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X-WR-CALDESC:Treasure Data Tech Talk Cafe #03
X-WR-CALNAME:Treasure Data Tech Talk Cafe #03
X-WR-TIMEZONE:Asia/Tokyo
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DTSTART:19700101T000000
TZOFFSETFROM:+0900
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BEGIN:VEVENT
UID:458211@techplay.jp
SUMMARY:Treasure Data Tech Talk Cafe #03
DTSTART;TZID=Asia/Tokyo:20150514T190000
DTEND;TZID=Asia/Tokyo:20150514T230000
DTSTAMP:20260419T063108Z
CREATED:20150427T094308Z
DESCRIPTION:イベント詳細はこちら\nhttps://techplay.jp/event/45821
 1?utm_medium=referral&utm_source=ics&utm_campaign=ics\n\n\n\nTreasure Dat
 aTech Talk Cafe #03\n\n概要説明\n\nトレジャーデータ米国本
 社からエンジニアを迎え、フロントエンド技術とその
 デザインの最前線について議論します。\nまた、トレ
 ジャーデータで開発中のオープンソース製品である「E
 mbulk」（http://www.embulk.org/）、\n「Hivemall」（https://github.
 com/myui/hivemall）を活用した技術についても紹介します
 。\n\n本セッションは前半英語、後半日本語で行われま
 す。\n質問等は適宜通訳致しますが、同時通訳は行わ
 れません。\n\n\nタイムスケジュール\n\n	\n	\n		\n			時間\n
 			講演内容\n		\n		\n			18:30 -\n			受付\n		\n		\n			19:00 - 19:05\
 n			\n				Introduction　はじめに\n				　Taro L. Saito\n			\n		\n		\
 n			19:05 - 19:30\n			Frontend Application Architecture\, Patterns\, and 
 Workflows\n			フロントエンドアプリケーションのアーキ
 テクチャ、パターン、ワークフロー\n			　Cesar Andreu\n			
 \n		\n		\n			19:30 - 19:55\n			On choosing a JavaScript framework\n			Jav
 aScriptフレームワークをいかに選ぶか？\n			　Fernando Mart
 inez\n			Web apps start small\, but complexity grows fast when you start 
 adding features. As your product evolves\, you suddenly have to worry abo
 ut session management\, caching\, UI performance\, data flow\, and browse
 r compatibility. Most of these things sound like pretty obvious nuisances
  of building a web app\, right? But when your product is still an idea\; 
 when you are still nurturing the essence of what it is\, all of those com
 plexities sound very far away. And that's the problem. You know these com
 plexities exist\, and that you will eventually need to face them\, but yo
 u never see them coming. By the time you notice\, it's already too late. 
 You have an unintelligible mess of an application.\n				\n				Unintelligi
 ble applications are costly to modify. They are brittle. They resist chan
 ge. If there is something that I have learned so far it is that requireme
 nts do change\, and they change often. If requirements are always changin
 g\, and complicated applications resist change\, then it begs the questio
 n: Can you build a billion dollar product when the product constantly fig
 hts back at every change you make? Probably. Will you be happy developing
  it? I doubt it.\n				\n				Here at Treasure Data we believe that we are 
 building a billion dollar product\, maybe even more\, and that's why it's
  important for us to choose a good framework.\n			\n		\n		\n			19:55 - 20
 :10\n			休憩\n		\n		\n			20:10 - 20:30\n			\n				Useful Design: Backgro
 und\, Process and Execution\n				使い勝手のよいデザインのた
 めの基礎知識、デザインプロセスとその実現\n				　Nilo
 lay Belitchenov\n				A bird’s eye look at the basics of design\, includ
 ing the design cycle and project lifecycle—from discovery\, to ideation
 \, to conceptualization\, execution\, and evaluation.\n			\n		\n		\n			20
 :30 - 20:50\n			\n				The Overview of Server-Side Bulk Loader\n				サー
 バーサイドバルクローダーの概要\n				　Muga Nishizawa\n			
 	This talk presents our new data import mechanism named Server-side Bulk 
 Loader which we recently added to our other traditional data import mecha
 nisms: td-agent\, mobile SDK\, and td import command. Unlike td-agent whi
 ch is installed and runs client-side and works by　continuously importin
 g data streams into Treasure Data\, the Server-side Bulk Loader enables u
 sers to upload data in large bulks reliably. The Server-side Bulk Loader 
 runs in the Treasure Data cloud and ingests data from the customer's own 
 AWS S3 buckets into Treasure Data. At the moment\, the user can configure
  server-side bulk ingestions using the command-line interface (td CLI) an
 d execute them as one-off jobs\; users can also configure bulk loads as s
 cheduled jobs to ingest data on a periodic basis. Data is extracted direc
 tly from the specified AWS S3 bucket\, translates into MPC (MessagePack C
 olumnar format) file format\, and stored in the Treasure Data's proprieta
 ry Plazma storage directly and efficiently. This mechanism relies on the 
 open-source Embulk(*) project\, which is a data loader core supporting in
 put and output plugins. This talk will go over the underlying architectur
 e\, implementation details\, and use cases for Server-side Bulk Loader.\n
 			\n		\n		\n			20:50 - 21:10\n			\n				Hivemall: A Scalable Machine Lear
 ning Framework\n				Hivemall: スケーラブルな機械学習フレー
 ムワーク\n				　Makoto Yui\n				This talk introduces new features of
  Hivemall v0.3 (e.g.\, Recommendation using Matrix Factorization) and cas
 e studies of it's industrial application.\n					Hivemallのv0.3の新機
 能（Matrix Factorizationを用いたレコメンデーションなど
 ）及び、Hivemallの利用事例について紹介します\n			\n		\n
 		\n			21:15 - 22:30\n			懇親会\n		\n	\n	\n\n\n参加費\n無料\n\n#
 ハッシュタグ\n#tdtech\n\nUSTREAM配信\n\n	本イベントはUst配
 信を行います。是非ご覧下さい。\n	\n	 eventdots - USTREAM\n
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 場合は、dots.公式Twitterへご連絡頂きますと、比較的早
 くご対応する事が可能です。\n	お問い合わせの際には
 「@eventdots」のメンションを付けてツイートください。
 \n	\n	 dots.公式Twitter\n	\n	※ dots.運営事務局へお問い合わ
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 場合がございますので、予めご了承ください。\n\n\n\n\n
 \n
LOCATION:Retty Cafe 東京都品川区東五反田３丁目20-14 住友不
 動産高輪パークタワー8階
URL:https://techplay.jp/event/458211?utm_medium=referral&utm_source=ics&utm
 _campaign=ics
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