Redaktora piezīme: Šis ir tiešraides tiešraidē atšifrējums. Pilnu tīmekļa apraidi varat apskatīt šeit.
Ēriks Kavanaghs: dāmas un kungi, ir pienācis laiks kļūt gudram! Ir pienācis laiks pilnīgi jaunai TechWise izrādei! Mani sauc Ēriks Kavanaghs. Es būšu jūsu moderators mūsu TechWise atklāšanas epizodē. Tieši tā. Tā ir Techopedia un Bloor Group, protams, Inside Analysis slavas partnerība.
Mani sauc Ēriks Kavanaghs. Es moderēšu šo patiešām interesanto un iesaistīto notikumu, ļaudis. Mēs dziļi iedziļināsimies austā, lai saprastu, kas notiek ar šo lielo lietu, kuru sauc Hadoop. Kas ir zilonis istabā? To sauc par Hadoop. Mēs mēģināsim izdomāt, ko tas nozīmē un kas ar to notiek.
Pirmkārt, liels paldies mūsu sponsoriem GridGain, Actian, Zettaset un DataTorrent. Netālu no šī notikuma beigām no katra no viņiem saņemsim dažus īsus vārdus. Mums būs arī jautājumi un atbildes, tāpēc nekautrējieties - sūtiet savus jautājumus jebkurā laikā.
Mēs izpētīsim detaļas un uzdosim smagos jautājumus mūsu ekspertiem. Un, runājot par ekspertiem, hey, viņi tur ir. Tātad, mēs dzirdēsim no mūsu pašu Dr Robin Bloor un ļaudīm, es esmu ļoti satraukts, ka mums ir leģendārais Ray Wang, galvenais analītiķis un Constellation Research dibinātājs. Viņš šodien ir tiešsaistē, lai izteiktu mums savas domas, un viņš, tāpat kā Robins, ir neticami daudzveidīgs un patiesībā koncentrējas uz daudzām dažādām jomām un spēj tos sintezēt un patiesi saprast, kas tur notiek visā šajā informācijas tehnoloģiju jomā un datu pārvaldība.
Tātad, tur ir tas mazais mīlīgais zilonis. Kā redzat, viņš ir ceļa sākumā. Tas tikai tagad sākas, tas ir tikai sava veida sākums, visa šī Hadoop lieta. Protams, es domāju, ka 2006. vai 2007. gadā tas tika nodots atklātā pirmkoda kopienai, taču ir noticis daudz lietu, ļaudis. Ir notikušas milzīgas izmaiņas. Patiesībā es vēlos iepazīstināt ar šo stāstu, tāpēc došos ātri dalīties ar darbvirsmu, vismaz es domāju, ka tāda esmu. Darīsim ātru koplietošanu darbvirsmā.
Es parādīšu jums šo vienkārši trako, trako stāstu ļaudīm. Tātad Intel ieguldīja 740 miljonus dolāru, lai nopirktu 18 procentus no Cloudera. Es domāju un esmu kā "Svētie Ziemassvētki!" Es sāku nodarboties ar matemātiku, un tas ir kā: "Tas ir 4, 1 miljarda ASV dolāru novērtējums." Pārdomāsim par šo brīdi. Es domāju, ja WhatsApp vērtība ir USD 2 miljardi, es domāju, ka Cloudera varētu būt arī 4, 1 miljarda USD vērts, vai ne? Es domāju, kāpēc ne? Daži no šiem numuriem mūsdienās ir tikai pa logu. Es domāju, parasti, runājot par ieguldījumiem, jums ir EBITDA un visi šie citi dažādie mehānismi, ieņēmumu reizinājumi un tā tālāk. Tas būs viens heck no ienākumu reizinātāja, lai sasniegtu 4, 1 miljardu USD Cloudera, kas ir satriecošs uzņēmums. Nekļūdieties man - tur ir daži ļoti, ļoti gudri cilvēki, ieskaitot puisi, kurš sāka visu Hadoop traku, Doug Cutting, viņš ir tur - ļoti daudz ļoti inteliģentu cilvēku, kuri dara ļoti daudz, patiešām, tiešām foršas lietas, bet vissvarīgākais ir tas, ka USD 4, 1 miljards, tas ir daudz naudas.
Tātad šeit ir sava veida nebrīvē acīmredzams brīdis, kad man tūlīt iet cauri galvai, kas ir mikroshēma Intel. Viņu mikroshēmu dizaineri ierauga kādu Hadoop optimizētu mikroshēmu - man tas ir jādomā, ļaudis. Tas ir tikai mans minējums. Tā ir tikai baume, kas nāk no manis, ja jūs to vēlēsities, taču tam ir sava jēga. Un ko tas viss nozīmē?
Tātad šeit ir mana teorija. Kas notiek? Liela daļa šo lietu nav nekas jauns. Liela paralēla apstrāde nav šausmīgi jauna. Paralēlā apstrāde noteikti nav jauna. Es kādu laiku esmu atradies superdatoru pasaulē. Daudzas no šīm notiekošajām lietām nav nekas jauns, taču pastāv tāda vispārēja apziņa, ka ir jauns veids, kā uzbrukt dažām no šīm problēmām. Tas, ko es redzu notiekot, ja paskatās uz dažiem lielajiem Cloudera vai Hortonworks pārdevējiem un dažiem no šiem citiem puišiem, tas, ko viņi patiesībā dara, ja jūs to vārāt līdz visdetalizētākajam destilētajam līmenim, ir lietojumprogrammu izstrāde. To viņi dara.
Viņi izstrādā jaunas lietojumprogrammas - daži no tiem ir saistīti ar biznesa analīzi; daži no tiem tikai ietver uzlādes sistēmas. Viens no mūsu pārdevējiem, kurš par to runāja, visu dienu, šodien, šovā, rīkojas ar šādām lietām. Bet, ja tas ir šausmīgi jauns, atkal atbilde ir “ne īsti”, bet notiek daudz kas, un personīgi es domāju, ka tas, kas notiek ar Intel, padarot šo milzīgo ieguldījumu, ir tirgus veidošanas solis. Viņi skatās uz šodienas pasauli un redz, ka šodien tā ir sava veida monopola pasaule. Tur ir Facebook, un viņi ir pārspējuši tikai puņķus no sliktā MySpace. LinkedIn ir sitis puņķi no nabaga Who's Who. Tātad, jūs paskatāties apkārt, un tas ir viens pakalpojums, kas šodien dominē visās šajās dažādajās telpās, un es domāju, ka ideja ir, ka Intel metīs visas viņu mikroshēmas uz Cloudera un mēģinās to pacelt kaudzes augšpusē - tas ir tikai mana teorija.
Tāpēc ļaudīm, kā es teicu, mums būs ilga Q&A sesija, tāpēc nekautrējieties. Sūtiet savus jautājumus jebkurā laikā. To var izdarīt, izmantojot šo apraides konsoles komponentu Q & A. Un līdz ar to es vēlos piekļūt mūsu saturam, jo mums ir daudz lietu, kas jāiziet cauri.
Robin Bloor, ļaujiet man nodot atslēgas jums, un grīda ir jūsu.
Robins Bloors: Labi, Ēriks, paldies par to. Ievedīsim dejojošos ziloņus. Īstenībā tā ir ziņkārīga lieta, ka ziloņi ir vienīgie sauszemes zīdītāji, kas faktiski nevar lēkt. Visi šie ziloņi šajā grafikā ir ieguvuši vismaz vienu pēdu uz zemes, tāpēc es domāju, ka tas ir iespējams, taču zināmā mērā tie acīmredzami ir Hadoop ziloņi, tāpēc ļoti, ļoti spējīgi.
Jautājums, kas, manuprāt, ir jāapspriež un ir jāapspriež pilnībā. Tas ir jāapspriež, pirms dodaties kaut kur citur, un ir jāsāk runāt par to, kas patiesībā ir Hadoop.
Viena no lietām, kas absolūti izriet no cilvēku spēles principa, ir galveno vērtību veikals. Mums kādreiz bija galvenās vērtības veikali. Mums tās kādreiz bija IBM lieldatoros. Mums viņi atradās minidatoros; DEC VAX bija IMS faili. Bija ISAM iespējas, kuras bija gandrīz katrā minidatorā, kurā varat nokļūt. Bet kaut kad ap 80. gadu beigām ienāca Unix, un Unix faktiski tajā nebija neviena atslēgas vērtības veikala. Kad Unix to izstrādāja, viņi attīstījās ļoti ātri. Tas, kas notika, bija tas, ka datu bāzu pārdevēji, it īpaši Oracle, devās uz turieni un viņi pārdeva jūsu datu bāzes, lai rūpētos par visiem datiem, kurus vēlaties pārvaldīt Unix. Windows un Linux izrādījās vienādi. Tātad nozare labāko 20 gadu daļu pagāja bez vispārējas nozīmes atslēgas vērtības veikala. Nu, tas ir atkal atpakaļ. Tas ir ne tikai atpakaļ, bet arī pielāgojams.
Tagad es domāju, ka tas tiešām ir Hadoop pamats, un zināmā mērā tas nosaka, kurp tas nonāks. Kas mums patīk galvenās vērtības veikalos? Tie no jums, kuri esmu tikpat veci kā cilvēki, un patiesībā atceras, ka strādāja ar galveno vērtību veikaliem, saprot, ka jūs tos varētu diezgan daudz izmantot, lai neoficiāli izveidotu datu bāzi, bet tikai neformāli. Jūs zināt, ka metadati ātri glabā programmas kodā, taču jūs faktiski varētu izveidot šo ārējo failu, un, ja jūs vēlētos sākt izturēties pret galveno vērtību krātuvi, piemēram, datu bāzē. Bet, protams, tam nebija visu to atkopšanas iespēju, kāda ir datu bāzei, un tajā nebija šausmīgi daudz lietu, ko tagad ir ieguvušas datu bāzes, taču tā bija patiešām noderīga funkcija izstrādātājiem, un tas ir viens no iemesliem, kāpēc es domāju ka Hadoop ir izrādījies tik populārs - vienkārši tāpēc, ka ātri ir bijuši kodētāji, programmētāji, izstrādātāji. Viņi saprata, ka ne tikai ir veikala galvenā vērtība, bet tas ir arī atslēgas vērtības veikals. Tas tiek izsvītrots diezgan daudz uz nenoteiktu laiku. Es nosūtīju šos mērogus tūkstošiem serveru, tāpēc Hadoop ir patiešām liela lieta, tas ir tas, kas tas ir.
Tam papildus ir arī MapReduce, kas ir paralizācijas algoritms, bet patiesībā tas, manuprāt, nav mazsvarīgs. Tātad, jūs zināt, Hadoop ir hameleons. Tā nav tikai failu sistēma. Esmu redzējis dažāda veida prasības, kas izvirzītas Hadoop: tā ir slepena datu bāze; tā nav slepena datu bāze; tas ir parasts veikals; tas ir analītisks rīku komplekts; tā ir ELT vide; tas ir datu tīrīšanas rīks; tā ir straumēšanas platformu datu noliktava; tas ir arhīvu veikals; tas ir vēža izārstēšana utt. Lielākā daļa no šīm lietām patiesībā nav taisnība vaniļa Hadoopa gadījumā. Hadoop, iespējams, ir prototips - tā noteikti ir SQL datu bāzes prototipēšanas vide, taču tā īsti nav, ja, ievietojot vecumu ar vecuma katalogu virs Hadoop, jums ir kaut kas, kas izskatās kā datu bāze, bet tas nav īsti tas, ko ikviens varētu dēvēt par datu bāzi spēju ziņā. Šo iespēju ir daudz, un jūs noteikti tās varat iegūt vietnē Hadoop. Viņu noteikti ir daudz. Faktiski jūs varat iegūt kādu Hadoop avotu, bet pats Hadoop nav tas, ko es sauktu par operatīvi rūdītu, un tāpēc darījums par Hadoop, es tiešām nebūtu uz kaut ko citu, ir tāds, ka jums ir nepieciešama trešā -partijas produkti, lai to uzlabotu.
Tātad, runājot par jums, jūs varat iemest tikai dažas līnijas, jo es runāju par Hadoop pārmērīgu aizsniegumu. Pirmkārt, reāllaika vaicājumu iespējas, labi zināt, ka reāllaiks ir biznesa laiks, patiesībā gandrīz vienmēr ir kritisks veiktspēja. Es domāju, kāpēc jūs būtu inženieris reāllaikā? Hadoop to īsti nedara. Tas dara kaut ko gandrīz reāllaikā, bet reāli nedara. Tas veic straumēšanu, bet nedara straumēšanu tādā veidā, kā es varētu dēvēt par patiešām kritiska tipa lietojumprogrammu straumēšanas platformām. Pastāv atšķirība starp datu bāzi un noņemamu veikalu. Sinhronizējot to ar Hadoop, tiek nodrošināta skaidra datu krātuve. Tas ir kā datu bāze, bet tas nav tas pats, kas datu bāze. Hadoop savā dzimtajā formā, manuprāt, īsti nekvalificējas par datu bāzi, jo tajā trūkst daudz lietu, kas datubāzei vajadzētu būt. Hadoop dara daudz, bet nedara to īpaši labi. Atkal jau ir spējas, taču mēs esam tālu no tā, lai visās šajās jomās mums būtu ātras iespējas.
Otra lieta, kas jāsaprot par Hadoop, ir tā, ka tas ir tāls ceļš, kopš tā tika izstrādāta. Tas tika izstrādāts pirmajās dienās; tā tika izstrādāta, kad mums bija serveri, kuriem faktiski bija tikai viens procesors vienam serverim. Mums nekad nebija daudzkodolu procesoru, un tas tika izveidots, lai darbotos pāri režģiem, palaistu režģus un atdalītājus. Viens no Hadoop dizaina mērķiem bija nekad nezaudēt darbu. Un tas tiešām bija saistīts ar diska kļūmēm, jo, ja jums ir simtiem serveru, tad, ja serveros ir diski, iespējams, ka jūs iegūsit kaut ko līdzīgu 99.8. Tas nozīmē, ka vidēji viena no šiem serveriem kļūme notiks reizi 300 vai 350 dienās, vienu dienu gadā. Tātad, ja tādu būtu simtiem, iespējams, ka serveru kļūme rastos jebkurā gada dienā.
Hadoop tika izveidots speciāli, lai risinātu šo problēmu - tā, ka gadījumā, ja kaut kas neizdodas, tas uzņem momentuzņēmumus par visu notiekošo katrā konkrētajā serverī un var atgūt tekošo pakešdarbu. Un tas bija viss, kas faktiski Hadoop jebkad darbojās sērijveida darbos, un tas ir jāsaka, ka tā ir patiešām noderīga spēja. Daži no sērijveida darbiem, kas tika vadīti - it īpaši Yahoo, kur, manuprāt, Hadoops bija piedzimis - darbosies divas vai trīs dienas, un, ja tas neizdevās pēc dienas, jūs patiešām negribējāt zaudēt darbu tas bija izdarīts. Tātad tas bija dizaina punkts aiz pieejamības Hadoop. Jūs nesauktu par tik augstu pieejamību, bet jūs to varētu nosaukt par augstu pieejamību sērijas pakešu darbiem. Tas, iespējams, ir veids, kā to apskatīt. Augsta pieejamība vienmēr tiek konfigurēta atbilstoši darba līnijas raksturlielumiem. Pašlaik Hadoop var konfigurēt tikai patiešām sērijveida pakešu darbiem, ņemot vērā šāda veida atkopšanu. Uzņēmējdarbības augstā pieejamība, domājams, vislabāk ir domājama saistībā ar LLP. Es uzskatu, ka, ja jūs neuzlūkojat to kā sava veida reālā laika lietu, Hadoop to vēl nedara. Droši vien tas ir tāls, lai to izdarītu.
Bet šeit ir skaista lieta par Hadoop. Grafika labajā pusē, kurai ap malu ir pārdevēju saraksts, un visas tajā esošās līnijas norāda savienojumus starp šiem pārdevējiem un citiem Hadoop ekosistēmas produktiem. Ja paskatās uz to, tā ir neticami iespaidīga ekosistēma. Tas ir diezgan ievērojams. Mēs acīmredzami runājam ar daudziem pārdevējiem, ņemot vērā viņu iespējas. Starp pārdevējiem, ar kuriem esmu runājis, ir dažas patiešām neparastas iespējas, kā lietot Hadoop un atmiņā, kā izmantot Hadoop kā saspiestu arhīvu, izmantot Hadoop kā ETL vidi utt. Un tā tālāk. Bet tiešām, ja jūs pievienojat produktu pašam Hadoop, tas darbojas īpaši labi noteiktā telpā. Tāpēc, kamēr es kritiski izteicos par dzimto Hadoop, es neesmu kritisks par Hadoop, kad jūs tam faktiski pieliekat zināmu varu. Manuprāt, Hadoop popularitātes veids garantē tās nākotni. Ar to es domāju, pat ja pazūd katra koda rindiņa, kas līdz šim rakstīta vietnē Hadoop, es neticu, ka HDFS API pazudīs. Citiem vārdiem sakot, es domāju, ka failu sistēma, API, ir šeit, lai paliktu, un, iespējams, YARN, plānotājs, kas to pārrauga.
Kad jūs to reāli aplūkojat, tā ir ļoti svarīga spēja, un es apmēram pēc minūtes to apskatīšu, bet otra lieta, kas, teiksim, aizraujoši cilvēki par Hadoop, ir visa atvērtā pirmkoda aina. Tāpēc ir vērts izpētīt atvērtā pirmkoda ainu attiecībā uz to, ko es uzskatu par reālu spēju. Kaut arī Hadoop un visi tā komponenti noteikti var darīt to, ko mēs saucam par datu garumu - vai, kā es labprātāk to saucu, par datu rezervuāru, tas noteikti ir ļoti labs pieturvieta, lai nomestu datus organizācijā vai apkopotu datus organizācijā - ārkārtīgi labi smilšu kastēm un makšķerēšanas datiem. Tas ir ļoti labi, kā prototipu izstrādes platforma, kuru jūs varētu ieviest dienas beigās, taču kā attīstības vide jūs zināt gandrīz visu, ko vēlaties. Kā arhīvu veikals tas ir diezgan daudz, un tajā ir viss nepieciešamais, un tas, protams, nav dārgs. Es nedomāju, ka mums vajadzētu šķirties no šīm divām lietām no Hadoop, pat ja tās formāli, ja jums patīk, nav Hadoop sastāvdaļas. Tiešsaistes ķīlis ir ienesis milzīgu daudzumu analītikas atvērtā koda pasaulē, un liela daļa šīs analītikas tagad tiek palaista vietnē Hadoop, jo tā nodrošina ērtu vidi, kurā jūs faktiski varat uzņemt daudz ārēju datu un vienkārši sākt spēlēt analītiskā smilšu kastē.
Un tad jums ir atvērtā koda iespējas, kuras abas ir mašīnmācība. Abas no tām ir ārkārtīgi spēcīgas tādā nozīmē, ka tās īsteno jaudīgus analītiskos algoritmus. Ja saliksit šīs lietas, jums būs ļoti, ļoti svarīgu spēju kodoli, kas vienā vai otrā veidā ir ļoti iespējams - neatkarīgi no tā, vai tā attīstās pati, vai arī pārdevēji ierodas aizpildīt trūkstošos gabalus - tas, visticamāk, turpināsies ilgu laiku, un, protams, es domāju, ka mašīnmācībai jau ir ļoti liela ietekme uz pasauli.
Hadoop, YARN evolūcija mainīja visu. Notika tas, ka MapReduce bija diezgan daudz piemetināta agrīnajai failu sistēmai HDFS. Kad tika ieviests YARN, tas pirmajā laidienā izveidoja plānošanas iespēju. Jūs negaidāt, ka ārkārtīgi sarežģītā plānošana būs jau no pirmās izlaišanas, taču tas nozīmēja, ka tagad tā vairs nav obligāti nepieciešama ielāpu vide. Tā bija vide, kurā varēja ieplānot vairākus darbus. Tiklīdz tas notika, bija virkne pārdevēju, kas bija turējušies prom no Hadoop - viņi vienkārši ienāca un pieslēdzās tam, jo tad viņi to varēja vienkārši aplūkot kā datplūsmas plānošanas vidi un adresēt lietas uz tā. Ir pat datu bāzu pārdevēji, kas savas datu bāzes ir ieviesuši HDFS, jo viņi vienkārši ņem motoru un vienkārši nodod to HDFS. Ar kaskādes un ar YARN palīdzību tā kļūst par ļoti interesantu vidi, jo, izmantojot HDFS, varat izveidot sarežģītas darbplūsmas, un tas tiešām nozīmē, ka jūs varat sākt domāt par to kā par tiešām platformu, kas vienlaikus var darbināt vairākus darbus un virzās uz priekšu darot misijai kritiskas lietas. Ja jūs to darīsit, jums, iespējams, būs jāiegādājas daži trešo pušu komponenti, piemēram, drošība un tā tālāk, un tā tālāk, kuriem Hadoop faktiski nav revīzijas konta, lai aizpildītu nepilnības, bet jums nokļūstiet vietā, kur pat ar vietējo atvērto avotu var izdarīt dažas interesantas lietas.
Runājot par to, kur es domāju, ka Hadoop gatavojas iet, es personīgi uzskatu, ka HDFS kļūs par noklusējuma faila sistēmu un tāpēc kļūs par OS - operētājsistēmu - datu plūsmas režģim. Es domāju, ka tai ir milzīga nākotne, un es nedomāju, ka tā apstāsies. Un es domāju, ka patiesībā ekosistēma tikai palīdz, jo gandrīz visi, visi kosmosa pārdevēji, vienā vai otrā veidā faktiski integrē Hadoop, un viņi to tikai ļauj. Runājot par vēl vienu punktu, ko vērts pievērst Hadoop pārmērībai, vai tā nav ļoti laba platforma plus paralēle. Ja jūs faktiski skatāties uz to, ko tas dara, tas, ko tas faktiski dara, ir tas, ka tas regulāri uzņem momentuzņēmumu katrā serverī, jo tas veic savus MapReduce darbus. Ja jūs plānojat projektēt patiešām ātru paralēlošanu, jūs neko tādu nedarītu. Faktiski jūs, iespējams, nelietojat MapReduce vienu pašu. MapReduce ir tikai tas, ko es teiktu puse, kas spēj radīt paralēlismu.
Paralēlismam ir divas pieejas: viena ir procesu virzīšana, otra - dalot datus MapReduce, un tas veic datu dalīšanu, tāpēc ir daudz darbu, kur MapReduce patiesībā nebūtu ātrākais veids, kā to izdarīt, bet tas tomēr notiks. sniegs jums paralēlismu, un no tā nekas netiks atņemts. Kad esat ieguvis daudz datu, šāda veida jauda parasti nav tik noderīga. Dzija, kā es jau teicu, ir ļoti jauna plānošanas spēja.
Hadoop ir tāds, kāds šeit ievelk līniju smiltīs, Hadoop nav datu noliktava. Tas ir tik tālu no datu noliktavas, ka tas ir gandrīz absurds ieteikums apgalvot, ka tāda ir. Šajā diagrammā tas, ko es rādu augšpusē, ir sava veida datu plūsma, pārejot no Hadoop datu rezervuāra uz grandiozu mēroga datu bāzi, ko mēs faktiski darīsim, uzņēmuma datu noliktavu. Es rādu mantotās datu bāzes, ievietoju datus datu noliktavā un veicu izkraušanu, izveidojot izkraušanas datu bāzes no datu noliktavas, taču tas faktiski ir attēls, kuru es sāku redzēt, un es teiktu, ka tas ir kā pirmā paaudze kas notiek ar datu noliktavu ar Hadoop. Bet, pats apskatot datu noliktavu, jūs saprotat, ka zem datu noliktavas jums ir optimizētājs. Jūs esat sadalījis vaicājumu darbiniekus ļoti daudzos procesos, kas sēž virs ļoti daudziem ļoti daudziem diskiem. Tas notiek datu noliktavā. Tas faktiski ir tāds veida arhitektūra, kas izveidots datu noliktavai, un kaut kas līdzīgs ir jāgaida diezgan ilgi, un Hadoop tāda vispār nav. Tātad Hadoop nav datu noliktava, un, manuprāt, tas nekļūs par drīzu.
Tam patiešām ir šis nosacītais datu rezervuārs, un tas izskatās interesanti, ja paskatās tikai uz pasauli kā uz notikumu virkni, kas ieplūst organizācijā. To es rādu šīs diagrammas kreisajā pusē. Pēc filtrēšanas un maršrutēšanas iespējām, kā arī straumēšanai nepieciešamās lietas tiek izdzēstas no straumēšanas lietotnēm, un viss pārējais nonāk tieši datu rezervuārā, kur tas ir sagatavots un iztīrīts, un pēc tam ETL to nodod vai nu atsevišķiem datiem noliktava vai loģiska datu noliktava, kas sastāv no vairākiem motoriem. Manuprāt, šī ir dabiska Hadoop attīstības līnija.
Runājot par ETW, viena no lietām, uz kuru ir vērts norādīt, ir tāda, ka pati datu noliktava faktiski tika pārvietota - tas nav tas, kas tas bija. Protams, mūsdienās jūs sagaidāt, ka pastāv hierarhiskas iespējas katram hierarhiskajam datu veidam par to, ko cilvēki vai daži cilvēki sauc par datu noliktavas dokumentiem. Tas ir JSON. Iespējams, tīkla vaicājumi ir grafiku datu bāzes, iespējams, analītika. Tātad, uz ko mēs virzāmies, ir ETW, kam faktiski ir sarežģītāka darba slodze nekā tiem, pie kuriem mēs esam pieraduši. Tātad tas ir sava veida interesants, jo savā ziņā tas nozīmē, ka datu noliktava kļūst vēl sarežģītāka, un tāpēc tas būs vēl ilgāks laiks, pirms Hadoop nokļūs kaut kur tuvu tai. Datu noliktavas nozīme paplašinās, taču tā joprojām ietver optimizāciju. Jums ir jābūt optimizācijas spējai, ne tikai vaicājumiem tagad, bet arī visām šīm darbībām.
Tā tas tiešām ir. Tas ir viss, ko es gribēju pateikt par Hadoop. Es domāju, ka varu nodot Ray, kurš nav ieguvis nevienu slaidu, bet viņš vienmēr prot runāt.
Ēriks Kavanaghs: Es paņemšu slaidus. Tur ir mūsu draugs Rejs Vangs. Tātad, Ray, kādas ir tavas domas par šo visu?
Ray Wang: Tagad es domāju, ka tas, iespējams, bija viens no kodolīgākajiem un lieliskajiem galveno vērtību veikalu vēstures gadījumiem un tas, kur Hadoop ir devies attiecībās ar uzņēmumiem, kuri darbojas, tāpēc es vienmēr daudz ko mācos, klausoties Robinu.
Patiesībā man ir viens slaids. Es šeit varu uzlēkt vienu slaidu.
Ēriks Kavanaghs: vienkārši dodieties uz priekšu un noklikšķiniet uz, noklikšķiniet uz Sākt un dodieties koplietot savu darbvirsmu.
Rejs Vangs: Es sapratu, tur jūs ejat. Es tiešām padalīšos. Jūs varat redzēt pašu lietotni. Redzēsim, kā iet.
Visas šīs runas par Hadoop un pēc tam mēs iedziļināmies sarunā par tehnoloģijām, kas pastāv un uz kurām virzās Hadoop, un daudzreiz es vienkārši vēlētos to paņemt atpakaļ, lai patiešām būtu biznesa diskusija. Liela daļa lietu, kas notiek tehnoloģiju jomā, patiešām ir šis gabals, kurā mēs runājām par datu noliktavām, informācijas pārvaldību, datu kvalitāti, šo datu pārvaldīšanu, un tāpēc mums ir tendence to redzēt. Tātad, ja jūs aplūkojat šo diagrammu šeit pašā apakšā, tas ir ļoti interesanti, ka indivīdu tipi, par kuriem mēs uzturamies, runā par Hadoopu. Mums ir tehnologi un datu zinātnieki, kuri meklē informāciju, kuriem ir daudz satraukuma, un parasti tas attiecas uz datu avotiem, vai ne? Kā mēs apgūstam datu avotus? Kā mēs to panākam pareizajā kvalitātes līmenī? Ko mēs darām ar pārvaldību? Ko mēs varam darīt, lai saskaņotu dažādus avotus? Kā mēs saglabājam ciltsrakstu? Un visa tāda veida diskusija. Un kā iegūt vairāk SQL no mūsu Hadoop? Tātad šī daļa notiek šajā līmenī.
Tad informācijas un orķestrēšanas pusē tas kļūst interesanti. Mēs sākam sasaistīt šīs atziņas ieguvumus, ko mēs iegūstam, vai mēs to sākam mainīt uz biznesa procesiem? Kā to sasaistīt ar jebkura veida metadatu modeļiem? Vai mēs savienojam punktus starp objektiem? Tātad jaunie darbības vārdi un diskusijas par to, kā mēs izmantojam šos datus, pāriet no tā, kas mums tradicionāli ir CRUD pasaulē: izveidot, lasīt, atjaunināt, izdzēst, uz pasauli, kurā tiek diskutēts par to, kā mēs iesaistāmies, dalāmies, sadarbojamies vai patīk vai kaut ko velk.
Tur mēs sākam redzēt daudz aizraujošu un jauninājumu, it īpaši par to, kā piesaistīt šo informāciju un padarīt to vērtīgu. Tā ir tehnoloģiju virzīta diskusija zem sarkanās līnijas. Virs šīs sarkanās līnijas mēs iegūstam tos pašus jautājumus, kurus vienmēr gribējām uzdot, un viens no tiem, ko vienmēr uzdodam, ir tāds, piemēram, varbūt mazumtirdzniecības jautājums jums ir šāds: “Kāpēc sarkanie džemperi pārdod labāk Alabamas štatā nekā zilas džemperi Mičiganā? " Jūs varētu domāt par to un pateikt: "Tas ir sava veida interesants." Jūs redzat šo modeli. Mēs uzdodam šo jautājumu un domājam: "Ei, ko mēs darām?" Varbūt tas attiecas uz valsts skolām - Mičiganu pret Alabamu. Labi, es to saprotu, es redzu, kurp mēs ejam. Un tā mēs sākam iegūt mājas biznesa pusi, cilvēkus ar finansēm, cilvēkus, kuriem ir tradicionālās BI iespējas, mārketinga un HR darbiniekus, sakot: "Kur ir mani modeļi?" Kā mēs nonākam pie šiem modeļiem? Un tā Hadoop pusē mēs redzam vēl vienu jauninājumu veidu. Tas patiešām ir par to, kā mēs ātrāk atjauninām ieskatu. Kā mēs veidojam šāda veida savienojumus? Tas pilnībā attiecas uz ļaudīm, kuri rīkojas tāpat kā ar reklamēšanu: tehnoloģija, kas galvenokārt mēģina savienot reklāmas un atbilstošu saturu no jebko, sākot no reāllaika cenu tīkliem un beidzot ar kontekstuālām reklāmām un reklāmu izvietošanu, un to darot lidojot.
Tāpēc tas ir interesanti. Jūs redzat Hadoop progresu no: "Hei, šeit ir tehnoloģiskais risinājums. Lūk, kas mums jādara, lai šī informācija tiktu nodota cilvēkiem." Tad, kad tas šķērso uzņēmējdarbības daļu, tas kļūst interesants. Tas ir ieskats. Kur ir izrāde? Kur ir atskaitījums? Kā mēs paredzam lietas? Kā mēs ņemam ietekmi? Un pēc tam nogādājiet to pēdējā līmenī, kur mēs faktiski redzam vēl vienu Hadoop jauninājumu kopumu, kas notiek ap lēmumu sistēmām un darbībām. Kāda ir nākamā labākā darbība? Tātad jūs zināt, ka Mičiganā zilie džemperi tiek pārdoti labāk. Jūs sēdējat uz tonnas zilu džemperi Alabamas štatā. Acīmredzams ir: "Jā, labi, lai mēs to izsūtīsim." Kā mēs to darām? Kāds ir nākamais solis? Kā mēs to sasaistīsim? Varbūt nākamā labākā darbība, varbūt tas ir ieteikums, varbūt tas ir kaut kas, kas palīdz novērst problēmu, varbūt tā nav arī darbība, kas pati par sevi ir darbība. Tātad mēs sākam redzēt, kā parādās šādi raksti. Un viss, ko jūs sakāt par galveno vērtību veikaliem Robinu, ir tas, ka tas notiek tik ātri. Tas notiek tā, kā mēs par to neesam domājuši.
Droši vien es teiktu, ka pēdējos piecos gados mēs esam palielinājušies. Mēs sākām domāt par to, kā mēs atkal varam piesaistīt galvenās vērtības veikalus, bet tieši pēdējos piecos gados cilvēki uz to raugās ļoti atšķirīgi, un tas ir tāpat kā tehnoloģiju cikli atkārtojas 40 gadu modeļos, tāpēc tas ir laipni smieklīgi, ja mēs skatāmies uz mākoni un es tāpat kā lieldatoru laika dalīšana. Mēs skatāmies uz Hadoop un, piemēram, galveno vērtību krātuvi - varbūt tas ir datu marts, kas ir mazāks par datu noliktavu - un tāpēc mēs atkal sākam redzēt šos modeļus. Tas, ko es šobrīd cenšos darīt, ir padomāt par to, ko cilvēki darīja pirms 40 gadiem? Kādas pieejas un paņēmieni un metodikas tika izmantotas, ko ierobežoja cilvēku izmantotās tehnoloģijas? Tas ir sava veida virzītājspēks šim domāšanas procesam. Tā kā mēs ejam cauri plašākam Hadoop kā rīka attēlam, kad atgriežamies un domājam par sekām uzņēmējdarbībā, tas ir sava veida ceļš, kuru parasti vedam cauri cilvēkiem, lai jūs varētu redzēt, kādi gabali, kādas detaļas ir datos lēmumu pieņemšanas ceļš. Tas ir tikai kaut kas, ar ko es vēlējos padalīties. Tā ir sava veida domāšana, ko mēs esam izmantojuši iekšēji, un, cerams, ka tā papildinās diskusiju. Tāpēc es to atgriezīšu pie jums, Ēriks.
Ēriks Kavanaghs: Tas ir fantastiski. Ja varat pieturēties pie dažiem jautājumiem un jautājumiem. Bet man patika, ka jūs to atgriezāt biznesa līmenī, jo dienas beigās tas viss attiecas uz biznesu. Tas viss ir par lietu padarīšanu un pārliecināšanos, ka naudu tērējat saprātīgi, un tas ir viens no jautājumiem, ko es jau redzēju, tāpēc runātāji, iespējams, vēlēsies padomāt, kas ir HADoop maršruta TCL. Starp tiem ir kāds salds plankums, piemēram, izmantojot biroja plauktu rīkus, lai darītu lietas tradicionālā veidā, un izmantojot jaunos rīku komplektus, jo atkal padomājiet par to, liela daļa šo lietu nav nekas jauns, tas ir tikai sava veida es domāju, ka labākais veids, kā to salikt, ir apvienošanās jaunā veidā.
Tāpēc iesim uz priekšu un iepazīstināsim mūsu draugu Ņikitu Ivanovu. Viņš ir GridGain dibinātājs un izpilddirektors. Ņikita, es došos uz priekšu un pasniegšu tev atslēgas, un es ticu, ka tu tur esi. Vai tu mani dzirdi Nikita?
Ņikita Ivanovs: Jā, es esmu šeit.
Ēriks Kavanaghs: teicami. Tātad grīda ir jūsu. Noklikšķiniet uz šī slaida. Izmantojiet lejupvērsto bultiņu un noņemiet to. Piecas minūtes.
Ņikita Ivanovs: uz kuru slaidu es noklikšķiniet?
Ēriks Kavanaghs: vienkārši noklikšķiniet uz jebkura šī slaida un pēc tam pārvietošanai izmantojiet tastatūras lejupvērstās bultiņas. Vienkārši noklikšķiniet uz paša slaida un izmantojiet lejupvērsto bultiņu.
Ņikita Ivanovs: Labi, ka tikai daži ātri slaidi par GridGain. Ko mēs darām šīs sarunas kontekstā? GridGain pamatā ražo datorā iebūvētu skaitļošanas programmatūru, un daļa no mūsu izstrādātās platformas ir Hadoop paātrinātājs atmiņā. Runājot par Hadoop, mums ir tendence domāt par sevi kā Hadoop veiktspējas speciālistiem. Tas, ko mēs galvenokārt darām, papildus mūsu galvenajai atmiņu skaitļošanas platformai, kas sastāv no tādām tehnoloģijām kā datu režģis, atmiņas straumēšana un aprēķināšanas režģi, spēs pievienot un atskaņot Hadoop paātrinātāju. Tas ir ļoti vienkārši. Būtu jauki, ja mēs varētu izveidot kaut kādu plug-and-play risinājumu, kuru var instalēt tieši Hadoop instalācijā. Ja jums, MapReduce izstrādātājam, ir nepieciešams stimuls, bez nepieciešamības rakstīt jaunu programmatūru vai koda maiņu vai izmaiņas, vai būtībā Hadoop klasterī ir jāveic minimālas konfigurācijas izmaiņas. To mēs izstrādājām.
Principā Hadoop paātrinātājs atmiņā ir balstīts uz divu Hadoop ekosistēmas komponentu optimizēšanu. Ja domājat par Hadoop, tas galvenokārt balstās uz HDFS, kas ir failu sistēma. MapReduce, kas ir pamats sacensību paralēlai vadīšanai failu sistēmā. Lai optimizētu Hadoop, mēs optimizējam abas šīs sistēmas. Mēs esam izstrādājuši atmiņas failu sistēmu, kas ir pilnībā savietojama, 100% saderīga plug-and-play ar HDFS. Varat palaist HDFS vietā, varat palaist virs HDFS. Mēs arī esam izstrādājuši atmiņā esošo MapReduce, kas ir plug-and-play saderīgs ar Hadoop MapReduce, taču ir daudz optimizāciju tam, kā darbojas MapReduce darba plūsma un kā darbojas MapReduce grafiks.
Ja paskatās, piemēram, uz šo slaidu, kur mēs parādām dublējuma veidu. Kreisajā pusē ir jūsu tipiskā operētājsistēma ar GDM, un šīs diagrammas augšpusē ir lietojumprogrammu centrs. Pa vidu jums ir Hadoop. Un Hadoop atkal ir balstīts uz HDFS un MapReduce. Tātad tas šajā diagrammā parāda, ka tieši to, ko mēs iestrādājam Hadoop kaudzē. Atkal tas ir plug-and-play; jums nav jāmaina kods. Tas vienkārši darbojas tāpat. Nākamajā slaidā mēs būtībā parādījām, kā mēs optimizējām MapReduce darbplūsmu. Iespējams, ka tā ir visinteresantākā daļa, jo tā dod jums vislielākās priekšrocības, palaižot MapReduce darbus.
Tipisks MapReduce, kad jūs iesniedzat darbu, un kreisajā pusē ir diagramma, tur ir parastā lietojumprogramma. Tāpēc parasti jūs iesniedzat darbu, un darbs nonāk darba meklētājā. 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. Paldies.
Eric Kavanagh: That's fantastic, great. Stick around for the Q&A. Finally, last but not the least, we've got Phu Hoang, CEO of DataTorrent. Let me go ahead and hand the keys to you. The keys are now yours. Click anywhere on that slide, use the down arrow on your keyboard to move them along.
Phu Hoang: Thank you so much.
So yes, I'm here to talk about DataTorrent and I actually think the story of DataTorrent is a great example of what Robin and Ray have been talking about through this session where they say that Hadoop is a great body of work, a great foundation. But it has a lot of goals. But the future is bright because the Hadoop ecosystem where more players are coming in are able to build and add value on top of that foundation to really bring it from storage to insights to action, and really that's the story of DataTorrent.
What I'm going to talk about today is really about real-time big data screening processing. What you see, as I'm interacting with customers, I've never met a single customer that says to me, "Hey, my goal is to take action hours or days after my business events arrive." In fact, they all say they want to take action immediately after the events occur. The problem with the delay is that, that is what Hadoop is today with its MapReduce paradigm. To understand why, it's worth revisiting the history of Hadoop.
I was leading much of Yahoo engineering when we hired Doug Cutting, the creator of Hadoop, and assigned over a hundred engineers to build out Hadoop to power our web search, advertising and data science processing. But Hadoop was built really as a back system to read and write and process these very large files. So while it's great disruptive technology because of its massive scalability and high ability at no cost, it has a hole in that there is a lot of latency to process these large files. Now, it is fair to say that Hadoop is now becoming the plateau operating system that is truly computing and is gaining wide adoption across many enterprises. They are still using that same process of collecting events into large files, running these batch Hadoop jobs to get there inside the next day. What enterprise customers now want is that they want those exact same insights but they want to build to get these insights much earlier, and this will enable them to really act on these events as the event happens, not after maybe hours later after it has been back processed.
Eric Kavanagh: Do you want to be moving your slides forward, just out of curiosity?
Phu Hoang: Yeah it's coming now. Let me illustrate that one example. In this example, using Hadoop in back-slope where you're constantly engaging with files, first an organization might accumulate all the events for the full day, 24 hours' worth of data. And then they batch process it, which may take another eight hours using MapReduce, and so now there is 32 hours of elapsed time before they get any insight. But with real-time stream processing, the events are coming in and are getting processed immediately, there is no accumulation time. Because we do all this processing, all in memory, the in-memory processing is also sub-second. All the time, you are reducing the elapsed time on 30 hours plus to something that is very small. If you're reducing 30 hours to 10 hours, that's valuable but if we can reduce it to a second, something profound happens. You can now act on your event while the event is still happening, and this gives enterprises the ability to understand what their products are doing, what their business is doing, what their users are doing in real time and react to it.
Let's take a look at how this happens. Really, a combination of market forces and technology has enabled a solution like DataTorrent to come together, so from a market perspective, Hadoop is really becoming the de facto big data architecture as we said, right? In an IDC study in 2013, they say that by the end of this year, two-thirds of enterprises would have deployed Hadoop and for DataTorrent, whether that's Apache Hadoop or any of our certified partners like Cloudera or Hortonworks, Hadoop is really clearly the choice for enterprise. From a technology perspective, and I think Robin and Ray alluded to this, Hadoop 2.0 was created to really enable Hadoop to extend to much more general cases than the batch MapReduce paradigm, and my co-founder, Amal, who was at Yahoo leading the development of Hadoop 2.0 really allows this layer of OS to have many more computation paradigms on top of it and real-time streaming is what we chose. By putting this layer of real-time streaming on top of YARN, you can really think of DataTorrent as the real-time equivalent of MapReduce. Whatever you can do in batch with MapReduce, you can now do in streaming with DataTorrent and we can process massive amount of data. We can slice and dice data in multiple dimensions. We have distributed computing and use YARN to give us resources. We have the full ecosystem of the open source Hadoop to enable fast application development.
Let me talk a little bit about the active capabilities of DataTorrent. In five minutes, it is hard for me to kind of give to you much in detail, but let me just discuss and re-differentiate it. First of all, sub-second scalable ingestions, right? This refers to DataTorrent's platform to be able to take that in real-time from hundreds of data sources and begin to process them immediately. This is in direct contact to the back processing of MapReduce that is in Hadoop 1.0 and events can vary in size. They may be as simple as a line in the log file or they may be much more complex like CDR, call data record in the telcom industry. DataTorrent is able to scale the ingestion dynamically up or down depending on the incoming load, and we can deal with tens of millions of incoming events per second. The other major thing here, of course, is the processing itself which is in real-time ETL logic. So once the data is in motion, it is going to go into the ETL logic where you are doing a stack transform and load, and so on. And the logic is really executed by combining a series of what we call operators connected together in a data flow grab. We have open source of over 400 operators today to allow you to build applications very quickly. And they cover everything from input connectors to all kinds of message process to database drivers and connectors where you are to load to all kinds of information to unstream.
The combination of doing all these in memory and building the scale across hundreds of nodes really drive the superior performance. DataTorrent is able to process billions of events per second with sub-second latency.
The last piece that I'd like to highlight is the high-availability architecture. DataTorrent's platform is fully post knowledge; that means that the platform automatically buffers the event and regularly checkpoints the state of the operators on the disk to ensure that there is possibly no problem. The applications can tell you in seconds with no data log and no human intervention. Simply put, data form processes billions of events and allots in data in seconds, it runs 24/7 and it never, ever goes down. The capabilities really set DataTorrent apart from the market and really make it the leading mission-critical, real-time analytics platform for enterprise. With that, we invite you to come visit our website and check us out.
Thanks.
Eric Kavanagh: Yeah, thank you so much. I'll throw a question over to you, really a comment, and let you kind of expound upon it. I really think you're on the ball here with this concept of turning over these operators and letting people use these operators almost like Legos to build big data applications. Can you kind of talk about what goes into the process of taking these operators and stitching them together, how do you actually do that?
Phu Hoang: That's a great question. So first of all, these operators are in your standard application Java Logic. We supply 400 of them. They do all kinds of processing and so to build your application, you really are just connecting operators together into a data flow graph. In our customers, we find that they use a number of operators that we have in our library as well as they take their own job of custom logic and make it an operator so that they can substantiate that into a graph.
Eric Kavanagh: OK, good. I think it's a good segue to bring in John Santaferraro from Actian because you guys have a slightly similar approach, it seems to me, in opening up a sort of management layer to be able to play around with different operators. Can you talk about what you do with respect to what tools we're just talking about, John?
John Santaferraro: Yeah, exactly. We have a library of analytics operators as well as transformational operators, operators for blending and enriching data and it is very similar. You use a drag-and-drop interface to be able to stitch together these data flows or work flows, and even analytic workflows. So it's everything from being able to connect to data, to be able to blend and enrich data, to be able to run data science or machine learning algorithms and then even being able to push that into a high-performance low-latency analytic engine. What we find is that it's all built on the open-source nine project. So we capture a lot of the operators that they are developing and then we take all of that, and via YARN, very similar to what Phu described at DataTorrent, we push that down so that it is parallelized against all of the nodes in a Hadoop cluster. A lot of it is about making the data in Hadoop much more accessible to business users and less-skilled workers, somebody besides a data scientist.
Eric Kavanagh: OK, let me go bring in Nikita once again. I'm going to throw your five up as well. Can you kind of talk about how you approach this solution vis-à-vis what these two gentlemen just talked about? How does someone actually put this stuff together and make use from GridGain?
Nikita Ivanov: Well, I think the biggest difference between us and from practically the rest of them is we don't require you to do any recording - you don't have to do anything, it's a plug-and-play. If you have an application today, it's going to work faster. You don't have to change code; you don't have to do anything; you just have to install GridGain along the side of Hadoop cluster and that's it. So that's the biggest difference and we talked to our customers. There are different myriad of solutions today that ask you to change something: programming, doing your API, using your interfaces and whatnot. Ours is very simple. You don't need to invest a lot of time into the Hadoop ecosystem, and whatever you used to do, the MapReduce or any of the tools continue to use. With GridGain, you don't have to change any single line of code, it's just going to work faster. That's the biggest difference and that's the biggest message for us.
Eric Kavanagh: Let's get Jim back in here too. Jim, your quote is killing me. I had to write it down in between that. I'll put it into some kind of deck, but the Hadoop ecosystem right now is like a pool party and the parents just came home. That is funny stuff man; that is brilliant. Can you kind of talk about how you guys come onto the scene? How do you actually implement this? How long does that take? How does all that work?
Jim Kaskade: Yes. So there are a couple of varieties depending on the target customer, but typically these days, you see evaluations where security is factored in, in some of these hardening requirements that I talked about. What has happened in some other cases, and especially last year where people had big plans to deploy, is that there was kind of a science project, if you will, or somebody was playing with the technology and had a cluster up and working and was working with it but then the security guy shows up, and if it is going to go on a live data center, it has to basically comply with the same requirements that we have for other equipment running in the data center, if it is going to be an infrastructure that we build out. Last year, we had even some banks that told us they were going to deploy 400 to 1, 000 nodes last year and they're still sitting on a 20-node cluster mainly because now a security person has been plugged in. They've got to be worried about financial compliance, about sets of information that is sitting on a cluster, and so forth. It varies by customer, but typically this is kind of what elongates the cycles and this is typical of a new technology where if you really want to deploy this in production environment, it really has to have some of these other pieces including the very valuable open-source pieces, right?
Eric Kavanagh: OK, good. Paskatīsimies. 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? Ar lielu spēku nāk liela atbildība. You really, in terms of the capabilities out there, I mean it changed me actually a long time ago. You know, I would give my ITs some of the capabilities that they have gotten now. We used to do it extraordinary amounts of what I would say was grunt work that the machines do right now and do it in parallel. They do things that we could never have imagined. I mean we would have understood mathematically, but we could never imagine doing. But there is some people understand data and Ray is completely right about this. The reason to be scared is that people will actually start getting wrong conclusions, that they will wrangle with the data and they will apply something extremely powerful and it will appear to suggest something and they will believe it without actually even being able to do anything as simple as have somebody doing audit on whether their result is actually a valid result. We used to do this all the time in the insurance company I used to work for. If anybody did any work, somebody always checks. Everything was checked by at least one person against the person who did it. These environments, the software is extremely strong but you got to have the discipline around it to use it properly. Otherwise, there'll be tears before bedtime, won't there?
Eric Kavanagh: I love that quote, that's awesome. Let me see. I'm going to go ahead and throw just for this slide up here from GridGain, can you talk about, Nikita, when you come in to play, how do you actually get these application super charged? I mean, I understand what you are doing, but what does the process look like to actually get you embedded, to get you woven in and to get all that stuff running?
Nikita Ivanov: Well, the process is relatively simple. You essentially just need to install GridGain and make a small configuration change, just to let Hadoop know that there is now the HDFS if you want to use HDFS and you have to set up which way you want to use it. You can get it from BigTop, by the way. It's probably the easiest way to install it if you're using the Hadoop. That's about it. With the new versions coming up, a little in about few weeks from now, by the end of May, we're going to have even more simplified process for this. So the whole point of the in-memory Hadoop accelerator is to, do not code. Do not make any changes to your code. The only that you need to do is install it and have enough RAM in the cluster and off you go, so the process is very simple.
Eric Kavanagh: Let me bring John Santaferraro back in. We'll take a couple more questions here. You know, John, you guys, we've been watching you from various perspectives of course. You were over at PEAR Excel; that got folded into Actian. Of course, Actian used to be called Ingres and you guys made a couple of other acquisitions. How are you stitching all of that stuff together? I realize you might not want to get too technical with this, but you guys have a lot of stuff now. You've got Data Rush. I'm not sure if it's still the same name, but you got a whole bunch of different products that have been kind of woven together to create this platform. Talk about what's going on there and how that's coming along.
John Santaferraro: The good news is, Eric, that separately in the companies that we're acquired Pervasive, PEAR Excel and even when Actian had developed, everybody developed their product with very similar architectures. Number one, they were open with regards to data and interacting with other platforms. Number two, everything was parallelized to run in a distributed environment. Number three, everything was highly optimized. What that allowed us to do is to very quickly make integration points, so that you can be creating these data flows already today. We have established the integration, so you create the data flows. You do your data blending and enriching right on Hadoop, everything parallelized, everything optimized. When you want, you move that over into our high-performance engines. Then, there's already a high-performance connection between Hadoop and our massively parallel analytic engine that does these super-low-latency things like helping a bank recalculate and recast their entire risk portfolio every two minutes and feeding that into our real-time trading system or feeding it into some kind of a desktop for the wealth manager so they can respond to the most valuable customers for the bank.
We have already put those pieces together. There's additional integration to be done. But today, we have the Actian Analytics Platform as our offering because a lot of that integration was ready to go. It has already been accomplished, so we're stitching those pieces together to drive this entire analytic value chain from connecting the data, all of the processing that you do of it, any kind of analytics you want to run, and then using it to feed into these automated business processes so that you're actually improving that activity over time. It's all about this end-to-end platform that already exists today.
Eric Kavanagh: That's pretty good stuff. And I guess, Jim, I'll bring you back in for another couple of comments, and Robin, I want to bring you in for just one big question, I suppose. Folks, we will keep all these questions - we do pass them on to the people who participated in the event today. If you ever feel a question you asked was not answered, feel free to email yours truly. You should have some information on me and how to get ahold from me. Also, just now I put a link to the full deck with slides from non-sponsoring vendors. So we put the word out to all the vendors out there in the whole Hadoop space. We said, "Tell us what your story is; tell us what's going on." It's a huge file. It's about 40-plus megabytes.
But Jim, let me bring you back in and just kind of talk about - again, I love this concept - where you're talking about the pool party that comes to an end. Could you talk about how it is that you manage to stay on top on what's happening in the open-source community? Because it's a very fast-moving environment. But I think you guys have a pretty clever strategy of serving this sort of enterprise-hardening vendor that sits on top or kind of around that. Can you talk about your development cycles and how you stay on top of what's happening?
Jim Vogt: Sure. It is pretty fast moving in terms of if you look at just a snapshot updates, but what we're shipping in functionality today is about a year to a year and a half ahead of what we can get on security capabilities out to the community today. It's not that they're not going to get there; it just takes time. It's a different process, it has contributors and so forth, and it just takes time. When we go to a customer, we need to be very well versed in the open source and very well versed in mainly the security things that we're bringing. The reason that we're actually issuing patents and submitting patents is that there is some real value in IP, intellectual property, around hardening these open-source components. When we support a customer, we have to support all the varying open-source components and all the varying distributions as we do, and we also need to have the expertise around the specific features that we're adding to that open source to create the solution that we create. As a company, although we don't want the customer to be a Hadoop expert, we don't think you need to be a mechanic to drive the car. We need to be a mechanic that understands the car and how it works and understand what's happening between our code and the open source code.
Eric Kavanagh: That's great. Phu, I'll give you one last question. Then Robin, I have one question for you and then we'll wrap up, folks. We will archive this webcast. As I suggested, we'll be up on insideanalysis.com. We'll also go ahead and have some stuff up on Techopedia. A big thank you to those folks for partnering with us to create this cool new series.
But Phu … I remember watching the demo of the stuff and I was just frankly stunned at what you guys have done. Can you explain how it is that you can achieve that level of no failover?
Phu Hoang: Sure, I think it's a great question. Really, the problem for us had three components. Number one is, you can't lose the events that are moving from operator to operator in the Hadoop cluster. So we have to have event buffering. But even more importantly, inside your operators, you may have states that you're calculating. Let's say you're actually counting money. There's a subtotal in there, so if that node goes down and it's in memory, that number is gone, and you can't start from some point. Where would you start from?
So today, you have to actually do a regular checkpoint of your operator state down to this. You put that interval so it does not become a big overhead, but when a node goes down, it can come back up and be able to go back to exactly the right state where you last checkpointed and be able to bring in the events starting from that state. That allows you to therefore continue as if the event actually has never happened. Of course, the last one is to make sure that your application manager is also fault tolerant so that doesn't go down. So all three factors need to be in place for you to say that you're fully fault tolerant.
Eric Kavanagh: Yeah, that's great. Let me go ahead and throw one last question over to Robin Bloor. So one of the attendees is asking, does anyone think that Hortonworks or another will get soaked up/invested in by a major player like Intel? I don't think there's any doubt about that. I'm not surprised, but I'm fascinated, I guess, that Intel jumped in before like an IBM or an Oracle, but I guess maybe the guys at IBM and Oracle think they've already got it covered by just co-opting what comes out of the open-source movement. What do you think about that?
Robin Bloor: It's a very curious move. We should see in light of the fact that Intel already had its own Hadoop distribution and what it has effectively done is just passed that over to Cloudera. There aren't many powers in the industry as large as Intel and it is difficult to know what your business model actually is if you have a Hadoop distribution, because it is difficult to know exactly what it is going to be used for in the future. In other words, we don't know where the revenue streams are necessarily coming from.
With somebody like Intel, they just want a lot of processes to be solved. It is going to support their main business plan the more that Hadoop is used. It's kind of easy to have a simplistic explanation of what Intel are up to. It's not so easy to guess what they might choose to do in terms of putting code on chips. I'm not 100% certain whether they're going to do that. I mean, it's a very difficult thing to call that. Their next move at the hardware level, I think, is the system on a chip. When we go to the system on a chip, you may actually want to put some basic software on the chip, so to speak. So putting HDFS on there; that might make some sense. But I don't think that that was what that money investment was about. I think all that money investment was about was just making sure that Intel had a hand in the game and is actually going forward.
In terms of who else is going to buy, that is also difficult to say. I mean, certainly the SAPs and Oracles of this world have got enough money to buy into this or IBM has got enough money to buy into it. But, you know, this is all open source. IBM never bought a Linux distribution, even though they plowed a lot of money into Linux. It didn't break their hearts that they didn't actually have a Linux distribution. They're very happy to cooperate with Red Hat. I would say maybe Red Hat will buy one of these distributions, because they know how to make that business model work, but it's difficult to say.
Eric Kavanagh: Yeah, great point. So folks, I'm going to go ahead and just share my desktop one last time here and just show you a couple of things. So after the event, check out Techopedia - you can see that on the left-hand side. Here's a story that yours truly wrote, I guess a couple of months ago or a month and a half ago, I suppose. It really kind of spun out of a lot of the experience that we had talking with various vendors and trying to dig in to understanding what exactly is going on with the space because sometimes it can be kind of difficult to navigate the buzz words and the hype and the terminology and so forth.
Also a very big thank you to all of those who have been Tweeting. We had one heck of a Tweet stream here going today. So, thank you, all of you. You see that it just goes on and on and on. A lot of great Tweets on TechWise today.
This is the first of our new series, folks. Thank you so much for tuning in. We will let you know what's going on for the next series sometime soon. I think we're going to focus on analytics probably in June sometime. And folks, with that, I think we're going to go ahead and close up our event. We will email you tomorrow with a link to the slides from today and we're also going to email you the link to that full deck, which is a huge deck. We've got about twenty different vendors with their Hadoop story. We're really trying to give you a sort of compendium of content around a particular topic. So for bedtime reading or whenever you're interested, you can kind of dive in and try to get that strategic view of what's going on here in the industry.
Ar to mēs jūs atvadīsimies, ļaudis. 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. Labdien, līdz.
