalno sestavni deli Hadoopa, če želite. Spletni klin je prinesel ogromno analitike v odprtokodni svet in veliko te analitike se zdaj izvaja na Hadoopu, ker vam daje priročno okolje, v katerem lahko dejansko vzamete veliko zunanjih podatkov in začnete predvajati na analitičnem peskovniku.
In potem imate na voljo odprtokodne zmogljivosti, oboje je strojno učenje. Obe sta izredno močni v smislu, da izvajata močne analitične algoritme. Če sestavite te stvari, boste dobili jedra zelo, zelo pomembne zmogljivosti, ki je tako ali drugače zelo verjetno - ali se bo razvijala sama ali pa bodo prodajalci prišli, da zapolnijo manjkajoče koščke - zelo verjetno bo še dolgo in zagotovo mislim, da strojno učenje že zelo močno vpliva na svet.
Evolucija Hadoopa, YARN je spremenila vse. Kar se je zgodilo, je bil MapReduce precej privezan na zgodnji datotečni sistem HDFS. Ko je bil YARN predstavljen, je v prvi izdaji ustvaril sposobnost načrtovanja. Od prve izdaje ne pričakujete izjemno sofisticiranega razporeda, vendar je to pomenilo, da zdaj ni več nujno okolje zakrpa. Šlo je za okolje, v katerem je bilo mogoče razporediti več delovnih mest. Takoj se je pojavila cela vrsta prodajalcev, ki so se držali stran od Hadoopa - pravkar so prišli in se povezali z njim, ker so potem lahko samo gledali kot okolje načrtovanja v datotečnem sistemu in so lahko naslovili stvari na to. Obstajajo celo prodajalci podatkovnih baz, ki so svoje baze podatkov implementirali na HDFS, ker samo vzamejo motor in ga preprosto postavijo na HDFS. S kaskadno in z YARN postane zelo zanimivo okolje, ker lahko ustvarite zapletene delovne tokove prek HDFS in to resnično pomeni, da lahko začnete razmišljati o njej kot o resnično platformi, ki lahko hkrati izvaja več opravil in se sama potiska k točki delati kritične stvari. Če boste to storili, boste verjetno morali kupiti nekaj komponent drugih proizvajalcev, kot so varnost in tako naprej, in tako naprej, kar Hadoop pravzaprav nima revizijskega računa, da bi zapolnil vrzeli, vendar stopi v točko, ko lahko tudi z izvornim odprtokodnim kodrom počneš nekaj zanimivih stvari.
Glede na to, kje mislim, da bo Hadoop v resnici šel, osebno verjamem, da bo HDFS postal privzeti datotečni sistem po meri in bo zato postal OS, operacijski sistem, za omrežje za pretok podatkov. Mislim, da ima v tem ogromno prihodnost in mislim, da se tam ne bo ustavilo. In dejansko mislim, da ekosistem samo pomaga, ker skoraj vsi, vsi prodajalci v vesolju, dejansko tako ali drugače vključujejo Hadoop in to samo omogočajo. V zvezi s še eno točko, ki jo je vredno ovrednotiti Hadoop, ali ne gre za zelo dobro platformo in paralelizacijo. Če dejansko pogledate, kaj počne, je tisto, kar dejansko počne, da redno fotografira na vsakem strežniku, ko izvaja svoje opravila MapReduce. Če bi načrtovali resnično hitro paralelizacijo, ne bi počeli nič takega. V bistvu najbrž ne bi uporabljal MapReduce sam. MapReduce je samo tisto, za kar bi rekel, da je napol sposoben paralelizma.
Obstajata dva pristopa k vzporednosti: eden je s cevovodnimi procesi, drugi pa z deljenjem podatkov MapReduce in dela delitev podatkov, tako da obstaja veliko delovnih mest, pri katerih MapReduce dejansko ne bi bil najhitrejši način, vendar bo, vam vzporedim paralelizem in tega ne morete odvzeti. Ko imate veliko podatkov, takšna moč ponavadi ni tako uporabna. Preja, kot sem že rekel, je zelo mlada zmožnost načrtovanja.
Hadoop je, nekako potegne črto na pesku, Hadoop ni skladišče podatkov. To je tako daleč od skladišča podatkov, da je skorajda nesmiseln predlog, če bi rekli, da je tako. V tem diagramu je prikazano na vrhu nekakšen pretok podatkov, ki iz Hadoopovega rezervoarja podatkov preide v bazo podatkov o velikem obsegu, kar bomo v resnici storili, poslovno skladišče podatkov. Prikažem starejše zbirke podatkov, vnašam podatke v podatkovno skladišče in sprostim dejavnost, ustvarjam baze podatkov iz skladišča podatkov, vendar to je dejansko slika, ki jo začenjam videti, in rekel bi, da je to kot prva generacija kaj se zgodi s podatkovnim skladiščem s Hadoopom. Če pa sami pogledate podatkovno skladišče, se zavedate, da pod podatkovnim skladiščem imate optimizator. Na voljo imate razporejene poizvedbene delavce v zelo številnih procesih, ki sedijo nad zelo velikim številom diskov. To se dogaja v podatkovnem skladišču. To je pravzaprav takšna arhitektura, ki je zgrajena za podatkovno skladišče, in za izdelavo česa takega je potrebno veliko časa, Hadoop pa tega sploh nima. Torej Hadoop ni skladišče podatkov in po mojem mnenju to ne bo postal kmalu.
Ima ta relativni rezervoar podatkov in nekako je videti zanimivo, če na svet gledate le na vrsto dogodkov, ki se pretakajo v organizacijo. To je prikazano na levi strani tega diagrama. Če gre skozi filtriranje in usmerjanje, in stvari, ki jih je treba uporabiti za pretakanje, se odstranijo iz aplikacij za pretakanje, vse drugo pa gre naravnost v zbiralnik podatkov, kjer je pripravljeno in očiščeno, nato pa ga ETL prenese bodisi do enega samega podatka skladišče ali skladišče logičnih podatkov, sestavljeno iz več motorjev. To je po mojem mnenju naravna razvojna linija za Hadoop.
V zvezi z ETW je ena od stvari, ki jih je vredno posebej poudariti, ta, da je bilo podatkovno skladišče dejansko premaknjeno - ni to, kar je bilo. Zagotovo danes pričakujete, da obstaja hierarhična zmožnost na hierarhične podatke o tem, kako ljudje ali nekateri ljudje kličejo dokumente v podatkovno zbirko. To je JSON. Mogoče bodo omrežne poizvedbe, ki vključujejo grafične baze podatkov, po možnosti analitike. Torej, k čemur gremo je ETW, ki ima dejansko bolj zapleteno delovno obremenitev kot tisti, ki smo je navajeni. Tako je zanimivo, ker na nek način pomeni, da je podatkovno skladišče še bolj prefinjeno, in zaradi tega bo še daljši čas, preden se bo Hadoop približal. Pomen skladišča podatkov se širi, vendar še vedno vključuje optimizacijo. Imeti morate sposobnost optimizacije, ne le nad poizvedbami, ampak nad vsemi temi dejavnostmi.
To je res, res. To je vse, kar sem hotel povedati o Hadoopu. Mislim, da lahko predam Rayu, ki nima diapozitivov, vendar vedno dobro govori.
Eric Kavanagh: Vzela bom diapozitive. Tu je naš prijatelj, Ray Wang. Torej, Ray, kaj misliš o vsem tem?
Ray Wang: Mislim, da je bila to verjetno ena najbolj sočasnih in odličnih zgodovin trgovin s ključnimi vrednostmi in kamor je Hadoop šel v razmerje do podjetja, ki je zunaj, zato se vedno poslušam veliko, ko poslušam Robina.
Pravzaprav imam en diapozitiv. Tu lahko pospremim en drsnik.
Eric Kavanagh: Pojdite naprej in kliknite na, kliknite Start in pojdite na skupno rabo namizja.
Ray Wang: Razumeš. Pravzaprav bom delil. Aplikacijo si lahko ogledate sami. Poglejmo, kako gre.
Ves ta pogovor o Hadoopu in potem gremo globoko v pogovor o tehnologijah, ki so tam in kamor hodi Hadoop, in velikokrat ga rad vzamem nazaj, da se resnično pogovori o podjetju. Veliko stvari, ki se dogaja na tehnološki strani, je res ta del, kjer smo govorili o skladiščih podatkov, upravljanju informacij, kakovosti podatkov, obvladovanju teh podatkov in tako ponavadi to vidimo. Če pogledate ta graf tukaj na samem dnu, je zelo zanimivo, da vrste posameznikov, na katere naletimo, govorijo o Hadoopu. Imamo tehnologe in podatkovne znanstvenike, ki pozirajo in so navdušeni, in običajno gre za vire podatkov, kajne? Kako obvladamo vire podatkov? Kako to spravimo v prave stopnje kakovosti? Kaj naredimo z upravljanjem? Kaj lahko storimo za ujemanje različnih vrst virov? Kako ohranjamo rodove? In vsa taka razprava. In kako lahko iz našega Hadoopa iztržimo več SQL? Tako da se ta del dogaja na tej ravni.
Potem je na strani informacij in orkestracije tu zanimivo. Začenjamo vezati rezultate tega vpogleda, ki ga dobimo ali ga povlečemo nazaj od poslovnih procesov? Kako ga povežemo z vsemi modeli metapodatkov? Ali povezujemo pike med predmeti? In tako novi glagoli in razprave o tem, kako uporabljamo te podatke, se premikajo od tistega, kar smo tradicionalno v svetu CRUD: ustvarjamo, beremo, posodabljamo, brišemo, v svet, ki razpravlja o tem, kako sodelujemo ali delimo ali sodelujemo oz. všeč ali potegniti nekaj.
Tu začnemo videti veliko navdušenja in inovacij, zlasti o tem, kako te podatke potegniti in jih ceniti. To je tehnološka razprava pod rdečo črto. Nad to rdečo črto dobimo zelo vprašanja, ki smo si jih vedno želeli zastaviti in eno od njih, ki ga vedno zastavljamo, je, na primer, morda je vprašanje v trgovini na drobno za vas: "Zakaj se rdeči puloverji prodajajo bolje v Alabami kot modri puloverji v Michiganu? " Lahko bi razmislili in si rekli: "To je nekako zanimivo." Vidite ta vzorec. Zastavimo to vprašanje in se sprašujemo: "Hej, kaj počnemo?" Mogoče gre za državne šole - Michigan proti Alabami. V redu, razumem, vidim, kam gremo. In tako začnemo dobivati poslovno stran hiše, ljudi v financah, ljudi, ki imajo tradicionalne BI-zmožnosti, ljudi v marketingu in ljudi v HR-ju, ki pravijo: "Kje so moji vzorci?" Kako pridemo do teh vzorcev? Tako na strani Hadoop vidimo še en način inovativnosti. Resnično gre za to, kako hitreje posodobimo spoznanja. Kako vzpostavimo tovrstne povezave? Vse to velja za ljudi, ki delajo podobno, ad: tech, ki v bistvu poskušajo povezati oglase in ustrezne vsebine, karkoli, od omrežij za zbiranje ponudb v realnem času do postavitve kontekstualnih oglasov in oglaševalskih akcij.
Torej je zanimivo. Vidite napredovanje Hadoopa iz: "Hej, tukaj je tehnološka rešitev. Tukaj moramo storiti, da te informacije razkrijemo ljudem." Potem, ko prestopi čez poslovni del, je tukaj zanimivo. To je vpogled. Kje je predstava? Kje je odbitek? Kako napovedujemo stvari? Kako prevzamemo vpliv? In nato to pripeljite na zadnjo raven, kjer dejansko vidimo še en niz Hadoop-ovih inovacij, ki se dogajajo okoli sistemov odločanja in ukrepov. Kaj je naslednje najboljše dejanje? Torej veste, da se modri puloverji bolje prodajajo v Michiganu. Sedite na toni modrih puloverjev v Alabami. Očitna stvar je: "Ja, no, dajmo to odpremiti tja." Kako to storimo? Kaj je naslednji korak? Kako to vrnemo nazaj? Mogoče je naslednje najboljše dejanje, morda je predlog, morda je nekaj, kar vam pomaga preprečiti težavo, morda tudi ni ukrepanje, kar je dejanje samo po sebi. Tako začnemo videti tovrstne vzorce. In lepota tega, kar govoriš o trgovinah s ključnimi vrednostmi, Robin, je v tem, da se dogaja tako hitro. Zgodi se tako, kot da o tem še nismo razmišljali.
Verjetno bi rekel v zadnjih petih letih, ko smo se pobrali. Začeli smo razmišljati v smislu, kako lahko spet izkoristimo prodajalne s ključnimi vrednostmi, toda šele v zadnjih petih letih ljudje na to gledajo zelo drugače in kot da se tehnološki cikli ponavljajo v 40-letnih vzorcih, tako da je to prijazno zabavne stvari, kjer gledamo v oblak in prav tako si delim čas z mainframeom. Ogledamo si Hadoop in podobno trgovino s ključnimi vrednostmi - morda gre za podatkovno ploščo, manj kot za shranjevanje podatkov - in spet začnemo videti te vzorce. Tisto, kar zdaj poskušam, je razmisliti, kaj so ljudje delali pred 40 leti? Kateri pristopi in tehnike in metodologije so se uporabljali, ki so jih omejile tehnologije, ki so jih ljudje imeli? To je nekako gonilo tega miselnega procesa. Ko gremo skozi širšo sliko Hadoopa kot orodja, ko se vrnemo nazaj in razmišljamo o poslovnih posledicah, je to nekakšna pot, ki jo ljudje običajno prehodimo, tako da lahko vidite, katere dele, katere dele so v podatkih pot odločitev. To je samo nekaj, kar sem želel deliti. To je nekakšno razmišljanje, ki ga uporabljamo notranje in upamo, da dodaja k razpravi. Torej, vrnem ti jo nazaj, Eric.
Eric Kavanagh: To je fantastično. Če se lahko držite za kaj vprašanj. Všeč mi je bilo, da ste ga ponovno prevzeli na poslovno raven, ker je na koncu vse skupaj posel. Vse je v tem, da se stvari lotite in poskrbite, da boste denar porabili pametno in to je eno izmed vprašanj, ki sem jih že videl, zato bodo govorci morda želeli razmisliti o tem, kaj je TCL o Hadoop poti. Vmes je nekaj dobrega, na primer z uporabo orodij za pisarniške police, da stvari počnete na nek tradicionalen način in z uporabo novih sklopov orodij, ker še enkrat, premislite, veliko teh stvari ni novo, ampak je le nekakšno Po prepričanju je najboljši način, da se to združi.
Pa pojdimo naprej in predstavimo našega prijatelja Nikita Ivanova. Je ustanovitelj in izvršni direktor GridGain. Nikita, šel bom naprej in ti izročil ključe in verjamem, da si tam. Me slišiš Nikita?
Nikita Ivanov: Ja, tu sem.
Eric Kavanagh: Odlično. Torej, tla so vaša. Kliknite na to diapozitiv. Uporabite puščico navzdol in jo odnesite. Pet minut.
Nikita Ivanov: Na kateri diapozitiv kliknem?
Eric Kavanagh: Samo kliknite kjer koli na ta drsnik in za premikanje uporabite puščico navzdol na tipkovnici. Samo kliknite na drsnik in uporabite puščico navzdol.
Nikita Ivanov: V redu, torej le nekaj hitrih diapozitivov o GridGainu. Kaj počnemo v okviru tega pogovora? GridGain v osnovi proizvaja računalniško programsko opremo v pomnilniku, del platforme, ki smo jo razvili, pa je pospeševalec Hadoop v pomnilniku. Glede Hadoopa smo ponavadi o sebi razmišljali kot o Hadoopovih strokovnjakih. Kar počnemo v bistvu na naši osnovni računalniški platformi v pomnilniku, ki je sestavljena iz tehnologij, kot so podatkovno omrežje, pomnilniško pretakanje in računalniške mreže, bi lahko vključili pospeševalnik Hadoop. To je zelo preprosto. Lepo bi bilo, če bomo razvili nekakšno rešitev plug-and-play, ki jo je mogoče namestiti prav v namestitvi Hadoop. Če vi, razvijalec MapReduce, potrebujete spodbudo, ne da bi morali napisati novo programsko opremo ali spremeniti kodo ali spremeniti ali v bistvu spremeniti vse minimalne konfiguracije v skupini Hadoop. To smo razvili.
V osnovi memorijski pospeševalnik Hadoop temelji na optimizaciji dveh komponent v ekosistemu Hadoop. Če pomislite na Hadoop, pretežno temelji na HDFS, ki je datotečni sistem. MapReduce, ki je okvir za izvajanje tekmovanj vzporedno na datotečnem sistemu. Da bi optimizirali Hadoop, optimiziramo oba sistema. Razvili smo datotečni sistem v pomnilniku, ki je popolnoma združljiv, 100% združljiv plug-and-play, s HDFS. Lahko tečete namesto HDFS, lahko tečete na vrhu HDFS. Razvili smo tudi MapReduce v pomnilniku, ki je plug-and-play združljiv s Hadoop MapReduce, vendar je veliko optimizacij, kako poteka pretok dela MapReduce in kako deluje urnik na MapReduce.
Če pogledate na primer na ta diapozitiv, kjer prikazujemo vrsto podvajanja. Na levi strani imate svoj značilni operacijski sistem z GDM, na vrhu tega diagrama pa aplikacijski center. Na sredini imaš Hadoop. In Hadoop spet temelji na HDFS in MapReduce. Torej to na tem diagramu predstavlja, da tisto, kar smo nekako vgradili v Hadoop-ov kup. Spet je plug-and-play; vam ni treba spremeniti nobene kode. Samo deluje na enak način. Na naslednjem diapozitivu smo v bistvu prikazali, kako smo optimizirali potek dela MapReduce. To je verjetno najbolj zanimiv del, ker vam prinese največ prednosti, ko zaženete opravila MapReduce.
Običajni MapReduce, ko oddate opravilo, na levi strani pa je diagram, je običajna aplikacija. Običajno oddajate delo in gre v sledilnik. 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. Pa poglejmo. 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? Z veliko močjo prihaja 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. O tem gre. 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 tem se bomo poslovili, ljudje. 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. Adijo.
