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Eric Kavanagh: Hadirin sekalian, inilah saatnya untuk menjadi bijak! Sudah waktunya untuk TechWise, pertunjukan baru! Nama saya Eric Kavanagh. Saya akan menjadi moderator Anda untuk episode perdana TechWise kami. Benar sekali. Ini adalah kemitraan dari Techopedia dan Grup Bloor, tentu saja, dari ketenaran Analisis Dalam.
Nama saya Eric Kavanagh. Saya akan memoderasi acara yang sangat menarik dan melibatkan ini, teman-teman. Kita akan menggali jauh ke dalam tenunan untuk memahami apa yang terjadi dengan benda besar bernama Hadoop ini. Apa gajah di ruangan itu? Itu disebut Hadoop. Kami akan mencoba mencari tahu apa artinya dan apa yang terjadi dengannya.
Pertama-tama, terima kasih banyak kepada sponsor kami, GridGain, Actian, Zettaset dan DataTorrent. Kami akan mendapatkan beberapa kata singkat dari masing-masing di dekat akhir acara ini. Kami juga akan memiliki T&J, jadi jangan malu - kirimkan pertanyaan Anda kapan saja.
Kami akan menggali rinciannya dan melemparkan pertanyaan-pertanyaan sulit kepada para ahli kami. Dan berbicara tentang para ahli, hei, itu mereka. Jadi, kita akan mendengar dari Dr. Robin Bloor kita sendiri, dan teman-teman, saya sangat senang memiliki legendaris Ray Wang, analis utama dan pendiri Constellation Research. Dia online hari ini untuk memberi kita pemikirannya dan dia seperti Robin bahwa dia sangat beragam dan sangat fokus pada banyak bidang yang berbeda dan memiliki kemampuan untuk mensintesisnya dan untuk benar-benar memahami apa yang sedang terjadi di seluruh bidang teknologi informasi ini. dan manajemen data.
Jadi, ada gajah kecil yang lucu. Dia ada di awal jalan, seperti yang Anda lihat. Ini baru saja mulai sekarang, ini hanya semacam permulaan, semua hal Hadoop ini. Tentu saja, pada tahun 2006 atau 2007, saya kira, adalah ketika dirilis ke komunitas open-source, tetapi ada banyak hal yang terjadi, kawan. Ada perkembangan besar. Sebenarnya, saya ingin mengemukakan cerita, jadi saya akan melakukan berbagi desktop cepat, setidaknya saya pikir saya. Mari kita lakukan berbagi desktop cepat.
Saya tunjukkan ini gila, orang-orang cerita gila. Jadi Intel menginvestasikan $ 740 juta untuk membeli 18 persen dari Cloudera. Saya berpikir dan saya seperti, "Natal Suci!" Saya mulai mengerjakan matematika dan itu seperti, "Ini penilaian $ 4, 1 miliar." Mari kita pikirkan ini sebentar. Maksud saya, jika WhatsApp bernilai $ 2 miliar, saya kira Cloudera mungkin juga bernilai $ 4, 1 miliar, bukan? Maksud saya, mengapa tidak? Beberapa dari angka-angka ini hanya keluar jendela hari ini, teman-teman. Maksud saya, biasanya dalam hal investasi, Anda memiliki EBITDA dan semua berbagai mekanisme lainnya, kelipatan pendapatan, dan sebagainya. Ya, itu akan menjadi salah satu dari kelipatan pendapatan untuk mendapatkan $ 4, 1 miliar untuk Cloudera, yang merupakan perusahaan yang luar biasa. Jangan salah paham - ada beberapa orang yang sangat, sangat pintar di sana termasuk orang yang memulai kegilaan Hadoop, Doug Cutting, dia ada di sana - banyak orang yang sangat cerdas yang melakukan banyak hal, benar-benar hal-hal keren, tetapi intinya adalah bahwa $ 4, 1 miliar, itu banyak uang.
Jadi di sini ada semacam momen yang jelas untuk pergi melalui kepala saya saat ini yang merupakan chip, Intel. Desainer chip mereka membawa untuk melihat beberapa chip yang dioptimalkan Hadoop - Saya harus berpikir begitu, kawan. Itu hanya dugaanku. Itu hanya rumor, datang dari saya, jika Anda mau, tapi itu masuk akal. Dan apa artinya semua ini?
Jadi inilah teoriku. Apa yang terjadi? Banyak hal ini bukan hal baru. Pemrosesan paralel masif bukanlah hal yang baru. Pemrosesan paralel tentu bukan hal baru. Saya sudah berada di dunia supercomputing untuk sementara waktu. Banyak hal yang terjadi bukanlah hal baru, tetapi ada semacam kesadaran umum bahwa ada cara baru untuk menyerang beberapa masalah ini. Apa yang saya lihat terjadi, jika Anda melihat beberapa vendor besar Cloudera atau Hortonworks dan beberapa dari orang-orang ini, apa yang mereka lakukan sebenarnya jika Anda merebusnya hingga tingkat yang paling terperinci adalah pengembangan aplikasi. Itu yang mereka lakukan.
Mereka merancang aplikasi baru - beberapa di antaranya melibatkan analitik bisnis; beberapa dari mereka hanya melibatkan sistem pengisian daya super. Salah satu vendor kami yang telah membicarakan hal itu, mereka melakukan hal semacam itu sepanjang hari, di acara hari ini. Tetapi jika ini sangat baru, sekali lagi jawabannya adalah "tidak benar-benar, " tetapi ada hal-hal besar yang terjadi, dan secara pribadi, saya pikir apa yang terjadi dengan Intel membuat investasi besar ini adalah langkah pembuatan pasar. Mereka melihat dunia hari ini dan melihat bahwa itu adalah dunia yang monopoli sekarang. Ada Facebook dan mereka telah mengalahkan begitu saja MySpace yang buruk. LinkedIn telah mengalahkan ingusan Who's Who yang malang. Jadi Anda melihat-lihat dan ini adalah salah satu layanan yang mendominasi semua ruang berbeda di dunia kita saat ini, dan saya pikir idenya adalah Intel akan melempar semua chip mereka ke Cloudera dan mencoba mengangkatnya ke atas tumpukan - itu hanya teoriku.
Jadi, orang-orang, seperti saya katakan, kita akan memiliki sesi tanya jawab yang panjang, jadi jangan malu-malu. Kirim pertanyaan Anda kapan saja. Anda dapat melakukannya menggunakan komponen T&J dari konsol webcast Anda. Dan dengan itu, saya ingin mendapatkan konten kami karena kami punya banyak hal untuk dilalui.
Jadi, Robin Bloor, izinkan saya menyerahkan kunci kepada Anda dan lantai milik Anda.
Robin Bloor: Oke, Eric, terima kasih untuk itu. Mari kita bawa gajah yang menari. Sebenarnya merupakan hal yang aneh bahwa gajah adalah satu-satunya mamalia darat yang tidak dapat benar-benar melompat. Semua gajah dalam grafik khusus ini memiliki setidaknya satu kaki di tanah, jadi saya kira itu layak, tetapi sampai batas tertentu, ini jelas gajah Hadoop, jadi sangat, sangat mampu.
Pertanyaannya, sungguh, yang menurut saya harus didiskusikan dan harus dibicarakan dengan jujur. Ini harus didiskusikan sebelum Anda pergi ke tempat lain, yaitu untuk benar-benar mulai berbicara tentang apa sebenarnya Hadoop.
Salah satu hal yang mutlak dari man-play adalah key-value store. Kami dulu memiliki toko kunci-nilai. Kami dulu memilikinya di mainframe IBM. Kami memilikinya di komputer mini; DEC VAX memiliki file IMS. Ada kemampuan ISAM yang ada di hampir setiap komputer mini yang bisa Anda dapatkan. Tetapi sekitar akhir 80-an, Unix masuk dan Unix tidak benar-benar menyimpan kunci nilai di dalamnya. Ketika Unix mengembangkannya, mereka berkembang dengan sangat cepat. Apa yang terjadi sebenarnya adalah vendor database, terutama Oracle, mengepul di sana dan mereka menjual basis data Anda untuk menjaga data apa pun yang ingin Anda kelola di Unix. Windows dan Linux ternyata sama. Jadi, industri ini berjalan selama 20 tahun tanpa toko nilai kunci untuk keperluan umum. Yah, sudah kembali sekarang. Tidak hanya itu kembali, ini bisa diskalakan.
Sekarang, saya pikir itu benar-benar fondasi dari apa Hadoop sebenarnya dan sampai tingkat tertentu, itu menentukan ke mana ia akan pergi. Apa yang kita sukai dari toko kunci? Anda yang setua saya dan benar-benar ingat bekerja dengan toko-toko kunci menyadari bahwa Anda bisa menggunakannya untuk membuat database, tetapi hanya secara informal. Anda tahu metadata dengan cepat menilai toko dalam kode program, tetapi Anda sebenarnya bisa menjadikannya file eksternal, dan Anda bisa jika Anda ingin mulai memperlakukan toko nilai kunci sedikit seperti database. Tapi tentu saja itu tidak memiliki semua kemampuan pemulihan yang dimiliki database dan tidak memiliki banyak hal yang dimiliki oleh database sekarang, tetapi itu adalah fitur yang sangat berguna bagi pengembang dan itulah salah satu alasan saya pikir bahwa Hadoop telah terbukti sangat populer - hanya karena telah coders, programmer, pengembang yang cepat. Mereka menyadari bahwa tidak hanya nilai kunci toko tetapi juga nilai kunci toko skala besar. Timbangannya cukup banyak tanpa batas. Saya mengirim timbangan ini ke ribuan server, jadi itu hal yang sangat besar tentang Hadoop, begitulah adanya.
Ini juga ada di atasnya MapReduce, yang merupakan algoritma paralelisasi, tetapi sebenarnya itu, menurut pendapat saya, tidak penting. Jadi, Anda tahu, Hadoop bunglon. Ini bukan hanya sistem file. Saya telah melihat berbagai macam klaim yang dibuat untuk Hadoop: ini adalah database rahasia; itu bukan basis data rahasia; itu adalah toko biasa; ini adalah kotak alat analitik; ini adalah lingkungan ELT; itu alat pembersih data; itu adalah gudang data platform streaming; ini adalah toko arsip; itu obat untuk kanker, dan sebagainya. Sebagian besar dari hal-hal ini benar-benar tidak benar untuk vanilla Hadoop. Hadoop mungkin merupakan prototipe - tentu saja merupakan lingkungan prototipe untuk database SQL, tetapi tidak benar-benar memiliki, jika Anda menempatkan ruang usia dengan katalog usia di atas Hadoop, Anda memiliki sesuatu yang terlihat seperti database, tetapi sebenarnya tidak apa yang orang sebut sebagai basis data dalam hal kemampuan. Banyak dari kemampuan ini, Anda pasti bisa mendapatkannya di Hadoop. Tentu ada banyak dari mereka. Pada kenyataannya, Anda dapat memperoleh beberapa sumber Hadoop, tetapi Hadoop sendiri bukan yang saya sebut pengerasan operasional, dan karena itu kesepakatan tentang Hadoop, sungguh saya tidak akan membahas hal lain, adalah bahwa Anda perlu memiliki ketiga -produk pihak untuk meningkatkannya.
Jadi, berbicara tentang Anda hanya bisa melempar beberapa baris karena saya berbicara Hadoop melampaui batas. Pertama-tama, kemampuan permintaan waktu-nyata, Anda tahu waktu-nyata adalah jenis waktu bisnis, sungguh, hampir selalu kinerja yang kritis. Maksud saya, mengapa Anda merekayasa secara real time? Hadoop tidak benar-benar melakukan ini. Itu melakukan sesuatu yang mendekati real-time tetapi tidak benar-benar melakukan hal-hal real-time. Memang streaming, tapi itu tidak melakukan streaming dengan cara yang saya sebut platform platform streaming aplikasi tipe mission-critical. Ada perbedaan antara database dan toko yang dapat dihapus. Menyinkronkannya ke lebih dari Hadoop memberi Anda penyimpanan data yang jelas. Itu seperti basis data tetapi tidak sama dengan basis data. Hadoop dalam bentuk aslinya, menurut saya, tidak benar-benar memenuhi syarat sebagai basis data sama sekali karena kekurangan beberapa hal yang seharusnya dimiliki oleh basis data. Hadoop melakukan banyak hal, tetapi tidak terlalu baik. Sekali lagi, kapabilitas ada di sana tetapi kami masih jauh dari benar-benar memiliki kapabilitas cepat di semua bidang ini.
Hal lain yang perlu dipahami tentang Hadoop adalah, itu agak panjang sejak dikembangkan. Ini dikembangkan pada hari-hari awal; itu dikembangkan ketika kami memiliki server yang sebenarnya hanya memiliki satu prosesor per server. Kami tidak pernah memiliki prosesor multi-core dan itu dibangun untuk melindas grid, meluncurkan grid dan severs. Salah satu tujuan desain Hadoop adalah untuk tidak pernah kehilangan pekerjaan. Dan itu benar-benar tentang kegagalan disk, karena jika Anda memiliki ratusan server, maka kemungkinannya adalah, jika Anda memiliki disk di server, kemungkinannya adalah Anda akan mendapatkan ketersediaan waktu uptime sekitar 99, 8. Itu berarti bahwa Anda akan mendapatkan rata-rata kegagalan dari salah satu server itu setiap 300 atau 350 hari, satu hari dalam setahun. Jadi, jika Anda memiliki ratusan ini, kemungkinan akan ada pada setiap hari sepanjang tahun bahwa Anda akan mendapatkan kegagalan server.
Hadoop dibangun khusus untuk mengatasi masalah itu - sehingga, jika ada yang gagal, ia mengambil snapshot dari semua yang terjadi, pada setiap server tertentu dan dapat memulihkan pekerjaan batch yang sedang berjalan. Dan itu semua yang benar-benar pernah berjalan di Hadoop adalah pekerjaan batch dan itu kemampuan yang sangat berguna, harus dikatakan. Beberapa pekerjaan batch yang sedang dijalankan - terutama di Yahoo, di mana saya pikir Hadoop agak lahir - akan berjalan selama dua atau tiga hari, dan jika gagal setelah sehari, Anda benar-benar tidak ingin kehilangan pekerjaan itu sudah dilakukan. Jadi itu adalah titik desain di balik ketersediaan di Hadoop. Anda tidak akan menyebutnya ketersediaan tinggi, tetapi Anda bisa menyebutnya ketersediaan tinggi untuk pekerjaan berantai seri. Mungkin itu cara untuk melihatnya. Ketersediaan tinggi selalu dikonfigurasi sesuai dengan karakteristik jalur kerja. Saat ini, Hadoop hanya dapat dikonfigurasikan untuk pekerjaan berantai seri sehubungan dengan pemulihan semacam itu. Ketersediaan perusahaan yang tinggi mungkin merupakan pemikiran terbaik dalam hal transaksional LLP. Saya percaya bahwa jika Anda tidak melihatnya sebagai sesuatu yang real-time, Hadoop belum melakukannya. Mungkin jauh dari melakukan itu.
Tapi inilah hal yang indah tentang Hadoop. Grafik di sisi kanan yang memiliki daftar vendor di sekitar dan semua garis di atasnya menunjukkan koneksi antara vendor dan produk lain di ekosistem Hadoop. Jika Anda melihatnya, itu adalah ekosistem yang sangat mengesankan. Ini sangat luar biasa. Kami jelas, kami berbicara dengan banyak vendor dalam hal kemampuan mereka. Di antara vendor yang saya ajak bicara, ada beberapa kemampuan yang sangat luar biasa menggunakan Hadoop dan dalam memori, cara menggunakan Hadoop sebagai arsip terkompresi, menggunakan Hadoop sebagai lingkungan ETL, dan seterusnya dan seterusnya. Tapi sungguh, jika Anda menambahkan produk ke Hadoop sendiri, itu bekerja sangat baik di ruang tertentu. Jadi, sementara saya mengkritik Hadoop asli, saya tidak mengkritik Hadoop ketika Anda benar-benar menambahkan kekuatan untuk itu. Menurut pendapat saya, popularitas Hadoop menjamin masa depannya. Maksud saya, bahkan jika setiap baris kode yang ditulis sejauh ini di Hadoop menghilang, saya tidak percaya bahwa API HDFS akan hilang. Dengan kata lain, saya pikir sistem file, API, ada di sini untuk tetap, dan mungkin BENANG, penjadwal yang melihat di atasnya.
Ketika Anda benar-benar melihat itu, itu adalah kemampuan yang sangat penting dan saya akan mengatasinya sebentar lagi, tetapi hal lain yaitu, katakanlah, orang-orang yang menarik tentang Hadoop adalah keseluruhan gambar sumber terbuka. Jadi ada baiknya membaca apa gambar open-source dalam hal apa yang saya anggap kemampuan nyata. Sementara Hadoop dan semua komponennya tentu saja dapat melakukan apa yang kita sebut panjang data - atau seperti saya lebih suka menyebutnya, reservoir data - ini tentu saja merupakan area pementasan yang sangat baik untuk memasukkan data ke dalam organisasi atau untuk mengumpulkan data dalam organisasi - sangat baik untuk kotak pasir dan untuk memancing data. Ini sangat bagus sebagai platform pengembangan prototyping yang mungkin Anda terapkan pada akhir hari, tetapi Anda tahu sebagai lingkungan pengembangan hampir semua yang Anda inginkan ada di sana. Sebagai toko arsip, cukup banyak yang Anda butuhkan, dan tentu saja itu tidak mahal. Saya tidak berpikir kita harus menceraikan salah satu dari dua hal ini dari Hadoop meskipun mereka tidak secara resmi, jika Anda suka, komponen Hadoop. Baji online telah membawa sejumlah besar analitik ke dunia open-source dan banyak analitik sekarang sedang dijalankan di Hadoop karena itu memberi Anda lingkungan yang nyaman di mana Anda benar-benar dapat mengambil banyak data eksternal dan mulai bermain di kotak pasir analitis.
Dan kemudian Anda memiliki kemampuan sumber terbuka, yang keduanya adalah pembelajaran mesin. Keduanya sangat kuat dalam arti bahwa mereka menerapkan algoritma analitik yang kuat. Jika Anda menyatukan hal-hal ini, Anda memiliki kernel dari beberapa kemampuan yang sangat, sangat penting, yang dalam satu atau lain cara sangat mungkin - apakah itu berkembang sendiri atau apakah vendor datang untuk mengisi bagian yang hilang - sangat mungkin untuk berlanjut untuk waktu yang lama dan tentunya saya pikir pembelajaran mesin sudah memiliki dampak yang sangat besar pada dunia.
Evolusi Hadoop, BENANG mengubah segalanya. Apa yang terjadi adalah MapReduce cukup banyak dilas ke sistem file awal HDFS. Ketika YARN diperkenalkan, itu menciptakan kemampuan penjadwalan dalam rilis pertama. Anda tidak mengharapkan penjadwalan yang sangat canggih dari rilis pertama, tetapi itu berarti bahwa itu sekarang tidak lagi perlu lingkungan tambalan. Itu adalah lingkungan di mana beberapa pekerjaan dapat dijadwalkan. Begitu itu terjadi, ada serangkaian vendor yang menjauhkan diri dari Hadoop - mereka baru saja masuk dan terhubung ke sana karena dengan begitu mereka bisa melihatnya sebagai lingkungan penjadwalan atas sistem file dan mereka dapat menangani barang-barang untuk Itu. Bahkan ada vendor database yang telah mengimplementasikan database mereka pada HDFS, karena mereka hanya mengambil mesin dan hanya meletakkannya di HDFS. Dengan cascading dan dengan BENANG, itu menjadi lingkungan yang sangat menarik karena Anda dapat membuat alur kerja yang kompleks melalui HDFS dan ini benar-benar berarti bahwa Anda dapat mulai memikirkannya sebagai benar-benar sebuah platform yang dapat menjalankan beberapa pekerjaan secara bersamaan dan mendorong dirinya sendiri ke titik melakukan hal-hal yang sangat penting untuk misi. Jika Anda akan melakukan itu, Anda mungkin perlu membeli beberapa komponen pihak ketiga seperti keamanan dan sebagainya, yang Hadoop sebenarnya tidak memiliki akun audit untuk mengisi kekosongan, tetapi Anda masuk ke titik di mana bahkan dengan open source asli Anda dapat melakukan beberapa hal menarik.
Dalam hal di mana saya pikir Hadoop sebenarnya akan pergi, saya pribadi percaya bahwa HDFS akan menjadi sistem file skala-out default dan karena itu akan menjadi OS, sistem operasi, untuk grid untuk aliran data. Saya pikir itu punya masa depan yang besar dalam hal itu dan saya tidak berpikir itu akan berhenti di sana. Dan saya pikir dalam kenyataannya ekosistem hanya membantu karena hampir semua orang, semua vendor di luar angkasa, sebenarnya mengintegrasikan Hadoop dalam satu atau lain cara dan mereka hanya memungkinkannya. Dalam hal poin lain yang layak dibuat, dalam hal overage Hadoop, apakah itu bukan platform yang sangat baik ditambah dengan paralelisasi. Jika Anda benar-benar melihat apa yang dilakukannya, apa yang sebenarnya dilakukannya adalah mengambil snapshot secara teratur di setiap server saat menjalankan pekerjaan MapReduce. Jika Anda akan merancang untuk paralelisasi yang sangat cepat, Anda tidak akan melakukan hal seperti itu. Pada kenyataannya, Anda mungkin tidak akan menggunakan MapReduce sendiri. MapReduce hanyalah apa yang saya katakan setengah mampu paralelisme.
Ada dua pendekatan untuk paralelisme: satu adalah dengan proses pipelining dan yang lainnya adalah dengan membagi data MapReduce dan itu melakukan pembagian data sehingga ada banyak pekerjaan di mana MapReduce sebenarnya tidak akan menjadi cara tercepat untuk melakukannya, tetapi itu akan memberi Anda paralelisme dan tidak ada jalan keluar dari itu. Ketika Anda memiliki banyak data, kekuatan seperti itu biasanya tidak berguna. BENANG, seperti yang sudah saya katakan, adalah kemampuan penjadwalan yang sangat muda.
Hadoop adalah, semacam menggambar garis di pasir di sini, Hadoop bukan gudang data. Sejauh ini bukan gudang data sehingga hampir tidak masuk akal untuk mengatakannya. Dalam diagram ini, apa yang saya tunjukkan di bagian atas adalah semacam aliran data, pergi dari reservoir data Hadoop ke database skala besar yang merupakan apa yang sebenarnya akan kita lakukan, gudang data perusahaan. Saya menunjukkan basis data lama, memasukkan data ke dalam gudang data dan aktivitas pembongkaran menciptakan basis data dari gudang data, tetapi itu sebenarnya adalah gambaran yang mulai saya lihat muncul, dan saya akan mengatakan ini seperti generasi pertama dari apa yang terjadi pada data warehouse dengan Hadoop. Tetapi jika Anda melihat sendiri data warehouse, Anda menyadari bahwa di bawah data warehouse, Anda memiliki pengoptimal. Anda telah mendapatkan pekerja kueri terdistribusi atas banyak proses yang duduk di barangkali sangat banyak disk. Itulah yang terjadi di gudang data. Sebenarnya itu semacam arsitektur yang dibangun untuk gudang data dan butuh waktu lama untuk membangun sesuatu seperti itu, dan Hadoop tidak memiliki semua itu sama sekali. Jadi Hadoop bukan gudang data dan tidak akan menjadi satu, menurut saya, dalam waktu dekat.
Memang memiliki reservoir data relatif ini, dan itu terlihat menarik jika Anda hanya melihat dunia sebagai serangkaian peristiwa yang mengalir ke organisasi. Itulah yang saya tunjukkan di sisi kiri diagram ini. Setelah itu melalui kemampuan penyaringan dan perutean dan hal-hal yang perlu dilakukan untuk streaming akan tersedot dari aplikasi streaming dan segala sesuatu lainnya langsung masuk ke reservoir data di mana itu disiapkan dan dibersihkan, dan kemudian diteruskan oleh ETL ke salah satu data tunggal gudang atau gudang data logis yang terdiri dari beberapa mesin. Ini, menurut pendapat saya, garis pengembangan alami untuk Hadoop.
Dalam hal ETW, salah satu hal yang layak ditunjukkan adalah bahwa data warehouse itu sendiri benar-benar dipindahkan - bukan seperti itu. Tentu saja, saat ini, Anda berharap akan ada kemampuan hirarkis per data hirarkis dari apa yang orang, atau beberapa orang, sebut dokumen di gudang data. Itu JSON. Mungkin, permintaan jaringan itu adalah basis data grafik, mungkin analitik. Jadi, yang kami tuju adalah ETW yang sebenarnya memiliki beban kerja yang lebih kompleks daripada yang biasa kami lakukan. Jadi itu agak menarik karena dengan cara itu berarti bahwa gudang data semakin canggih, dan karena itu, ini akan menjadi waktu yang lebih lama sebelum Hadoop mendekati tempat itu. Arti dari data warehouse meluas, tetapi masih termasuk optimasi. Anda harus memiliki kemampuan pengoptimalan, bukan hanya atas permintaan sekarang, tetapi atas semua aktivitas ini.
Itu dia, sungguh. Itu semua yang ingin saya katakan tentang Hadoop. Saya pikir saya bisa memberikan kepada Ray, yang tidak punya slide, tapi dia selalu pandai berbicara.
Eric Kavanagh: Saya akan mengambil slide. Ada teman kita, Ray Wang. Jadi, Ray, apa pendapatmu tentang semua ini?
Ray Wang: Sekarang, saya pikir itu mungkin salah satu yang paling ringkas dan sejarah hebat dari toko-toko kunci dan di mana Hadoop telah menjalin hubungan dengan perusahaan yang keluar, jadi saya selalu belajar banyak ketika mendengarkan Robin.
Sebenarnya, saya punya satu slide. Saya dapat memunculkan satu slide di sini.
Eric Kavanagh: Silakan dan klik pada, klik mulai dan pergi untuk berbagi desktop Anda.
Ray Wang: Mengerti, ini dia. Saya akan benar-benar berbagi. Anda dapat melihat aplikasi itu sendiri. Mari kita lihat bagaimana kelanjutannya.
Semua ini berbicara tentang Hadoop dan kemudian kita masuk ke dalam percakapan tentang teknologi yang ada dan ke mana Hadoop menuju, dan banyak kali saya hanya ingin membawanya kembali untuk benar-benar melakukan diskusi bisnis. Banyak hal yang terjadi di sisi teknologi benar-benar bagian ini di mana kita telah berbicara tentang gudang data, manajemen informasi, kualitas data, penguasaan data itu, dan kita cenderung melihat ini. Jadi jika Anda melihat grafik ini di sini di bagian paling bawah, itu sangat menarik bahwa tipe individu yang kita bahas tentang pembicaraan tentang Hadoop. Kami memiliki teknologi dan ilmuwan data yang geeking, memiliki banyak kegembiraan, dan biasanya tentang sumber data, bukan? Bagaimana kita menguasai sumber data? Bagaimana kita memasukkan ini ke tingkat kualitas yang tepat? Apa yang kita lakukan tentang tata kelola? Apa yang bisa kita lakukan untuk mencocokkan berbagai jenis sumber? Bagaimana cara kita menjaga garis keturunan? Dan semua diskusi semacam itu. Dan bagaimana kita mendapatkan lebih banyak SQL dari Hadoop kita? Jadi bagian itu terjadi pada level ini.
Kemudian di sisi informasi dan orkestrasi, di sinilah ia menjadi menarik. Kami mulai mengikat keluaran wawasan ini yang kami dapatkan atau menariknya dari proses bisnis? Bagaimana kita mengikatnya kembali ke model metadata apa pun? Apakah kita menghubungkan titik-titik di antara objek? Jadi, kata kerja dan diskusi baru tentang bagaimana kita menggunakan data itu, bergerak dari apa yang secara tradisional kita berada di dunia CRUD: membuat, membaca, memperbarui, menghapus, ke dunia yang membahas tentang bagaimana kita terlibat atau berbagi atau berkolaborasi atau suka atau tarik sesuatu.
Di situlah kita mulai melihat banyak kegembiraan dan inovasi, terutama tentang cara menarik informasi ini dan membawanya ke nilai. Itulah diskusi berbasis teknologi di bawah garis merah. Di atas garis merah itu, kami mendapatkan pertanyaan yang selalu ingin kami tanyakan dan salah satu dari mereka yang selalu kami kemukakan adalah seperti, misalnya, mungkin pertanyaan di ritel untuk Anda adalah seperti, "Mengapa sweter merah menjual lebih baik di Alabama daripada sweater biru di Michigan? " Anda bisa memikirkannya dan berkata, "Itu agak menarik." Anda melihat pola itu. Kami mengajukan pertanyaan itu, dan kami bertanya-tanya, "Hei, apa yang kita lakukan?" Mungkin ini tentang sekolah negeri - Michigan versus Alabama. OK, saya mengerti, saya melihat kemana kita pergi. Jadi kita mulai mendapatkan sisi bisnis dari rumah, orang-orang di bidang keuangan, orang-orang yang telah mendapatkan kemampuan BI tradisional, orang-orang di bidang pemasaran, dan orang-orang di HR berkata, "Di mana pola saya?" Bagaimana kita sampai ke pola itu? Jadi kami melihat cara inovasi lain di sisi Hadoop. Ini benar-benar tentang bagaimana kita memunculkan pembaruan wawasan lebih cepat. Bagaimana kita membuat koneksi semacam ini? Ini berlaku untuk orang-orang yang melakukan seperti, iklan: teknologi yang pada dasarnya mencoba untuk menghubungkan iklan dan konten yang relevan dari apa pun dari jaringan penawaran real-time ke iklan kontekstual dan penempatan iklan dan melakukannya dengan cepat.
Jadi itu menarik. Anda melihat perkembangan Hadoop dari, "Hei, inilah solusi teknologinya. Inilah yang perlu kita lakukan untuk menyampaikan informasi ini kepada orang-orang." Kemudian ketika ia melintasi garis porsi bisnis, di sinilah ia semakin menarik. Ini wawasan. Di mana kinerjanya? Di mana deduksi itu? Bagaimana kita memprediksi sesuatu? Bagaimana kita mengambil pengaruh? Dan kemudian membawanya ke tingkat terakhir di mana kita benar-benar melihat serangkaian inovasi Hadoop yang terjadi di sekitar sistem dan tindakan keputusan. Apa tindakan terbaik selanjutnya? Jadi, Anda tahu sweater biru lebih laris di Michigan. Anda duduk di satu ton sweater biru di Alabama. Yang jelas adalah, "Ya, mari kita kirim ini ke sana." Bagaimana kita melakukannya? Apa langkah selanjutnya? Bagaimana kita mengikat itu kembali? Mungkin tindakan terbaik berikutnya, mungkin itu saran, mungkin itu sesuatu yang membantu Anda mencegah masalah, mungkin juga tidak ada tindakan, yang merupakan tindakan itu sendiri. Jadi kita mulai melihat pola seperti ini muncul. Dan keindahan dari kembali ke apa yang Anda katakan tentang toko-toko kunci, Robin, adalah bahwa hal itu terjadi begitu cepat. Itu terjadi dengan cara yang belum kita pikirkan seperti ini.
Mungkin saya akan mengatakan dalam lima tahun terakhir kami menjemput. Kami mulai berpikir dalam hal bagaimana kami dapat memanfaatkan toko-toko bernilai kunci lagi, tetapi hanya dalam lima tahun terakhir, orang-orang melihat ini dengan sangat berbeda dan itu seperti siklus teknologi berulang dalam pola 40 tahun, jadi ini adalah jenis satu hal yang lucu di mana kami melihat cloud dan saya hanya suka berbagi waktu mainframe. Kami melihat Hadoop dan menyukai penyimpanan nilai kunci - mungkin itu adalah data mart, kurang dari data warehouse - dan kami mulai melihat pola-pola ini lagi. Apa yang saya coba lakukan saat ini adalah memikirkan apa yang dilakukan orang 40 tahun lalu? Apa pendekatan dan teknik serta metodologi yang diterapkan yang dibatasi oleh teknologi yang dimiliki orang? Itulah yang mendorong proses pemikiran ini. Jadi ketika kita melihat gambaran yang lebih besar tentang Hadoop sebagai alat, ketika kita kembali dan memikirkan implikasi bisnis, ini adalah jenis jalan yang biasanya kita lalui sehingga kita dapat melihat potongan apa, bagian apa yang ada dalam data jalur keputusan. Itu hanya sesuatu yang ingin saya bagikan. Ini semacam pemikiran yang telah kami gunakan secara internal dan mudah-mudahan menambah diskusi. Jadi aku akan mengembalikannya padamu, Eric.
Eric Kavanagh: Itu luar biasa. Jika Anda bisa bertahan untuk beberapa T&J. Tapi saya suka Anda membawanya kembali ke tingkat bisnis karena pada akhirnya, itu semua tentang bisnis. Ini semua tentang menyelesaikan sesuatu dan memastikan bahwa Anda membelanjakan uang dengan bijak dan itu adalah salah satu pertanyaan yang sudah saya lihat, jadi para pembicara mungkin ingin berpikir tentang apa yang TCL lakukan dengan rute Hadoop. Ada beberapa titik manis di antaranya, misalnya, menggunakan alat rak kantor untuk melakukan sesuatu dengan cara tradisional dan menggunakan set alat baru, karena sekali lagi, pikirkan tentang hal itu, banyak hal ini bukan hal baru, itu hanya semacam bersatu dengan cara baru, saya kira, cara terbaik untuk mengatakannya.
Jadi mari kita lanjutkan dan kenalkan teman kita, Nikita Ivanov. Dia adalah pendiri dan CEO GridGain. Nikita, aku akan pergi ke depan dan menyerahkan kuncinya padamu, dan aku yakin kamu di luar sana. Bisakah kamu mendengarku Nikita?
Nikita Ivanov: Ya, saya di sini.
Eric Kavanagh: Luar Biasa. Jadi lantai adalah milikmu. Klik pada slide itu. Gunakan panah bawah, dan bawa pergi. Lima menit.
Nikita Ivanov: Slide mana yang harus saya klik?
Eric Kavanagh: Cukup klik di mana saja pada slide itu dan kemudian Anda menggunakan panah bawah pada keyboard untuk bergerak. Cukup klik pada slide itu sendiri dan gunakan panah bawah.
Nikita Ivanov: Baiklah, jadi hanya beberapa slide cepat tentang GridGain. Apa yang kita lakukan dalam konteks percakapan ini? GridGain pada dasarnya menghasilkan perangkat lunak komputasi dalam memori dan bagian dari platform yang kami kembangkan adalah akselerator Hadoop dalam memori. Dalam hal Hadoop, kita cenderung berpikir tentang diri kita sebagai spesialis kinerja Hadoop. Apa yang kami lakukan, pada dasarnya, di atas platform komputasi inti dalam memori kami yang terdiri dari teknologi seperti kisi data, streaming memori, dan kisi komputasi akan dapat menghubungkan dan memainkan akselerator Hadoop. Itu sangat sederhana. Alangkah baiknya jika kita dapat mengembangkan beberapa jenis solusi plug-and-play yang dapat diinstal langsung di instalasi Hadoop. Jika Anda, pengembang MapReduce, memang membutuhkan dorongan tanpa perlu menulis perangkat lunak baru atau perubahan kode atau perubahan, atau pada dasarnya memiliki semua perubahan konfigurasi minimal di cluster Hadoop. Itulah yang kami kembangkan.
Pada dasarnya, akselerator Hadoop dalam memori didasarkan pada pengoptimalan dua komponen dalam ekosistem Hadoop. Jika Anda berpikir tentang Hadoop, sebagian besar didasarkan pada HDFS, yang merupakan sistem file. MapReduce, yang merupakan kerangka kerja untuk menjalankan kompetisi secara paralel di atas sistem file. Untuk mengoptimalkan Hadoop, kami mengoptimalkan kedua sistem ini. Kami mengembangkan sistem file dalam memori yang sepenuhnya kompatibel, plug-and-play 100% kompatibel, dengan HDFS. Anda dapat menjalankan alih-alih HDFS, Anda dapat menjalankan di atas HDFS. Dan kami juga mengembangkan MapReduce dalam memori yang plug-and-play kompatibel dengan Hadoop MapReduce, tetapi ada banyak optimisasi tentang bagaimana alur kerja MapReduce dan bagaimana jadwal pada MapReduce bekerja.
Jika Anda melihat, misalnya pada slide ini, di mana kami menunjukkan jenis tumpukan duplikasi. Di sebelah kiri, Anda memiliki sistem operasi khas Anda dengan GDM dan di atas diagram ini Anda memiliki pusat aplikasi. Di tengah Anda memiliki Hadoop. Dan Hadoop sekali lagi didasarkan pada HDFS dan MapReduce. Jadi ini mewakili pada diagram ini, bahwa apa yang kami masukkan ke dalam tumpukan Hadoop. Sekali lagi, ini plug-and-play; Anda tidak perlu mengubah kode apa pun. Cara kerjanya sama saja. Pada slide berikutnya, kami menunjukkan bagaimana kami mengoptimalkan alur kerja MapReduce. Itu mungkin bagian yang paling menarik karena memberi Anda keuntungan terbesar saat Anda menjalankan pekerjaan MapReduce.
MapReduce yang khas, ketika Anda mengirimkan pekerjaan, dan di sebelah kiri ada diagram, ada aplikasi biasa. Jadi biasanya Anda mengirimkan pekerjaan dan pekerjaan 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.
Terima kasih.
Eric Kavanagh: Yeah, thank you so much. I'll throw a question over to you, really a comment, and let you kind of expound upon it. I really think you're on the ball here with this concept of turning over these operators and letting people use these operators almost like Legos to build big data applications. Can you kind of talk about what goes into the process of taking these operators and stitching them together, how do you actually do that?
Phu Hoang: That's a great question. So first of all, these operators are in your standard application Java Logic. We supply 400 of them. They do all kinds of processing and so to build your application, you really are just connecting operators together into a data flow graph. In our customers, we find that they use a number of operators that we have in our library as well as they take their own job of custom logic and make it an operator so that they can substantiate that into a graph.
Eric Kavanagh: OK, good. I think it's a good segue to bring in John Santaferraro from Actian because you guys have a slightly similar approach, it seems to me, in opening up a sort of management layer to be able to play around with different operators. Can you talk about what you do with respect to what tools we're just talking about, John?
John Santaferraro: Yeah, exactly. We have a library of analytics operators as well as transformational operators, operators for blending and enriching data and it is very similar. You use a drag-and-drop interface to be able to stitch together these data flows or work flows, and even analytic workflows. So it's everything from being able to connect to data, to be able to blend and enrich data, to be able to run data science or machine learning algorithms and then even being able to push that into a high-performance low-latency analytic engine. What we find is that it's all built on the open-source nine project. So we capture a lot of the operators that they are developing and then we take all of that, and via YARN, very similar to what Phu described at DataTorrent, we push that down so that it is parallelized against all of the nodes in a Hadoop cluster. A lot of it is about making the data in Hadoop much more accessible to business users and less-skilled workers, somebody besides a data scientist.
Eric Kavanagh: OK, let me go bring in Nikita once again. I'm going to throw your five up as well. Can you kind of talk about how you approach this solution vis-à-vis what these two gentlemen just talked about? How does someone actually put this stuff together and make use from GridGain?
Nikita Ivanov: Well, I think the biggest difference between us and from practically the rest of them is we don't require you to do any recording - you don't have to do anything, it's a plug-and-play. If you have an application today, it's going to work faster. You don't have to change code; you don't have to do anything; you just have to install GridGain along the side of Hadoop cluster and that's it. So that's the biggest difference and we talked to our customers. There are different myriad of solutions today that ask you to change something: programming, doing your API, using your interfaces and whatnot. Ours is very simple. You don't need to invest a lot of time into the Hadoop ecosystem, and whatever you used to do, the MapReduce or any of the tools continue to use. With GridGain, you don't have to change any single line of code, it's just going to work faster. That's the biggest difference and that's the biggest message for us.
Eric Kavanagh: Let's get Jim back in here too. Jim, your quote is killing me. I had to write it down in between that. I'll put it into some kind of deck, but the Hadoop ecosystem right now is like a pool party and the parents just came home. That is funny stuff man; that is brilliant. Can you kind of talk about how you guys come onto the scene? How do you actually implement this? How long does that take? How does all that work?
Jim Kaskade: Yes. So there are a couple of varieties depending on the target customer, but typically these days, you see evaluations where security is factored in, in some of these hardening requirements that I talked about. What has happened in some other cases, and especially last year where people had big plans to deploy, is that there was kind of a science project, if you will, or somebody was playing with the technology and had a cluster up and working and was working with it but then the security guy shows up, and if it is going to go on a live data center, it has to basically comply with the same requirements that we have for other equipment running in the data center, if it is going to be an infrastructure that we build out. Last year, we had even some banks that told us they were going to deploy 400 to 1, 000 nodes last year and they're still sitting on a 20-node cluster mainly because now a security person has been plugged in. They've got to be worried about financial compliance, about sets of information that is sitting on a cluster, and so forth. It varies by customer, but typically this is kind of what elongates the cycles and this is typical of a new technology where if you really want to deploy this in production environment, it really has to have some of these other pieces including the very valuable open-source pieces, right?
Eric Kavanagh: OK, good. Let's see. I'm going to bring Phu back into the equation here. We've got a good question for you. One of the attendees is asking how is DataTorrent different from Storm or Kafka or the Redis infrastructure. Phu, are you out there? Hey, Phu, can you hear me? Maybe I'm mute.
Let's bring Ray Wang back into this. Ray, you've seen a lot of these technologies and looked at how they worked. I really love this concept of turning over control or giving control to end users of the operators. I like to think of them as like really powerful Legos that they can use to kind of build some of these applications. Can you comment on that? What do you think about all that?
Ray Wang: Coming from my technical background, I'd say I'm scared - I was scared shitless! But honestly, I think it's important, I mean, in order to get scale. There's no way you can only put so many requests. Think about the old way we did data warehousing. In the business I had to file the request for a report so that they could match all the schemes. I mean, it's ridiculous. So we do have to get to a way for the business side of the house and definitely become data jocks. We actually think that in this world, we're going to see more digital artists and people that have the right skills, but also understand how to take that data and translate that into business value. And so these digital artisans, data artisans depending on how you look at this, are going to need both really by first having the curiosity and the right set of questions, but also the knowledge to know when the data set stinks. If I'm getting a false positive or a false negative, why is that happening?
I think a basic level of stats, a basic level of analytics, understanding that there's going to be some training required. But I don't think it's going to be too hard. I think if you get the right folks that should be able to happen. You can't democratize the whole decision-making process. I see that happening. We see that in a lot of companies. Some are financial services clients are doing that. Some of our retail folks are doing that, especially in the razor-thin margins that you are seeing in retail. I was definitely seeing that in high tech just around here in the valley. That's just kind of how people are. It's emerging that way but it's going to take some time because these basic data skills are still lacking. And I think we need to combine that with some of the stuff that some of these guys are doing here on this webinar.
Eric Kavanagh: Well, you bring up a really good point. Like how many controls you want to give to the average end user. You don't want to give an airplane cockpit to someone who's driving a car for the first time. You want to be able to closely control what they have control over. I guess my excitement kind of stems around being able to do things yourself, but the key is you got to put the right person in that cockpit. You got to have someone who really knows what they're doing. No matter what you hear from the vendor community folks, when somebody's more powerful tools are extremely complex, I mean if you are talking about putting together a string of 13, 14, 15 operators to do a particular type of transformation on your data, there are not many people who could do that well. I think we're going to have many, many more people who do that well because the tools are out there now and you can play with the stuff, and there is going to be a drive to be able to perfect that process or at least get good at it.
We did actually lose Phu, but he's back on the line now. So, Phu, the question for you is how is DataTorrent different from, like, Storm or Kafka or Redis or some of these others?
Phu Hoang: I think that's a great question. So, Redis of course is really an in-memory data store and we connect to Redis. We see ourselves as really a processing engine of data, of streaming data. Kafka again is a great bus messaging bus we use. It's actually one of our favorite messaging bus, but someone has to do the big data processing across hundreds of nodes that is fault tolerant, that is scalable, and I repeat that as the job that we play. So, yes, we are similar to Storm, but I think that Storm is really developed a long time ago even before Hadoop, and it doesn't have the enterprise-level thinking about scalability to the hundreds and millions, now even billions of events, nor does it really have the HA capability that I think enterprise requires.
Eric Kavanagh: Great. And you know, speaking of HA, I'll use that as an excuse to bring Robin Bloor back into the conversation. We just talked about this yesterday. What do you mean by high availability? What do you mean by fault tolerance? What do you mean by real time, for example? These are terms that can be bent. We see this all time in the world of enterprise technology. It's a good term that other people kind of glom onto and use and co-opt and move around and then suddenly things don't mean quite what they used to. You know, Robin, one of my pet peeves is this whole universe of VOIP. It's like "Why would we go down in quality? Isn't it important to understand what people say to you and why that matters?" But I'll just ask you to kind of comment on what you think. I'm still laughing about Ray's comment that he's scared shitless about giving these people. What do you think about that?
Ray Wang: Oh, I think it's a Spider-man problem, isn't it? Dengan kekuatan besar datang tanggung jawab 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 mengucapkan selamat tinggal, kawan. 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. Sampai jumpa.
