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Eric Kavanagh: Dámy a páni, je čas na múdrosť! Je čas na úplne novú show TechWise! Volám sa Eric Kavanagh. Budem vaším moderátorom našej úvodnej epizódy TechWise. Presne tak. Toto je partnerstvo Techopedia a Bloor Group, samozrejme, slávy Inside Analysis.
Volám sa Eric Kavanagh. Ľudia, moderujem túto skutočne zaujímavú a zúčastnenú udalosť. Budeme kopať hlboko do väzenia, aby sme pochopili, čo sa deje s touto veľkou vecou zvanou Hadoop. Čo je slon v miestnosti? Volá sa Hadoop. Pokúsime sa prísť na to, čo to znamená a čo sa s tým deje.
V prvom rade ďakujem našim sponzorom, GridGain, Actian, Zettaset a DataTorrent. Na konci tejto udalosti dostaneme od každého z nich pár krátkych slov. Budeme mať aj otázky a odpovede, takže sa nemusíte hanbiť - kedykoľvek pošlite svoje otázky.
Vykopneme do detailov a hodíme tvrdé otázky našim odborníkom. A keď už hovoríme o odborníkoch, hej, sú tam. Takže, budeme počuť od nášho vlastného Dr. Robina Bloora a ľudia, som veľmi rád, že mám legendárneho Ray Wanga, hlavného analytika a zakladateľa Constellation Research. Dnes je online, aby nám dal svoje myšlienky a je ako Robin, že je neuveriteľne rozmanitý a skutočne sa zameriava na veľa rôznych oblastí a má schopnosť ich syntetizovať a skutočne porozumieť tomu, čo sa tu deje v tejto oblasti informačných technológií. a správa údajov.
Takže je tu malý roztomilý slon. Ako vidíte, je na začiatku cesty. Je to len začiatok, je to len začiatok, celá táto vec Hadoop. Samozrejme, myslím si, že v roku 2006 alebo 2007 to bolo, keď bol prepustený do komunity s otvoreným zdrojom, ale veľa vecí sa deje, ľudia. Došlo k obrovskému vývoju. V skutočnosti chcem uviesť príbeh, takže sa chystám rýchlo zdieľať plochu, aspoň si myslím, že som. Urobme rýchle zdieľanie pracovnej plochy.
Ukazujem ti toto bláznivé, bláznivé príbehy. Preto spoločnosť Intel investovala 740 miliónov dolárov, aby kúpila 18 percent spoločnosti Cloudera. Pomyslel som si a som rád, "Sväté Vianoce!" Začal som robiť matematiku a je to ako: „Je to ocenenie 4, 1 miliardy dolárov.“ Zamyslime sa nad tým na chvíľu. Myslím, že ak WhatsApp má hodnotu 2 miliardy dolárov, myslím, že by Cloudera mohla mať hodnotu 4, 1 miliardy dolárov, však? Myslím, prečo nie? Niektorí z týchto čísel sú dnes práve mimo okna. Myslím tým, že zvyčajne máte z hľadiska investícií EBITDA a všetky tieto rôzne rôzne mechanizmy, násobky výnosov atď. No, bude to sakra z viacerých výnosov, keď sa pre spoločnosť Cloudera, ktorá je úžasnou spoločnosťou, dostane 4, 1 miliardy dolárov. Nechápte ma zle - sú tam nejakí veľmi, veľmi inteligentní ľudia, vrátane toho, kto začal celú hádku s Hadoopom, Doug Cutting, je tam - veľa veľmi inteligentných ľudí, ktorí robia veľa naozaj, naozaj super veci, ale konečný výsledok je, že 4, 1 miliardy dolárov, to je veľa peňazí.
Takže tu je akýsi okamih, ktorý je zrejmý v zajatí, keď som teraz prešiel hlavou, čo je čip, Intel. Ich návrhári čipov prinášajú nejaké optimalizované čipy optimalizované pre Hadoop - musím si to myslieť, ľudia. To je len môj odhad. To je len zvesť, ktorá pochádza odo mňa, ak chceš, ale dáva to zmysel. A čo to všetko znamená?
Takže tu je moja teória. Čo sa deje? Mnohé z týchto vecí nie sú nové. Masívne paralelné spracovanie nie je úplne nové. Paralelné spracovanie určite nie je nové. Chvíľu som vo svete superpočítačov. Mnohé z týchto vecí, ktoré sa dejú, nie sú nové, existuje však všeobecná informovanosť o tom, že existuje nový spôsob, ako zaútočiť na niektoré z týchto problémov. Čo sa deje, vidím, keď sa pozriete na niektorých veľkých predajcov spoločnosti Cloudera alebo Hortonworks a na niektorých z týchto ďalších ľudí, čo naozaj robia, ak to uvaríte na najzrnitejšiu destilovanú úroveň, je vývoj aplikácií. To robia.
Navrhujú nové aplikácie - niektoré z nich zahŕňajú obchodné analýzy; niektoré z nich obsahujú len systémy preplňovania. Jeden z našich predajcov, ktorý o tom hovoril, robí tento druh vecí celý deň, dnes v prehliadke. Ale ak je to strašne nové, opäť je odpoveďou „nie je to naozaj“, ale deje sa tu veľké veci a osobne si myslím, že to, čo sa deje s Intelom, že táto obrovská investícia je krokom na trhu. Pozerajú sa na svet dnes a vidia, že je to dnes taký monopolný svet. Je tu Facebook a zbili iba slabiny z chudobného MySpace. LinkedIn porazil chudobných z chudobných Kto je kto. Takže sa rozhliadate a je to jedna služba, ktorá dnes dominuje vo všetkých týchto rôznych priestoroch v našom svete, a myslím si, že myšlienka je, že Intel hodí všetky svoje žetóny na Clouderu a pokúsi sa ju povýšiť na vrchol stohu - to je len moja teória.
Takže ľudia, ako som povedal, budeme mať dlhé Q&A sedenie, takže sa nemusíte hanbiť. Svoje otázky môžete kedykoľvek poslať. Môžete tak urobiť pomocou tejto súčasti otázok a odpovedí vo vašej webovej vysielacej konzole. A s tým sa chcem dostať k nášmu obsahu, pretože tu máme veľa vecí, ktoré musíme prekonať.
Robin Bloor, dovoľte mi odovzdať kľúče a podlaha je na vás.
Robin Bloor: Dobre, Eric, vďaka za to. Vezmime tancujúcich slonov. Je skutočne zvláštne, že slony sú jedinými suchozemskými cicavcami, ktorí v skutočnosti nemôžu skočiť. Všetci títo sloni v tejto konkrétnej grafike majú na zemi aspoň jednu nohu, takže si myslím, že je to uskutočniteľné, ale do istej miery sú to samozrejme hadoopskí sloni, tak veľmi, veľmi schopní.
Otázka, o ktorej si myslím, že sa musí diskutovať, musí byť otvorená. Musí sa o ňom diskutovať skôr, ako pôjdete kamkoľvek inde, čo je skutočne začať hovoriť o tom, čo Hadoop v skutočnosti je.
Jednou z vecí, ktorú absolútne vychádza z princípu „človek hrať“, je uchovávanie kľúčov a hodnôt. Mali sme obchody s kľúčom a hodnotou. Zvyčajne sme ich mali na mainframe IBM. Mali sme ich na minipočítačoch; DEC VAX obsahoval súbory IMS. Existovali možnosti ISAM, ktoré boli takmer na každom minipočítači, na ktorý máte ruky. Ale niekedy okolo konca 80. rokov prišla Unix a Unix v skutočnosti nemal žiadny kľúč-hodnota. Keď ho Unix vyvinul, vyvíjali sa veľmi rýchlo. Skutočne sa stalo, že predajcovia databáz, najmä Oracle, sa tam naparili a predali vaše databázy, aby sa postarali o všetky údaje, ktoré chcete spravovať na Unixe. Windows a Linux sa ukázali byť rovnaké. Odvetvie teda prešlo najlepšiu časť 20 rokov bez toho, aby existoval univerzálny kľúč-hodnota. Teraz je späť. Nielen, že je späť, je škálovateľná.
Teraz si myslím, že v skutočnosti je to základ toho, čo Hadoop skutočne je, a do určitej miery určuje, kam to pôjde. Čo sa nám na obchodoch s kľúčovou hodnotou páči? Tí z vás, ktorí sú takí starí ako ja, a skutočne si pamätám, že pracujete s obchodmi s kľúčovou hodnotou, si uvedomujú, že by ste ich mohli použiť na neformálne zriadenie databázy, ale iba neformálne. Viete, že metadáta sa rýchlo ukladajú do hodnoty v programovom kóde, ale v skutočnosti by ste mohli vytvoriť externý súbor a mohli by ste, ak by ste chceli začať s ukladaním kľúč-hodnota spracovať trochu ako databáza. Ale samozrejme to nemalo všetko také obnovovacie schopnosti, ktoré má databáza a nemalo strašne veľa vecí, ktoré databázy teraz majú, ale pre vývojárov to bola skutočne užitočná funkcia, a to je jeden z dôvodov, prečo si myslím že Hadoop sa stal tak populárnym - jednoducho preto, že to boli kodéry, programátori, vývojári, ktorí sa k nemu rýchlo dostali. Uvedomili si, že to nie je len kľúč-hodnota obchodu, ale je to aj mierka-hodnota obchodu kľúč-hodnota. Do značnej miery sa zmenšuje na neurčito. Tieto stupnice som poslal na tisíce serverov, takže to je naozaj veľká vec, o Hadoop, to je ono.
Má tiež MapReduce, čo je algoritmus paralelizácie, ale podľa môjho názoru to vlastne nie je dôležité. Takže, Hadoop je chameleón. Nejde iba o súborový systém. Videl som rôzne tvrdenia týkajúce sa Hadoopu: je to tajná databáza; nejde o tajnú databázu; je to bežný obchod; je to analytický súbor nástrojov; je to prostredie ELT; je to nástroj na čistenie údajov; je to dátový sklad streamovacích platforiem; je to archívny obchod; je to liek na rakovinu a tak ďalej. Väčšina z týchto vecí v skutočnosti neplatí pre vanilku Hadoopovú. Hadoop je pravdepodobne prototypovanie - určite to je prototypingové prostredie pre SQL databázu, ale v skutočnosti to tak nie je, ak vložíte vekový priestor s vekovým katalógom nad Hadoop, máte niečo, čo vyzerá ako databáza, ale v skutočnosti to nie je čo by niekto nazval databázou z hľadiska schopností. Mnoho z týchto schopností, môžete ich určite dostať na Hadoop. Určite ich je veľa. V skutočnosti môžete získať nejaký zdroj Hadoopu, ale samotný Hadoop nie je tým, čo by som nazval operačne tvrdým, a preto dohoda o Hadoop, naozaj by som na ničom nebola, je, že musíte mať tretiu -party produkty na zlepšenie.
Takže, keď hovoríme o vás, môžete hádzať iba niekoľkými riadkami, keď hovorím o dosahu Hadoop. Po prvé, schopnosť dotazov v reálnom čase, dobre viete, že v reálnom čase je druh obchodného času, v skutočnosti takmer vždy je výkon kritický inak. Prečo by si robil technik v reálnom čase? Hadoop to v skutočnosti nerobí. Robí niečo, čo je takmer v reálnom čase, ale v skutočnosti to nerobí veci v reálnom čase. Poskytuje streaming, ale nerobí to tak, ako by som nazval naozajstne kritickým typom platforiem aplikačného streamingu. Existuje rozdiel medzi databázou a odštiepiteľným obchodom. Jeho synchronizácia s Hadoopom vám poskytne prehľadné úložisko údajov. Je to niečo ako databáza, ale nie je to rovnaké ako databáza. Hadoop vo svojej natívnej podobe sa podľa môjho názoru v skutočnosti vôbec nepovažuje za databázu, pretože nemá dosť vecí, ktoré by databáza mala mať. Hadoop robí veľa, ale nerobí to zvlášť dobre. Opäť platí, že schopnosť je tam, ale my sme spôsoby, ako od skutočne mať rýchly schopnosti vo všetkých týchto oblastiach.
Ďalšou vecou, ktorú treba pochopiť o spoločnosti Hadoop, je to, že od jej vývoja sa vyvinula dlhá cesta. Bola vyvinutá v prvých dňoch; bol vyvinutý, keď sme mali servery, ktoré v skutočnosti mali iba jeden procesor na server. Nikdy sme nemali viacjadrové procesory a bolo vyrobené tak, aby prešlo cez siete, štartovacie siete a stoky. Jedným z cieľov dizajnu Hadoopu bolo nikdy nestratiť prácu. A to naozaj bolo o zlyhaní disku, pretože ak máte stovky serverov, potom je pravdepodobné, že ak máte na serveroch disky, je pravdepodobné, že získate dostupnosť v čase dostupnosti napríklad 99, 8. To znamená, že v priemere dostanete zlyhanie jedného z týchto serverov raz za 300 alebo 350 dní, jeden deň v roku. Ak by ste ich mali stovky, pravdepodobnosť zlyhania servera by nastala v ktorýkoľvek deň v roku.
Hadoop bol špeciálne navrhnutý na vyriešenie tohto problému - takže v prípade, že by niečo zlyhalo, vyfotí sa všetko, čo sa deje, na každom konkrétnom serveri a môže obnoviť spustenú dávkovú úlohu. A to bolo všetko, čo sa v skutočnosti stalo na Hadoope, boli dávkové úlohy, a to je skutočne užitočná schopnosť, treba povedať. Niektoré z dávkových úloh, ktoré sa vykonávali - najmä v Yahoo, kde sa domnievam, že sa Hadoop narodil - by bežali dva alebo tri dni, a ak to zlyhá po dni, naozaj by ste nechceli prísť o prácu to sa stalo. To bol bod dizajnu za dostupnosťou na Hadoope. Túto vysokú dostupnosť by ste nenazvali, ale mohli by ste ju nazvať vysokou dostupnosťou pre sériové dávkové úlohy. To je asi spôsob, ako sa na to pozerať. Vysoká dostupnosť je vždy nakonfigurovaná podľa charakteristík pracovnej linky. V súčasnosti je možné Hadoop nakonfigurovať iba na skutočne sériové dávkové úlohy, pokiaľ ide o tento druh obnovy. Vysoká dostupnosť podniku je pravdepodobne najlepšia myšlienka z hľadiska transakčného LLP. Verím, že ak sa na to nepozeráte ako na vec v reálnom čase, Hadoop to ešte neurobí. Je to pravdepodobne ďaleko od toho.
Ale tu je krásna vec o Hadoopovi. Táto grafika na pravej strane, ktorá obsahuje zoznam predajcov po celom obvode a všetky riadky na nej, naznačujú prepojenia medzi týmito predajcami a inými výrobkami v ekosystéme Hadoop. Ak sa na to pozriete, je to neuveriteľne pôsobivý ekosystém. Je to pozoruhodné. O ich schopnostiach samozrejme hovoríme s mnohými dodávateľmi. Medzi predajcami, s ktorými som hovoril, existujú niektoré skutočne výnimočné možnosti použitia Hadoopu a in-memory, spôsobu použitia Hadoopu ako komprimovaného archívu, použitia Hadoopu ako prostredia ETL, atď. Ale naozaj, ak pridáte produkt do samotného Hadoopu, v určitom priestore to funguje veľmi dobre. Takže keď som kritický voči rodnému Hadoopovi, nie som voči Hadoopovi kritický, keď k nemu skutočne pridáte nejakú moc. Podľa môjho názoru druh popularity spoločnosti Hadoop zaručuje jej budúcnosť. Myslím tým, že aj keď každý riadok kódu, ktorý je doposiaľ napísaný na Hadoop, zmizne, neverím, že rozhranie HDFS API zmizne. Inými slovami, myslím, že súborový systém, API, je tu, aby zostal, a možno YARN, plánovač, ktorý naň pozerá.
Keď sa na to skutočne pozriete, je to veľmi dôležitá vlastnosť a asi o chvíľu to budem voskovať, ale druhou vecou, povedzme, vzrušujúcich ľudí o Hadoope, je celý obraz s otvoreným zdrojovým kódom. Takže stojí za to prejsť tým, čo je obrázok s otvoreným zdrojom, pokiaľ ide o to, čo považujem za skutočné schopnosti. Aj keď spoločnosť Hadoop a všetky jej súčasti určite dokážu urobiť to, čo nazývame dátové dĺžky - alebo ako to uprednostňujem, dátový rezervoár - určite je to veľmi dobrá pracovná oblasť na to, aby sa údaje mohli preniesť do organizácie alebo zbierať údaje v organizácii - veľmi dobrá pre karantény a údaje o rybolove. Je to veľmi dobré ako platforma na vývoj prototypov, ktorú by ste mohli implementovať na konci dňa, ale ako vývojové prostredie viete skoro všetko, čo chcete, je tam. Ako archívny obchod má skoro všetko, čo potrebujete, a samozrejme to nie je drahé. Nemyslím si, že by sme sa mali s Hadoopom rozviesť jednu z týchto vecí, aj keď nie sú formálne, ak sa vám páči, komponenty Hadoopu. Online klin priniesol obrovské množstvo analytických údajov do sveta otvorených zdrojov a veľa analytických nástrojov sa teraz spúšťa na serveri Hadoop, pretože vám poskytuje vhodné prostredie, v ktorom môžete skutočne vziať veľa externých údajov a začať hrať. v analytickej karanténe.
A potom máte možnosti otvoreného zdroja, ktoré sú strojovým učením. Obidva sú mimoriadne silné v tom zmysle, že implementujú výkonné analytické algoritmy. Ak dáte tieto veci dokopy, máte jadrá niektorých veľmi, veľmi dôležitých schopností, ktoré je tak či onak veľmi pravdepodobné - či už sa vyvinie samo o sebe alebo či predajcovia prichádzajú, aby vyplnili chýbajúce kúsky - je veľmi pravdepodobné, že bude pokračovať dlho a určite si myslím, že strojové učenie už má na svet veľmi veľký vplyv.
Vývoj Hadoop, YARN zmenil všetko. To, čo sa stalo, bolo, že MapReduce bol do veľkej miery privarený k skorému systému súborov HDFS. Keď bol predstavený YARN, vytvoril schopnosť plánovania vo svojom prvom vydaní. Neočakávate extrémne sofistikované plánovanie od prvého vydania, ale znamenalo to, že to už nie je nevyhnutne záplatové prostredie. Bolo to prostredie, v ktorom bolo možné naplánovať viacero úloh. Hneď ako sa to stalo, existovala celá skupina predajcov, ktorí sa držali ďalej od spoločnosti Hadoop - práve prišli a pripojili sa k nej, pretože potom sa na ňu mohli jednoducho pozerať ako na prostredie plánovania cez systém súborov a mohli adresovať veci ono. Existujú dokonca aj dodávatelia databáz, ktorí implementovali svoje databázy na HDFS, pretože jednoducho prevezmú motor a jednoducho ho odovzdajú na HDFS. Vďaka kaskádovaniu a YARN sa stáva veľmi zaujímavým prostredím, pretože môžete vytvárať komplexné pracovné toky cez HDFS, čo skutočne znamená, že môžete začať premýšľať o tom, že je to skutočne platforma, ktorá dokáže súčasne spúšťať viac úloh a tlačí sa smerom k bodu robiť kritické veci. Ak to urobíte, pravdepodobne budete musieť kúpiť nejaké komponenty tretích strán, ako napríklad zabezpečenie atď., Ktoré spoločnosť Hadoop v skutočnosti nemá audítorský účet na vyplnenie medzier, ale vy dostať sa do bodu, keď aj s natívnym otvoreným zdrojom môžete robiť zaujímavé veci.
Pokiaľ ide o miesto, ktoré si myslím, že spoločnosť Hadoop skutočne pôjde, osobne verím, že systém HDFS sa stane predvoleným súborovým systémom so zmenšeným rozsahom, a preto sa z neho stane OS, operačný systém, pre mriežku pre tok údajov. Myslím, že má v tom obrovskú budúcnosť a nemyslím si, že sa to zastaví. A v skutočnosti si myslím, že v skutočnosti ekosystém len pomáha, pretože do značnej miery všetci, všetci predajcovia vo vesmíre, skutočne integrujú Hadoop tak či onak a len to umožňujú. Pokiaľ ide o ďalší bod, ktorý stojí za to urobiť, pokiaľ ide o nadbytok Hadoop, nie je to veľmi dobrá platforma plus paralelizácia. Ak sa skutočne pozriete na to, čo robí, v skutočnosti to robí snímka pravidelne na každom serveri, keď vykonáva svoje úlohy MapReduce. Keby ste chceli navrhnúť skutočne rýchlu paralelizáciu, nič také by ste nerobili. V skutočnosti by ste MapReduce pravdepodobne nepoužívali sami. MapReduce je len to, čo by som povedal, že polovica je schopná paralelizmu.
Existujú dva prístupy k paralelizmu: jeden je spracovaním procesov a druhý rozdelením údajov MapReduce a robí rozdelenie údajov, takže existuje veľa úloh, kde by MapReduce v skutočnosti nebol najrýchlejším spôsobom, ako to urobiť, ale bude dá vám paralelizmus a nič z toho nebude brať. Ak máte veľa údajov, tento druh energie zvyčajne nie je taký užitočný. YARN, ako som už povedal, je veľmi mladá schopnosť plánovania.
Hadoop je, druh kreslenie čiary v piesku tu, Hadoop nie je dátový sklad. Nie je to ani zďaleka dátový sklad, že je to takmer absurdný návrh povedať, že je. Na tomto diagrame vidím druh toku údajov, ktorý ide zo zásobníka údajov Hadoop do gargantuánskej škálovateľnej databázy, čo je to, čo skutočne urobíme, sklad podnikových údajov. Zobrazujem staršie databázy, vkladám údaje do dátového skladu a vykladam činnosti, ktoré vytvárajú offload databázy z dátového skladu, ale to je vlastne obrázok, ktorý sa začína objavovať, a povedal by som, že je to ako prvá generácia čo sa stane s dátovým skladom s Hadoop. Ale ak sa pozriete na dátový sklad sami, uvedomíte si, že pod dátovým skladom máte optimalizátor. Máte distribuovaných dotazovacích pracovníkov pre veľmi veľa procesov, ktoré sedia pravdepodobne nad veľkým počtom diskov. To sa deje v dátovom sklade. Je to vlastne druh architektúry, ktorá je postavená pre dátový sklad, a vybudovanie niečoho takého trvá dlho a spoločnosť Hadoop to vôbec nemá. Hadoop teda nie je dátový sklad a podľa môjho názoru sa z neho čoskoro nestane.
Má tento relatívny zásobník údajov a vyzerá to zaujímavo, ak sa len pozeráte na svet ako na sériu udalostí, ktoré prichádzajú do organizácie. To je to, čo ukazujem na ľavej strane tohto diagramu. Ak to prejde filtrovacou a smerovacou schopnosťou a všetko, čo potrebuje na streamovanie, sa sifónuje z aplikácií na streamovanie a všetko ostatné ide priamo do dátového rezervoáru, kde je pripravené a vyčistené, a potom ETL odovzdá buď jediné údaje. sklad alebo logický dátový sklad pozostávajúci z viacerých motorov. Podľa môjho názoru je to prirodzená línia vývoja pre Hadoop.
Pokiaľ ide o ETW, jedna z vecí, ktorú stojí za to upozorniť, je skutočnosť, že samotný dátový sklad bol skutočne presunutý - nie je to tak. V súčasnosti určite očakávate, že podľa hierarchických údajov bude existovať hierarchická schopnosť toho, čo ľudia alebo niektorí ľudia nazývajú dokumenty v dátovom sklade. To je JSON. Pravdepodobne sieťové dotazy, ktoré sú grafovými databázami, prípadne analytické. Takže sa zameriavame na ETW, ktorý má v skutočnosti zložitejšie pracovné zaťaženie ako tie, na ktoré sme zvyknutí. Je to tak trochu zaujímavé, pretože to znamená, že dátový sklad je stále sofistikovanejší, a preto bude ešte dlhšie, kým sa Hadoop dostane k nemu blízko. Význam dátového skladu sa rozširuje, stále však zahŕňa optimalizáciu. Musíte mať možnosť optimalizácie, nielen nad otázkami, ale aj nad všetkými týmito činnosťami.
To je naozaj. To je všetko, čo som chcel povedať o Hadoopovi. Myslím, že môžem odovzdať Rayovi, ktorý nemá žiadne snímky, ale vždy dokáže rozprávať.
Eric Kavanagh: Vezmem snímky. Je tu náš priateľ, Ray Wang. Ray, čo si o tom myslíš?
Ray Wang: Teraz si myslím, že to bol pravdepodobne jeden z najkratších a najväčších dejín obchodov s kľúčovými hodnotami a kde Hadoop odišiel do vzťahu s podnikmi, ktoré sú mimo podniku, takže sa pri počúvaní Robina vždy veľa naučím.
Vlastne mám jednu snímku. Tu môžem otvoriť jednu snímku.
Eric Kavanagh: Len choďte do toho a kliknite na, kliknite na Štart a choďte zdieľať plochu.
Ray Wang: Rozumiem, ideš. Vlastne sa o to podelím. Môžete vidieť samotnú aplikáciu. Pozrime sa, ako to chodí.
Celé toto rozprávanie o spoločnosti Hadoop a potom sa dostávame hlboko do rozhovoru o technológiách, ktoré existujú a kam smeruje spoločnosť Hadoop, a mnohokrát by som to rád vzal späť, aby som skutočne vedel obchodnú diskusiu. Veľa vecí, ktoré sa dejú na technologickej stránke, je skutočne tento kus, v ktorom hovoríme o dátových skladoch, správe informácií, kvalite údajov, zvládnutí týchto údajov, a preto máme tendenciu to vidieť. Ak sa teda pozriete na tento graf úplne dole, je veľmi zaujímavé, že typy ľudí, ktorých narazíme do diskusie, hovoria o hre Hadoop. Máme technológov a vedcov údajov, ktorí geekingujú, majú veľa vzrušenia a zvyčajne ide o zdroje údajov, nie? Ako zvládneme zdroje údajov? Ako to dosiahneme na správnej úrovni kvality? Čo robíme pre správu vecí verejných? Čo môžeme urobiť, aby sme spojili rôzne typy zdrojov? Ako si udržíme líniu? A všetky takéto diskusie. A ako získame viac SQL z nášho Hadoopu? Táto časť sa teda deje na tejto úrovni.
Potom, na informačnej a organizačnej stránke, to je miesto, kde sa to stane zaujímavým. Začíname spájať výstupy tohto prehľadu, ktorý získavame alebo ťaháme späť od obchodných procesov? Ako ho môžeme spojiť s akýmkoľvek modelom metadát? Spájame bodky medzi objektmi? A tak nové slovesá a diskusie o tom, ako tieto údaje používame, presúvajúc sa od toho, čo sme tradične vo svete CRUD: vytvárať, čítať, aktualizovať, mazať, do sveta, ktorý diskutuje o tom, ako zapojíme alebo zdieľame alebo spolupracujeme alebo páči alebo niečo vytiahni.
Tam začíname vidieť veľa vzrušenia a inovácií, najmä o tom, ako tieto informácie pritiahnuť a priniesť ich hodnotu. To je diskusia zameraná na technológie pod červenou čiarou. Nad touto červenou čiarou dostávame tie isté otázky, ktoré sme sa vždy chceli spýtať, a jedna z nich, ktorú vždy kladieme, je napríklad, napríklad, otázka pre maloobchodníkov je pre vás napríklad: „Prečo sa červené svetre lepšie predávajú v Alabame ako modré svetre v Michigane? “ Mohli by ste o tom premýšľať a povedať: „To je zaujímavé.“ Vidíš ten vzorec. Pýtame sa na túto otázku a pýtame sa: „Hej, čo to robíme?“ Možno ide o štátne školy - Michigan verzus Alabama. Dobre, chápem to, vidím, kam ideme. A tak začíname získavať obchodnú stránku domu, ľudí vo financovaní, ľudí, ktorí majú tradičné schopnosti BI, ľudí v oblasti marketingu a ľudí v oblasti ľudských zdrojov, ktorí hovoria: „Kde sú moje vzorce?“ Ako sa dostaneme k týmto vzorcom? A tak vidíme iný spôsob inovácie na strane Hadoopu. Je to naozaj o tom, ako môžeme rýchlejšie získať informácie o aktualizáciách. Ako vytvoríme tieto druhy spojení? Záleží to na ľuďoch, ktorí sa im páčia, ad: tech, ktorý sa v podstate pokúša spojiť reklamy a relevantný obsah od všetkého, od sietí ponúkajúcich ceny v reálnom čase, až po kontextové reklamy a umiestňovanie reklám.
Je to zaujímavé. Vidíte vývoj Hadoopu z: „Hej, tu je technologické riešenie. Tu je to, čo musíme urobiť, aby sme tieto informácie dostali ľuďom.“ Potom, keď prechádza cez časť podnikania, je to miesto, kde sa to stane zaujímavým. Je to vhľad. Kde je predstavenie? Kde je odpočet? Ako predpovedáme veci? Ako ovplyvňujeme? A potom to prineste na poslednú úroveň, kde vlastne vidíme ďalšiu sadu inovácií Hadoop, ktoré sa odohrávajú okolo rozhodovacích systémov a akcií. Aká je ďalšia najlepšia akcia? Takže viete, že modré svetre sa predávajú lepšie v Michigane. Sedíte na tóne modrých svetrov v Alabame. Je zrejmé, že „Áno, dobre, nechaj nás to tam dostať.“ Ako to urobíme? Aký je ďalší krok? Ako to môžeme zviazať? Možno nasledujúcu najlepšiu akciu, možno je to návrh, možno je to niečo, čo vám pomôže predchádzať problému, možno to nie je ani akcia, čo je samo o sebe akcia. Takže začíname vidieť, ako sa tento druh objaví. A krása toho, čo hovoríte o obchodoch s kľúčovou hodnotou, Robin, je, že sa to deje tak rýchlo. Deje sa to tak, že sme o tom nepremýšľali.
Pravdepodobne by som povedal, že za posledných päť rokov sme to zdvihli. Začali sme premýšľať o tom, ako môžeme znova využiť páky s kľúčovou hodnotou, ale práve v posledných piatich rokoch sa ľudia na to pozerajú úplne inak a je to, akoby sa technologické cykly opakovali v 40-ročných modeloch, takže je to milé legrační vec, kde sa pozeráme na cloud a ja som ako zdieľanie času mainframe. Pozeráme sa na Hadoop a ako sklad kľúčov a hodnôt - možno ide o dátový server, menej ako o dátový sklad - a preto začneme znova vidieť tieto vzorce. Čo sa teraz snažím urobiť, je premýšľať o tom, čo ľudia robili pred 40 rokmi? Aké prístupy a techniky a metodiky sa uplatňovali a ktoré boli obmedzené technológiami, ktoré ľudia mali? To je hnacou silou tohto myšlienkového procesu. Keď sa teda pozrieme na väčší obrázok nástroja Hadoop ako nástroja, keď sa vraciame späť a premýšľame o obchodných dôsledkoch, jedná sa o druh cesty, ktorou sa zvyčajne dostávame ľudí, aby ste videli, aké kúsky, ktoré časti sú v údajoch. rozhodovacia cesta. Chcel som sa o ňu podeliť. Je to trochu myslenie, ktoré sme interne používali a dúfajme, že sa k diskusii pridáva. Takže to otočím späť k tebe, Eric.
Eric Kavanagh: To je fantastické. Ak sa dokážete uchytiť na nejaké otázky a odpovede. Ale páčilo sa mi, že ste to vzali späť na úroveň podnikania, pretože na konci dňa je to všetko o podnikaní. Je to všetko o tom, ako veci urobiť, a ubezpečiť sa, že míňate peniaze rozumne, a to je jedna z otázok, ktoré som už videl, takže rečníci možno budú chcieť premýšľať o tom, čo je TCL cesty po ceste Hadoop. Medzi nástrojmi na kancelárske police je napríklad nejaký tradičný spôsob, ako robiť nové veci a používať nové sady nástrojov, pretože je tu opäť veľa vecí, ktoré nie sú nové, je to len druh zlučovanie novým spôsobom je, myslím, najlepší spôsob, ako to povedať.
Poďme teda a predstavme nášho priateľa Nikitu Ivanov. Je zakladateľom a výkonným riaditeľom spoločnosti GridGain. Nikita, idem do toho a podám ti kľúče a verím, že si tam vonku. Počuješ ma Nikita?
Nikita Ivanov: Áno, som tu.
Eric Kavanagh: Výborne. Takže podlaha je na vás. Kliknite na túto snímku. Použite šípku nadol a odneste ju. Päť minút.
Nikita Ivanov: Na ktorý obrázok kliknem?
Eric Kavanagh: Stačí kliknúť kdekoľvek na túto snímku a potom pomocou klávesov so šípkou nadol pohybovať. Stačí kliknúť na samotnú snímku a použiť šípku nadol.
Nikita Ivanov: Dobre, tak pár rýchlych snímok o GridGain. Čo robíme v súvislosti s touto konverzáciou? GridGain v podstate vyrába výpočtový softvér v pamäti a súčasťou platformy, ktorú sme vyvinuli, je urýchľovač Hadoop v pamäti. Pokiaľ ide o spoločnosť Hadoop, máme tendenciu myslieť na seba ako na špecialistov na výkon spoločnosti Hadoop. To, čo robíme, v podstate na vrchole našej základnej platformy v pamäti, ktorá pozostáva z technológií, ako sú dátová mriežka, streamovanie pamäte a výpočtové siete, by bolo schopné zapojiť a prehrávať urýchľovač Hadoop. To je veľmi jednoduché. Bolo by pekné, keby sme mohli vyvinúť nejaké riešenie typu plug-and-play, ktoré sa dá nainštalovať priamo v inštalácii Hadoop. Ak vy, vývojár MapReduce, potrebujete podporu bez toho, aby ste museli písať nový softvér alebo zmenu kódu alebo zmenu, alebo v zásade máte minimálnu konfiguračnú zmenu v klastri Hadoop. To sme vyvinuli.
V zásade je urýchľovač Hadoop v pamäti založený na optimalizácii dvoch komponentov v ekosystéme Hadoop. Ak uvažujete o Hadoop, je to hlavne na HDFS, čo je súborový systém. MapReduce, čo je rámec pre paralelné vedenie súťaží v hornej časti systému súborov. S cieľom optimalizovať Hadoop optimalizujeme oba tieto systémy. Vyvinuli sme systém súborov v pamäti, ktorý je úplne kompatibilný so 100% kompatibilným systémom plug-and-play s HDFS. Môžete spustiť namiesto systému HDFS, môžete ho spustiť aj nad systémom HDFS. Vyvinuli sme tiež pamäť MapReduce v pamäti, ktorá je kompatibilná s technológiou Hadoop MapReduce, ale existuje veľa optimalizácií spôsobu fungovania MapReduce a fungovania harmonogramu MapReduce.
Ak sa pozriete napríklad na túto snímku, ukážeme druh stohovania duplikátov. Na ľavej strane máte svoj typický operačný systém s GDM a na vrchu tohto diagramu je aplikačné centrum. Uprostred máte Hadoop. A Hadoop je opäť založený na HDFS a MapReduce. To teda na tomto diagrame predstavuje to, čo vlastne vkladáme do zásobníka Hadoop. Opäť je to plug-and-play; nemusíte meniť žiadny kód. Funguje to rovnako. Na nasledujúcom obrázku sme v podstate ukázali, ako sme optimalizovali pracovný postup MapReduce. Toto je pravdepodobne najzaujímavejšia časť, pretože vám poskytuje najväčšiu výhodu pri spúšťaní úloh MapReduce.
Typická MapReduce, keď posielate úlohu, a na ľavej strane je schéma, tam je obvyklá aplikácia. Zvyčajne tak zadávate úlohu a úloha ide na sledovanie úloh. 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. Ďakujem.
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.
Vďaka.
Eric Kavanagh: Yeah, thank you so much. I'll throw a question over to you, really a comment, and let you kind of expound upon it. I really think you're on the ball here with this concept of turning over these operators and letting people use these operators almost like Legos to build big data applications. Can you kind of talk about what goes into the process of taking these operators and stitching them together, how do you actually do that?
Phu Hoang: That's a great question. So first of all, these operators are in your standard application Java Logic. We supply 400 of them. They do all kinds of processing and so to build your application, you really are just connecting operators together into a data flow graph. In our customers, we find that they use a number of operators that we have in our library as well as they take their own job of custom logic and make it an operator so that they can substantiate that into a graph.
Eric Kavanagh: OK, good. I think it's a good segue to bring in John Santaferraro from Actian because you guys have a slightly similar approach, it seems to me, in opening up a sort of management layer to be able to play around with different operators. Can you talk about what you do with respect to what tools we're just talking about, John?
John Santaferraro: Yeah, exactly. We have a library of analytics operators as well as transformational operators, operators for blending and enriching data and it is very similar. You use a drag-and-drop interface to be able to stitch together these data flows or work flows, and even analytic workflows. So it's everything from being able to connect to data, to be able to blend and enrich data, to be able to run data science or machine learning algorithms and then even being able to push that into a high-performance low-latency analytic engine. What we find is that it's all built on the open-source nine project. So we capture a lot of the operators that they are developing and then we take all of that, and via YARN, very similar to what Phu described at DataTorrent, we push that down so that it is parallelized against all of the nodes in a Hadoop cluster. A lot of it is about making the data in Hadoop much more accessible to business users and less-skilled workers, somebody besides a data scientist.
Eric Kavanagh: OK, let me go bring in Nikita once again. I'm going to throw your five up as well. Can you kind of talk about how you approach this solution vis-à-vis what these two gentlemen just talked about? How does someone actually put this stuff together and make use from GridGain?
Nikita Ivanov: Well, I think the biggest difference between us and from practically the rest of them is we don't require you to do any recording - you don't have to do anything, it's a plug-and-play. If you have an application today, it's going to work faster. You don't have to change code; you don't have to do anything; you just have to install GridGain along the side of Hadoop cluster and that's it. So that's the biggest difference and we talked to our customers. There are different myriad of solutions today that ask you to change something: programming, doing your API, using your interfaces and whatnot. Ours is very simple. You don't need to invest a lot of time into the Hadoop ecosystem, and whatever you used to do, the MapReduce or any of the tools continue to use. With GridGain, you don't have to change any single line of code, it's just going to work faster. That's the biggest difference and that's the biggest message for us.
Eric Kavanagh: Let's get Jim back in here too. Jim, your quote is killing me. I had to write it down in between that. I'll put it into some kind of deck, but the Hadoop ecosystem right now is like a pool party and the parents just came home. That is funny stuff man; that is brilliant. Can you kind of talk about how you guys come onto the scene? How do you actually implement this? How long does that take? How does all that work?
Jim Kaskade: Yes. So there are a couple of varieties depending on the target customer, but typically these days, you see evaluations where security is factored in, in some of these hardening requirements that I talked about. What has happened in some other cases, and especially last year where people had big plans to deploy, is that there was kind of a science project, if you will, or somebody was playing with the technology and had a cluster up and working and was working with it but then the security guy shows up, and if it is going to go on a live data center, it has to basically comply with the same requirements that we have for other equipment running in the data center, if it is going to be an infrastructure that we build out. Last year, we had even some banks that told us they were going to deploy 400 to 1, 000 nodes last year and they're still sitting on a 20-node cluster mainly because now a security person has been plugged in. They've got to be worried about financial compliance, about sets of information that is sitting on a cluster, and so forth. It varies by customer, but typically this is kind of what elongates the cycles and this is typical of a new technology where if you really want to deploy this in production environment, it really has to have some of these other pieces including the very valuable open-source pieces, right?
Eric Kavanagh: OK, good. Let's see. I'm going to bring Phu back into the equation here. We've got a good question for you. One of the attendees is asking how is DataTorrent different from Storm or Kafka or the Redis infrastructure. Phu, are you out there? Hey, Phu, can you hear me? Maybe I'm mute.
Let's bring Ray Wang back into this. Ray, you've seen a lot of these technologies and looked at how they worked. I really love this concept of turning over control or giving control to end users of the operators. I like to think of them as like really powerful Legos that they can use to kind of build some of these applications. Can you comment on that? What do you think about all that?
Ray Wang: Coming from my technical background, I'd say I'm scared - I was scared shitless! But honestly, I think it's important, I mean, in order to get scale. There's no way you can only put so many requests. Think about the old way we did data warehousing. In the business I had to file the request for a report so that they could match all the schemes. I mean, it's ridiculous. So we do have to get to a way for the business side of the house and definitely become data jocks. We actually think that in this world, we're going to see more digital artists and people that have the right skills, but also understand how to take that data and translate that into business value. And so these digital artisans, data artisans depending on how you look at this, are going to need both really by first having the curiosity and the right set of questions, but also the knowledge to know when the data set stinks. If I'm getting a false positive or a false negative, why is that happening?
I think a basic level of stats, a basic level of analytics, understanding that there's going to be some training required. But I don't think it's going to be too hard. I think if you get the right folks that should be able to happen. You can't democratize the whole decision-making process. I see that happening. We see that in a lot of companies. Some are financial services clients are doing that. Some of our retail folks are doing that, especially in the razor-thin margins that you are seeing in retail. I was definitely seeing that in high tech just around here in the valley. That's just kind of how people are. It's emerging that way but it's going to take some time because these basic data skills are still lacking. And I think we need to combine that with some of the stuff that some of these guys are doing here on this webinar.
Eric Kavanagh: Well, you bring up a really good point. Like how many controls you want to give to the average end user. You don't want to give an airplane cockpit to someone who's driving a car for the first time. You want to be able to closely control what they have control over. I guess my excitement kind of stems around being able to do things yourself, but the key is you got to put the right person in that cockpit. You got to have someone who really knows what they're doing. No matter what you hear from the vendor community folks, when somebody's more powerful tools are extremely complex, I mean if you are talking about putting together a string of 13, 14, 15 operators to do a particular type of transformation on your data, there are not many people who could do that well. I think we're going to have many, many more people who do that well because the tools are out there now and you can play with the stuff, and there is going to be a drive to be able to perfect that process or at least get good at it.
We did actually lose Phu, but he's back on the line now. So, Phu, the question for you is how is DataTorrent different from, like, Storm or Kafka or Redis or some of these others?
Phu Hoang: I think that's a great question. So, Redis of course is really an in-memory data store and we connect to Redis. We see ourselves as really a processing engine of data, of streaming data. Kafka again is a great bus messaging bus we use. It's actually one of our favorite messaging bus, but someone has to do the big data processing across hundreds of nodes that is fault tolerant, that is scalable, and I repeat that as the job that we play. So, yes, we are similar to Storm, but I think that Storm is really developed a long time ago even before Hadoop, and it doesn't have the enterprise-level thinking about scalability to the hundreds and millions, now even billions of events, nor does it really have the HA capability that I think enterprise requires.
Eric Kavanagh: Great. And you know, speaking of HA, I'll use that as an excuse to bring Robin Bloor back into the conversation. We just talked about this yesterday. What do you mean by high availability? What do you mean by fault tolerance? What do you mean by real time, for example? These are terms that can be bent. We see this all time in the world of enterprise technology. It's a good term that other people kind of glom onto and use and co-opt and move around and then suddenly things don't mean quite what they used to. You know, Robin, one of my pet peeves is this whole universe of VOIP. It's like "Why would we go down in quality? Isn't it important to understand what people say to you and why that matters?" But I'll just ask you to kind of comment on what you think. I'm still laughing about Ray's comment that he's scared shitless about giving these people. Čo si o tom myslíš?
Ray Wang: Oh, I think it's a Spider-man problem, isn't it? S veľkou mocou prichádza veľká zodpovednosť. 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. Čo si o tom myslíš?
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 týmto, sa ťa rozlúčime, ľudia. 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. Ahoj, ahoj.
