Nota Editor: Ini adalah transkrip sesebuah Webcast langsung. Anda boleh melihat siaran web sepenuhnya di sini.
Eric Kavanagh: Tuan dan lelaki, sudah tiba masanya untuk menjadi bijak! Ia adalah masa untuk TechWise, rancangan baru! Nama saya Eric Kavanagh. Saya akan menjadi moderator anda untuk episod sulung TechWise kami. Itulah betul. Ini adalah perkongsian Techopedia dan Kumpulan Bloor, sudah tentu, Inside Analysis ketenaran.
Nama saya Eric Kavanagh. Saya akan menyederhanakan acara yang benar-benar menarik dan terlibat ini. Kami akan menggali jauh ke dalam tenunan untuk memahami apa yang sedang berlaku dengan perkara besar ini yang dipanggil Hadoop. Apakah gajah di dalam bilik? Ia dipanggil Hadoop. Kami akan cuba untuk mencari apa yang dimaksudkan dan apa yang berlaku dengannya.
Pertama sekali, terima kasih kepada penaja kami, GridGain, Actian, Zettaset dan DataTorrent. Kami akan mendapat beberapa perkataan ringkas dari setiap daripada mereka pada akhir acara ini. Kami juga akan mempunyai Q & A, jadi jangan malu - hantar soalan anda pada bila-bila masa.
Kami akan menggali butiran dan membuang soalan-soalan yang sukar di pakar kami. Dan bercakap tentang pakar, hei, ada mereka. Oleh itu, kita akan mendengar dari Dr Robin Bloor sendiri, dan orang-orang, saya sangat teruja untuk mempunyai legenda Ray Wang, penganalisis utama dan pengasas Constellation Research. Dia dalam talian hari ini untuk memberi kita fikirannya dan dia seperti Robin bahawa dia sangat pelbagai dan benar-benar memberi tumpuan kepada banyak bidang yang berbeza dan mempunyai keupayaan untuk mensintesis mereka dan untuk benar-benar memahami apa yang sedang berlaku di dalam bidang ini teknologi maklumat dan pengurusan data.
Jadi, ada gajah comel yang kecil itu. Dia berada di awal jalan, seperti yang anda lihat. Ia baru bermula sekarang, ia semacam permulaan, perkara Hadoop keseluruhan ini. Sudah tentu, pada tahun 2006 atau 2007, saya rasa, adalah apabila ia dikeluarkan kepada komuniti sumber terbuka, tetapi terdapat banyak perkara yang berlaku, orang ramai. Terdapat perkembangan besar. Malah, saya ingin memaparkan cerita itu, jadi saya akan melakukan bahagian desktop yang cepat, sekurang-kurangnya saya rasa saya. Mari kita buat bahagian desktop yang cepat.
Saya menunjukkan kepada anda ini orang-orang cerita gila dan gila. Jadi Intel melabur $ 740 juta untuk membeli 18 peratus daripada Cloudera. Saya fikir dan saya suka, "Krismas Suci!" Saya mula melakukan matematik dan ia seperti, "Ini penilaian sebanyak $ 4.1 bilion." Mari kita fikirkan perkara ini buat beberapa saat. Maksud saya, jika WhatsApp bernilai $ 2 bilion, saya rasa Cloudera mungkin bernilai $ 4.1 bilion, kan? Maksud saya, mengapa tidak? Beberapa nombor ini hanya keluar dari tetingkap hari ini, orang ramai. Maksud saya, biasanya dari segi pelaburan, anda mempunyai EBITDA dan semua mekanisme lain yang lain, gandaan pendapatan dan sebagainya. Nah, ia akan menjadi satu daripada banyak hasil untuk mendapatkan $ 4.1 bilion untuk Cloudera, yang merupakan sebuah syarikat yang hebat. Jangan salahkan saya - ada beberapa orang yang sangat pintar di sana termasuk lelaki yang memulakan keseluruhan kegilaan Hadoop, Doug Cutting, dia ada di sana - banyak orang yang sangat bijak yang melakukan banyak, benar-benar perkara-perkara yang sejuk, tetapi yang paling penting ialah $ 4.1 bilion, itu banyak wang.
Oleh itu, di sini adalah jenis masa yang sangat menarik untuk melepasi kepala saya sekarang yang merupakan cip, Intel. Pereka cip mereka membawa untuk melihat beberapa cip dioptimumkan Hadoop - Saya fikir begitu, orang. Itu hanya tekaan saya. Itu hanya khabar angin, datang dari saya, jika anda mahu, tetapi ia agak masuk akal. Dan apa maksudnya?
Jadi inilah teori saya. Apa yang berlaku? Banyak perkara ini bukan perkara baru. Pemprosesan selari secara besar-besaran tidak terlalu baru. Pemprosesan selari pasti tidak baru. Saya telah berada dalam dunia superkomputer untuk seketika. Banyak perkara-perkara yang sedang berlaku bukanlah perkara baru, tetapi terdapat kesedaran umum bahawa ada cara baru untuk menyerang beberapa masalah ini. Apa yang saya lihat berlaku, jika anda melihat beberapa vendor besar Cloudera atau Hortonworks dan beberapa orang lain, apa yang mereka lakukan dengan benar jika anda mendidihkannya ke tahap sulingan yang paling berbutir adalah pembangunan aplikasi. Itulah yang mereka lakukan.
Mereka sedang merekabentuk aplikasi baru - sebahagiannya melibatkan analisis perniagaan; sesetengah daripada mereka hanya melibatkan sistem supercharging. Salah satu vendor kami yang telah membincangkannya, mereka melakukan perkara sedemikian sepanjang hari, pada hari ini. Tetapi jika ia sangat baru, sekali lagi jawapannya adalah "tidak benar-benar, " tetapi ada perkara besar yang berlaku, dan secara peribadi, saya fikir apa yang berlaku dengan Intel membuat pelaburan besar ini adalah langkah membuat pasaran. Mereka melihat dunia hari ini dan melihat bahawa ia adalah jenis dunia monopoli hari ini. Ada Facebook dan mereka telah mengalahkan MySpace miskin. LinkedIn telah mengalahkan orang miskin daripada Siapa Yang. Jadi, anda melihat sekeliling dan ini satu perkhidmatan yang menguasai semua ruang yang berbeza di dunia sekarang ini, dan saya fikir idea Intel akan membuang semua cip mereka di Cloudera dan cuba mengangkatnya ke bahagian atas timbunan - itu hanya teori saya.
Oleh itu, seperti yang saya katakan, kita akan mempunyai sesi Q & A yang panjang, jadi jangan malu. Hantar soalan anda pada bila-bila masa. Anda boleh melakukannya menggunakan komponen Q & A konsol webcast anda. Dan dengan itu, saya mahu mendapatkan kandungan kami kerana kami mempunyai banyak perkara untuk diteruskan.
Jadi, Robin Bloor, izinkan saya menyerahkan kunci kepada anda dan lantai adalah milik anda.
Robin Bloor: Baiklah, Eric, terima kasih kerana itu. Mari kita bawa gajah tarian. Ia adalah satu perkara yang ingin tahu, sebenarnya, gajah adalah satu-satunya mamalia tanah yang tidak dapat benar-benar melompat. Semua gajah ini dalam grafik tertentu telah mendapat sekurang-kurangnya satu kaki di atas tanah, jadi saya rasa ia boleh dilaksanakan, tetapi pada tahap tertentu, ini jelas gajah Hadoop, sangat, sangat mampu.
Persoalannya, sebenarnya, saya fikir perlu dibincangkan dan harus dibincangkan dalam semua kejujuran. Ia perlu dibincangkan sebelum anda pergi ke mana-mana sahaja, yang benar-benar mula bercakap mengenai apa sebenarnya Hadoop.
Salah satu perkara yang benar-benar berasal dari asas permainan manusia adalah kedai nilai penting. Kami digunakan untuk mempunyai kedai nilai utama. Kami pernah menggunakannya di kerangka utama IBM. Kami mempunyai mereka pada minicomputers; DEC VAX mempunyai fail IMS. Terdapat keupayaan ISAM yang hampir setiap miniatur komputer anda boleh mendapatkan tangan anda. Tetapi pada sekitar 80-an, Unix masuk dan Unix tidak mempunyai sebarang kedai penting. Apabila Unix mengembangkannya, mereka maju dengan pantas. Apa yang berlaku benar-benar adalah vendor pangkalan data, terutamanya Oracle, pergi mengukus di sana dan mereka menjual pangkalan data anda untuk menjaga setiap data yang anda peduli untuk menguruskan pada Unix. Windows dan Linux ternyata sama. Oleh itu, industri ini menjadi sebahagian terbaik daripada 20 tahun tanpa kedai utama nilai utama. Nah, sekarang sudah pulih. Bukan sahaja ia kembali, ia boleh diukur.
Kini, saya fikir ia benar-benar menjadi asas kepada apa yang sebenarnya Hadoop dan tahap tertentu, ia menentukan di mana ia akan pergi. Apa yang kita suka tentang kedai-kedai bernilai utama? Mereka yang sudah tua seperti saya dan benar-benar ingat bekerja dengan kedai-kedai bernilai utama menyedari bahawa anda cukup menggunakannya untuk secara tidak rasmi menubuhkan pangkalan data, tetapi hanya secara tidak rasmi. Anda tahu metadata dengan cepat menghargai kedai-kedai dalam kod program, tetapi anda sebenarnya boleh membuat fail luaran itu, dan anda boleh jika anda mahu mula merawat kedai nilai penting sedikit seperti pangkalan data. Tetapi sudah tentu ia tidak mempunyai semua keupayaan pemulihan yang mempunyai pangkalan data dan ia tidak mempunyai banyak perkara yang dahulunya pangkalan data yang ada sekarang, tetapi ia merupakan ciri yang sangat berguna untuk pemaju dan itulah salah satu sebab yang saya fikirkan bahawa Hadoop telah terbukti sangat popular - semata-mata kerana ia telah menjadi coder, pengaturcara, pemaju yang cepat. Mereka menyedari bahawa bukan sahaja nilai utama kedai tetapi ia adalah skala nilai nilai utama. Ia berselerak cukup selama-lamanya. Saya menghantar skala ini ke beribu-ribu pelayan, jadi itu perkara yang sangat besar mengenai Hadoop, itulah yang ia.
Ia juga mempunyai di atasnya MapReduce, yang merupakan algoritma paralelisasi, tetapi sebenarnya itu, pada pendapat saya, tidak penting. Jadi, anda tahu, hadoop adalah bunglon. Ia bukan hanya sistem fail. Saya telah melihat pelbagai jenis tuntutan yang dibuat untuk Hadoop: ia merupakan pangkalan data rahsia; ia bukan pangkalan data rahsia; ia adalah kedai biasa; ia adalah kotak peralatan analisis; ia adalah persekitaran ELT; alat pembersihan data; ia adalah gudang data platform streaming; ia adalah kedai arkib; ia adalah ubat untuk kanser, dan sebagainya. Kebanyakan perkara ini benar-benar tidak benar untuk Hadoop vanila. Hadoop mungkin prototaip - ia semestinya persekitaran prototaip untuk pangkalan data SQL, tetapi ia tidak benar-benar ada, jika anda meletakkan ruang umur dengan katalog umur lebih daripada Hadoop, anda mempunyai sesuatu yang kelihatan seperti pangkalan data, tetapi ia tidak benar-benar apa sesiapa yang akan memanggil pangkalan data dari segi keupayaan. Banyak keupayaan ini, anda pasti boleh mendapatkannya di Hadoop. Sudah pasti banyak dari mereka. Sebenarnya, anda boleh mendapatkan beberapa sumber Hadoop, tetapi Hadoop sendiri bukanlah apa yang saya panggil beroperasi secara keras, dan oleh itu kesepakatan tentang Hadoop, sebenarnya saya tidak akan ada apa-apa lagi, adakah anda sememangnya memerlukan ketiga -produk pihak untuk meningkatkannya.
Oleh itu, bercakap tentang anda hanya boleh membuang beberapa baris kerana saya bercakap tentang Hadoop overreach. Pertama sekali, keupayaan pertanyaan masa nyata, baik anda tahu masa nyata adalah jenis waktu perniagaan, benar-benar, hampir selalu prestasi kritis sebaliknya. Maksud saya, kenapa kamu akan menjadi jurutera untuk masa sebenar? Hadoop tidak benar-benar melakukan ini. Ia melakukan sesuatu yang dekat dengan masa sebenar tetapi ia tidak benar-benar melakukan perkara nyata. Ia melakukan streaming, tetapi ia tidak melakukan streaming dalam cara yang saya akan panggil benar-benar misi kritikal jenis platform streaming aplikasi boleh lakukan. Terdapat perbezaan antara pangkalan data dan kedai yang jelas. Menyegerakkannya ke lebih dari Hadoop memberikan anda kedai data yang jelas. Itulah seperti pangkalan data tetapi ia tidak sama dengan pangkalan data. Hadoop dalam bentuk asalnya, pada pendapat saya, tidak benar-benar layak sebagai pangkalan data sama sekali kerana ia agak kurang dari beberapa perkara yang harus diperoleh oleh pangkalan data. Hadoop memang banyak, tetapi ia tidak melakukannya dengan baik. Sekali lagi, keupayaan itu ada tetapi kita jauh dari kemampuan yang mempunyai keupayaan cepat dalam semua bidang ini.
Perkara lain untuk memahami tentang Hadoop adalah, ia agak jauh sejak ia dibangunkan. Ia telah dibangunkan pada hari-hari awal; ia telah dibangunkan apabila kami mempunyai pelayan yang sebenarnya hanya mempunyai satu pemproses bagi setiap pelayan. Kami tidak pernah mempunyai pemproses multi-teras dan dibina untuk menjalankan grid, melancarkan grid dan severs. Salah satu tujuan reka bentuk Hadoop adalah untuk tidak pernah kehilangan kerja. Dan itu benar-benar mengenai kegagalan cakera, kerana jika anda telah mendapat beratus-ratus pelayan, maka kemungkinannya, jika anda mempunyai cakera pada server, kemungkinannya akan mendapatkan ketersediaan waktu uptime seperti 99.8. Ini bermakna bahawa anda akan mendapat purata kegagalan salah satu pelayan itu setiap 300 atau 350 hari, satu hari dalam setahun. Jadi jika anda mempunyai beratus-ratus ini, kemungkinan akan berlaku pada mana-mana hari dalam tahun ini bahawa anda akan mendapat kegagalan pelayan.
Hadoop dibina khusus untuk menangani masalah itu - supaya, sekiranya sesuatu yang gagal, ia mengambil gambar semua yang berlaku, pada setiap pelayan tertentu dan ia dapat memulihkan kerja kelompok yang berjalan. Dan itu semua yang sebenarnya berlari ke atas Hadoop adalah pekerjaan batch dan itulah kemampuan yang sangat berguna, ia harus dikatakan. Beberapa pekerjaan batch yang sedang dijalankan - terutamanya di Yahoo, di mana saya fikir Hadoop adalah sejenis dilahirkan - akan berjalan selama dua atau tiga hari, dan jika ia gagal selepas sehari, anda benar-benar tidak mahu kehilangan kerja yang telah dilakukan. Jadi itu adalah titik reka bentuk di belakang ketersediaan Hadoop. Anda tidak akan memanggil ketersediaan tinggi, tetapi anda boleh menyebutnya ketersediaan yang tinggi untuk kerja kelompok bersiri. Itu mungkin cara untuk melihatnya. Ketersediaan tinggi sentiasa dikonfigurasikan mengikut ciri-ciri garis kerja. Pada masa ini, Hadoop hanya boleh dikonfigurasikan untuk kerja batch bersiri yang berkaitan dengan pemulihan semacam itu. Ketersediaan tinggi perusahaan mungkin paling baik dari segi transaksi LLP. Saya percaya jika anda tidak memandangnya sebagai sesuatu yang tepat masa, Hadoop tidak berbuat demikian. Ini mungkin cara yang jauh dari melakukan itu.
Tapi inilah perkara yang indah tentang Hadoop. Graf itu di sebelah kanan yang telah mendapat senarai vendor di sekitar tepi dan semua baris di atasnya menunjukkan hubungan antara vendor dan produk lain dalam ekosistem Hadoop. Jika anda melihatnya, itu adalah ekosistem yang sangat mengagumkan. Ia agak luar biasa. Kami jelas, kami bercakap dengan banyak vendor dari segi keupayaan mereka. Antara vendor yang saya telah bicarakan, terdapat beberapa keupayaan yang sangat luar biasa menggunakan Hadoop dan memori, cara menggunakan Hadoop sebagai arkib termampat, menggunakan Hadoop sebagai persekitaran ETL, dan sebagainya dan sebagainya. Tetapi sebenarnya, jika anda menambah produk ke Hadoop sendiri, ia berfungsi dengan sangat baik dalam ruang tertentu. Jadi semasa saya kritikal terhadap Hadoop asli, saya tidak kritis terhadap Hadoop apabila anda benar-benar menambahkan kuasa kepadanya. Pada pendapat saya, populariti Hadoop jenis menjamin masa depannya. Oleh yang saya maksudkan, walaupun setiap baris kod yang ditulis setakat Hadoop hilang, saya tidak percaya API HDFS akan hilang. Dengan kata lain, saya fikir sistem fail, API, berada di sini untuk kekal, dan mungkin YARN, penjadual yang melihatnya.
Apabila anda benar-benar melihatnya, itu adalah keupayaan yang sangat penting dan saya akan jenis lilin tentang itu dalam satu minit, tetapi perkara lain yang, katakan, orang yang menarik tentang Hadoop adalah gambaran keseluruhan sumber terbuka. Oleh itu, ia berbaloi menerusi gambaran sumber terbuka dari segi apa yang saya anggap sebagai keupayaan sebenar. Walaupun Hadoop dan semua komponennya pasti dapat melakukan apa yang kita panggil panjang data - atau kerana saya lebih suka memanggilnya, takungan data - itu pasti kawasan pementasan yang sangat baik untuk menjatuhkan data ke dalam organisasi atau untuk mengumpul data dalam organisasi - sangat baik untuk kotak pasir dan untuk data pemancing. Ia sangat baik sebagai platform pembangunan prototaip yang mungkin anda laksanakan pada penghujung hari, tetapi anda tahu sebagai persekitaran pembangunan hampir semua yang anda inginkan ada. Sebagai kedai arkib, ia cukup mendapat semua yang anda perlukan, dan sudah tentu ia tidak mahal. Saya tidak fikir kita harus menceraikan kedua-dua perkara ini dari Hadoop walaupun mereka tidak secara rasmi, jika anda suka, komponen Hadoop. Baji dalam talian telah membawa sejumlah besar analitik ke dalam dunia sumber terbuka dan banyak analitik yang kini sedang dijalankan di Hadoop kerana ini memberikan anda persekitaran yang mudah di mana anda benar-benar dapat mengambil banyak data luaran dan hanya mula bermain di kotak pasir analitis.
Dan kemudian anda mempunyai keupayaan sumber terbuka, yang keduanya adalah pembelajaran mesin. Kedua-dua mereka sangat kuat dalam erti kata bahawa mereka melaksanakan algoritma analisis yang kuat. Sekiranya anda meletakkan perkara-perkara ini bersama-sama, anda mempunyai beberapa keupayaan yang sangat penting, yang dalam satu cara atau yang sangat mungkin - sama ada ia berkembang sendiri atau sama ada vendor masuk untuk mengisi kepingan yang hilang - ia mungkin akan berterusan untuk masa yang lama dan sudah tentu saya fikir pembelajaran mesin sudah mempunyai kesan yang sangat besar di dunia.
Evolusi Hadoop, YARN mengubah segalanya. Apa yang telah berlaku adalah MapReduce cukup banyak dikimpal kepada sistem fail awal HDFS. Apabila YARN diperkenalkan, ia mewujudkan keupayaan penjadualan dalam pembebasan pertama. Anda tidak menjangkakan penjadualan yang sangat canggih dari pembebasan pertama, tetapi itu bermakna bahawa ia tidak lagi semestinya persekitaran patch. Ia adalah satu persekitaran di mana pelbagai pekerjaan boleh dijadualkan. Sebaik sahaja itu berlaku, terdapat satu siri penjual yang telah menjauhkan diri dari Hadoop - mereka hanya masuk dan berhubung dengannya kerana kemudian mereka hanya dapat melihatnya sebagai persekitaran penjadualan di atas sistem fail dan mereka boleh menangani perkara untuk ia. Terdapat juga vendor pangkalan data yang telah melaksanakan pangkalan data mereka di HDFS, kerana mereka hanya mengambil enjin dan hanya meletakkannya di HDFS. Dengan meluncurkan dan dengan YARN, ia menjadi persekitaran yang sangat menarik kerana anda boleh membuat aliran kerja yang kompleks ke atas HDFS dan ini benar-benar bermakna bahawa anda boleh mula memikirkannya sebagai platform yang dapat menjalankan banyak pekerjaan serentak dan mendorong dirinya ke arah titik melakukan perkara-perkara kritikal misi. Sekiranya anda akan melakukannya, anda mungkin perlu membeli beberapa komponen pihak ketiga seperti keselamatan dan sebagainya dan sebagainya, yang Hadoop sebenarnya tidak mempunyai akaun audit untuk mengisi jurang, tetapi anda masuk ke titik di mana walaupun dengan sumber terbuka asli anda boleh melakukan beberapa perkara yang menarik.
Dari segi yang saya fikir Hadoop sebenarnya akan pergi, saya percaya bahawa HDFS akan menjadi sistem fail berskala lalai dan oleh itu akan menjadi OS, sistem operasi, untuk grid untuk aliran data. Saya fikir ia telah mendapat masa depan yang besar dan saya tidak fikir ia akan berhenti di sana. Dan saya fikir sebenarnya ekosistem hanya membantu kerana kebanyakan orang, semua vendor di ruang angkasa, sebenarnya mengintegrasikan Hadoop dalam satu cara atau yang lain dan mereka hanya membenarkannya. Dari segi titik lain yang patut dibuat, dari segi overage Hadoop, bukankah itu bukan platform yang sangat baik ditambah dengan selari. Jika anda benar-benar melihat apa yang dilakukannya, apa yang sebenarnya dilakukan ialah mengambil gambar secara kerap pada setiap pelayan kerana ia melaksanakan tugas MapReduce. Sekiranya anda akan membuat reka bentuk untuk pemesejan yang benar-benar pantas, anda tidak akan melakukan apa-apa seperti itu. Sebenarnya, anda mungkin tidak menggunakan MapReduce sendiri. MapReduce hanyalah apa yang saya katakan separuh mampu paralelisme.
Terdapat dua pendekatan untuk parallelism: satu dengan proses pipelining dan yang lain adalah dengan membahagikan data MapReduce dan pembahagian data jadi ada banyak pekerjaan di mana MapReduce tidak semestinya cara yang paling cepat untuk melakukannya, tetapi akan memberi anda paralelisme dan tidak ada yang mengambil dari itu. Apabila anda mempunyai banyak data, kuasa semacam itu biasanya tidak berguna. YARN, seperti yang saya katakan, adalah kemampuan penjadualan yang sangat muda.
Hadoop adalah, jenis lukisan di pasir di sini, Hadoop bukanlah gudang data. Setakat ini menjadi gudang data bahawa ia hampir satu cadangan yang tidak masuk akal untuk mengatakan bahawa ia adalah. Dalam gambarajah ini, apa yang saya tunjukkan di sepanjang bahagian atas adalah sejenis aliran data, pergi dari takungan data Hadoop ke dalam pangkalan data skala besar yang sebenarnya kita lakukan, sebuah gudang data perusahaan. Saya menunjukkan pangkalan data warisan, memberi makan data ke dalam gudang data dan aktiviti offload yang mewujudkan pangkalan data offload dari gudang data, tetapi itu sebenarnya gambaran yang saya mula melihat muncul, dan saya akan mengatakan ini seperti generasi pertama apa yang berlaku kepada gudang data dengan Hadoop. Tetapi jika anda melihat gudang data itu sendiri, anda sedar bahawa di bawah gudang data, anda mempunyai pengoptimuman. Anda mempunyai pekerja pertanyaan teragih atas proses yang banyak yang duduk di atas mungkin banyak jumlah cakera yang besar. Itulah yang berlaku dalam gudang data. Itu sebenarnya jenis seni bina yang dibina untuk gudang data dan memerlukan masa yang lama untuk membina sesuatu seperti itu, dan Hadoop tidak mempunyai apa-apa pun. Oleh itu Hadoop bukan gudang data dan ia tidak akan menjadi satu, pada pendapat saya, dalam waktu dekat.
Ia mempunyai takungan data relatif ini, dan ia agak kelihatan menarik jika anda hanya melihat dunia sebagai satu siri peristiwa yang mengalir ke dalam organisasi. Itulah yang saya tunjukkan di sebelah kiri gambarajah ini. Memandangkan ia melalui keupayaan penapisan dan penghalaan dan perkara yang perlu dihidupkan untuk streaming akan disedut dari aplikasi penstriman dan segala-galanya akan terus ke takungan data di mana ia disediakan dan dibersihkan, dan kemudian diluluskan oleh ETL sama ada satu data tunggal gudang atau gudang data logik yang terdiri daripada pelbagai enjin. Inilah pendapat saya, garis pembangunan semula jadi untuk Hadoop.
Dari segi ETW, salah satu daripada perkara-perkara yang bernilai semestinya menunjukkan bahawa gudang data itu sendiri telah dipindahkan - bukannya apa itu. Sudah tentu, pada masa kini, anda mengharapkan keupayaan hierarki setiap data hierarki mengenai apa yang orang, atau sesetengah orang, memanggil dokumen dalam gudang data. Itulah JSON. Mungkin, pertanyaan rangkaian yang merupakan pangkalan grafik, mungkin analitik. Jadi, apa yang kita sedang menuju ke arah adalah ETW yang sebenarnya telah mendapat beban kerja yang lebih kompleks daripada yang kita gunakan. Jadi itu agak menarik kerana dengan cara itu ia bermakna gudang data semakin canggih, dan kerana itu, ia akan menjadi lebih lama sebelum Hadoop mendapat di mana-mana dekat dengannya. Makna gudang data memperluaskan, tetapi ia masih termasuk pengoptimuman. Anda harus mempunyai keupayaan pengoptimuman, bukan hanya melalui pertanyaan sekarang tetapi atas semua aktiviti ini.
Itu sahaja, betul. Itulah yang saya mahu katakan tentang Hadoop. Saya fikir saya boleh menyerahkan kepada Ray, yang tidak mendapat sebarang slaid, tetapi dia sentiasa pandai bercakap.
Eric Kavanagh: Saya akan mengambil slaid. Ada kawan kita, Ray Wang. Jadi, Ray, apa pemikiran kamu tentang semua ini?
Ray Wang: Sekarang, saya fikir ia mungkin salah satu sejarah yang paling ringkas dan hebat di kedai-kedai utama dan di mana Hadoop telah berurusan dengan perusahaan yang berada di luar, jadi saya sentiasa belajar banyak ketika mendengar Robin.
Sebenarnya, saya mempunyai satu slaid. Saya boleh muncul satu slaid di sini.
Eric Kavanagh: Hanya pergi ke depan dan klik pada, klik mula dan pergi untuk berkongsi desktop anda.
Ray Wang: Ada, anda pergi. Saya sebenarnya akan berkongsi. Anda boleh melihat apl itu sendiri. Mari lihat bagaimana ia berlaku.
Semua ini bercakap tentang Hadoop dan kemudian kita pergi jauh ke dalam perbualan tentang teknologi yang ada dan di mana Hadoop sedang menuju, dan banyak kali saya suka mengambilnya kembali untuk benar-benar mempunyai perbincangan perniagaan. Banyak perkara yang berlaku di sisi teknologi adalah benar-benar bahagian ini di mana kita telah bercakap tentang gudang data, pengurusan maklumat, kualiti data, menguasai data itu, dan sebagainya kita cenderung melihatnya. Oleh itu, jika anda melihat graf ini di sini di bahagian paling bawah, itu sangat menarik bahawa jenis individu yang kita bincangkan mengenai perbincangan mengenai Hadoop. Kami mempunyai ahli teknologi dan saintis data yang tidak tahu, mempunyai banyak keseronokan, dan biasanya mengenai sumber data, bukan? Bagaimanakah kita menguasai sumber data? Bagaimanakah kita dapat mencapai tahap kualiti yang betul? Apa yang kita buat mengenai tadbir urus? Apa yang boleh kita lakukan untuk memadankan pelbagai jenis sumber? Bagaimanakah kita menjaga keturunan? Dan semua perbincangan semacam itu. Dan bagaimana kita mendapatkan lebih banyak SQL daripada Hadoop kami? Jadi bahagian itu berlaku pada tahap ini.
Kemudian di sisi maklumat dan orkestrasi, ini adalah tempat yang menarik. Kami mula mengikat keluaran wawasan ini yang kami dapatkan atau kami menariknya dari proses perniagaan ke belakang? Bagaimanakah kita mengikatnya kembali ke apa-apa jenis model metadata? Adakah kita menghubungkan titik antara objek? Kata kerja dan perbincangan baru tentang bagaimana kami menggunakan data itu, bergerak dari apa yang kami secara tradisinya berada dalam dunia CRUD: membuat, membaca, mengemas kini, memadam, ke dunia yang membincangkan tentang bagaimana kami terlibat atau berkongsi atau bekerjasama atau suka atau menarik sesuatu.
Di sinilah kita mula melihat banyak keseronokan dan inovasi, terutamanya tentang cara menarik maklumat ini dan membawanya kepada nilai. Itulah perbincangan yang didorong oleh teknologi di bawah garis merah. Di atas garis merah, kami mendapat soalan yang selalu kami tanya dan salah satu daripada mereka yang kami selalu tampil seperti, sebagai contoh, mungkin pertanyaan dalam runcit untuk anda adalah seperti, "Mengapa baju sejuk merah menjual lebih baik di Alabama daripada baju biru di Michigan? " Anda boleh memikirkannya dan berkata, "Itu agak menarik." Anda melihat pola itu. Kami bertanya soalan itu, dan kami tertanya-tanya, "Hei, apa yang kita buat?" Mungkin ia mengenai sekolah negeri - Michigan berbanding Alabama. OK, saya dapati ini, saya dapat melihat ke mana kita pergi. Oleh itu, kita mula mendapat bahagian perniagaan rumah, orang dalam kewangan, orang yang mempunyai keupayaan BI tradisional, orang dalam pemasaran, dan orang dalam HR berkata, "Di manakah corak saya?" Bagaimanakah cara kita mendapatkan pola tersebut? Dan jadi kita melihat cara inovasi lain di sisi Hadoop. Ini benar-benar mengenai bagaimana kita mengatasi pandangan kemas kini dengan lebih cepat. Bagaimanakah kita membuat hubungan ini? Ia pergi ke arah orang-orang yang berbuat seperti, iklan: teknologi yang pada asasnya cuba menyambungkan iklan dan kandungan yang berkaitan dari apa-apa dari rangkaian pembidaan masa nyata ke iklan kontekstual dan penempatan iklan dan melakukan itu dengan cepat.
Oleh itu, ia menarik. Anda melihat perkembangan Hadoop dari, "Hei, inilah penyelesaian teknologi. Inilah yang perlu kita lakukan untuk mendapatkan maklumat ini kepada orang-orang." Kemudian apabila ia melintasi garis bahagian perniagaan, ini adalah di mana ia menjadi menarik. Ia adalah wawasan. Di manakah prestasi? Di manakah potongan? Bagaimana kita memprediksi perkara? Bagaimanakah kita mengambil pengaruh? Dan kemudian bawa ke tahap terakhir di mana kita benar-benar melihat satu lagi set inovasi Hadoop yang berlaku di sekitar sistem dan tindakan keputusan. Apa tindakan terbaik yang akan datang? Jadi anda tahu sweater biru menjual lebih baik di Michigan. Anda duduk di atas satu tan baju biru biru di Alabama. Perkara yang jelas ialah, "Ya, mari mari kita kirimkan ke sana." Bagaimana kita melakukannya? Apa langkah seterusnya? Bagaimana kita mengikat semula? Mungkin tindakan terbaik yang akan datang, mungkin ia satu cadangan, mungkin ia adalah sesuatu yang membantu anda mencegah masalah, mungkin tindakan itu bukan tindakan, itu sendiri. Oleh itu, kita mula melihat corak jenis ini muncul. Dan keindahan ini kembali kepada apa yang anda katakan tentang kedai-kedai bernilai utama, Robin, adalah bahawa ia berlaku begitu pantas. Ia berlaku dengan cara yang kita tidak memikirkannya dengan cara ini.
Mungkin saya akan mengatakan dalam tempoh lima tahun yang lalu kami mengambil. Kami mula berfikir tentang bagaimana kami dapat memanfaatkan kedai-kedai nilai utama sekali lagi, tetapi hanya dalam tempoh lima tahun yang lalu, orang melihat ini sangat berbeza dan ia seperti kitaran teknologi mengulangi sendiri dalam corak 40 tahun, jadi ini baik sesuatu yang lucu di mana kita melihat awan dan saya seperti perkongsian masa mainframe. Kami melihat Hadoop dan seperti kedai nilai penting - mungkin ia adalah data mart, kurang daripada gudang data - dan oleh itu kita mula melihat corak ini sekali lagi. Apa yang saya cuba lakukan sekarang ialah berfikir tentang apa yang dilakukan orang 40 tahun yang lalu? Apakah pendekatan dan teknik dan metodologi yang digunakan yang dihadkan oleh teknologi yang dimiliki orang? Itu semacam memandu proses pemikiran ini. Oleh itu, ketika kita meneruskan gambaran yang lebih besar dari Hadoop sebagai alat, ketika kita kembali dan berfikir tentang implikasi bisnis, ini adalah jenis jalan yang biasanya kita ambil orang sehingga Anda dapat melihat bagian-bagian apa, apa bahagian dalam data keputusan laluan. Ia hanya sesuatu yang saya mahu berkongsi. Ia sememangnya berfikir bahawa kita telah menggunakan secara dalaman dan mudah-mudahan menambah perbincangan. Jadi saya akan mengembalikannya kepada anda, Eric.
Eric Kavanagh: Itu hebat. Sekiranya anda boleh bertahan untuk beberapa Q & A. Tetapi saya suka bahawa anda membawanya semula ke peringkat perniagaan kerana pada penghujung hari, itu semua tentang perniagaan. Ini semua tentang perkara-perkara yang dilakukan dan pastikan anda membelanjakan wang dengan bijak dan itu adalah salah satu soalan yang saya lihat, jadi pembicara mungkin ingin memikirkan tentang apakah TCL akan pergi ke laluan Hadoop. Terdapat beberapa tempat yang manis di antara, contohnya, menggunakan alat rak pejabat untuk melakukan perkara-perkara dalam beberapa cara tradisional dan menggunakan set alat baru, kerana sekali lagi, fikirkannya, banyak perkara ini tidak baru, itu hanya semacam koala dengan cara yang baru adalah, saya rasa, cara terbaik untuk meletakkannya.
Jadi mari kita pergi dan memperkenalkan kawan kita, Nikita Ivanov. Beliau adalah pengasas dan Ketua Pegawai Eksekutif GridGain. Nikita, saya akan pergi ke depan dan menyerahkan kunci kepada anda, dan saya percaya anda di luar sana. Bolehkah anda mendengar saya Nikita?
Nikita Ivanov: Ya, saya di sini.
Eric Kavanagh: Cemerlang. Jadi lantai adalah milik anda. Klik pada slaid itu. Gunakan anak panah bawah, dan bawa ia. Lima minit.
Nikita Ivanov: Slaid yang saya klik?
Eric Kavanagh: Cukup klik di mana sahaja di slaid itu dan kemudian anda menggunakan anak panah bawah pada papan kekunci untuk bergerak. Hanya klik pada slaid itu sendiri dan gunakan anak panah ke bawah.
Nikita Ivanov: Baiklah, hanya beberapa slaid cepat mengenai GridGain. Apa yang kita lakukan dalam konteks perbualan ini? GridGain pada dasarnya menghasilkan perisian pengkomputeran dalam memori dan sebahagian daripada platform yang kami usahakan adalah pemecut dalam memori Hadoop. Dari segi Hadoop, kita cenderung untuk memikirkan diri kita sebagai pakar prestasi Hadoop. Apa yang kita lakukan, pada dasarnya, di atas platform pengkomputeran teras dalam memori yang terdiri daripada teknologi seperti grid data, aliran memori dan grid pengiraan akan dapat memasangkan dan mempercepatkan pemecut Hadoop. Itu sangat mudah. Akan lebih bagus jika kita boleh mengembangkan sejenis penyelesaian plug-and-play yang boleh dipasang tepat pada pemasangan Hadoop. Jika anda, pemaju MapReduce, memerlukan rangsangan tanpa perlu menulis apa-apa perisian baru atau perubahan kod atau perubahan, atau pada dasarnya mempunyai semua konfigurasi perubahan minimum dalam cluster Hadoop. Inilah yang kita usahakan.
Secara asasnya, pemecut dalam memori Hadoop adalah berdasarkan mengoptimumkan dua komponen dalam ekosistem Hadoop. Jika anda berfikir tentang Hadoop, ia didasarkan pada HDFS, iaitu sistem fail. MapReduce, yang merupakan kerangka untuk menjalankan pertandingan selari di atas sistem fail. Untuk mengoptimumkan Hadoop, kami mengoptimumkan kedua-dua sistem ini. Kami membangunkan sistem fail dalam memori yang serasi sepenuhnya, plug-and-play serasi 100%, dengan HDFS. Anda boleh berjalan bukannya HDFS, anda boleh berjalan di atas HDFS. Dan kami juga telah membangunkan dalam ingatan MapReduce yang memasangkan dan bermain serasi dengan Hadoop MapReduce, tetapi terdapat banyak pengoptimuman tentang bagaimana aliran kerja MapReduce dan bagaimana jadual pada MapReduce berfungsi.
Jika anda melihat, sebagai contoh pada slaid ini, di mana kita menunjukkan jenis tumpukan duplikasi. Di sebelah kiri, anda mempunyai sistem operasi biasa anda dengan GDM dan di atas rajah ini anda mempunyai pusat aplikasi. Di tengah anda mempunyai Hadoop. Dan Hadoop lagi berdasarkan HDFS dan MapReduce. Oleh itu, ini menunjukkan pada gambar rajah ini, bahawa apa yang kita sememangnya dimasukkan ke dalam susunan Hadoop. Sekali lagi, ia adalah plug-and-play; anda tidak perlu menukar sebarang kod. Ia hanya berfungsi dengan cara yang sama. Pada slaid seterusnya, kami menunjukkan pada dasarnya bagaimana kami mengoptimumkan aliran kerja MapReduce. Ini mungkin bahagian yang paling menarik kerana ia memberi anda kelebihan paling banyak apabila anda menjalankan pekerjaan MapReduce.
MapReduce yang tipikal, apabila anda menyerahkan tugas itu, dan di sebelah kiri terdapat gambar rajah, terdapat aplikasi biasa. Oleh itu, biasanya anda menyerahkan kerja dan pekerjaan itu pergi ke pelacak pekerjaan. 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. Terima kasih.
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. Mari lihat. 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? Dengan kuasa besar datang tanggungjawab yang besar. 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.
Dengan itu, kami akan membida anda perpisahan, orang-orang. 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. Selamat tinggal.
