37 Things One Architect Knows About IT Transformation
Note: This book has evolved into The Software Architect Elevator, which contains 5 new chapters and many edits. It's available from Amazon and the O'Reilly Learning Platform. The Changing Role of IT Many large enterprises are feeling pressure from the rapid digitalization of the world: digital disruptors attack unexpectedly with brand-new business models; the "FaceBook generation" has dramatically
AWS re:Invent 2015 | (BDT305) Amazon EMR Deep Dive and Best Practices
Amazon EMR is one of the largest Hadoop operators in the world. In this session, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters, and other Amazon EMR architectural best practices. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can fine-tune your cluster. We
Using logs to build a solid data infrastructure (or: why dual writes are a bad idea) | Confluent
This is an edited transcript of a talk I gave at the Craft Conference 2015. The video and slides are also available. How does your database store data on disk reliably? It uses a log. How does one database replica synchronise with another replica? It uses a log. How does a distributed algorithm like Raft achieve consensus? It uses a log. How does activity data get recorded in a system like Apache
DSN 2014 Keynote: "Sibyl: A System for Large Scale Machine Learning at Google"
Large scale machine learning is playing an increasingly important role in improving the quality and monetization of Internet properties. A small number of techniques, such as regression, have proven to be widely applicable across Internet properties and applications. Sibyl is a research project that implements these primitives at scale and is widely used within Google. In this talk I will outline
大規模分散システムのレスポンスを向上させる工夫 - ワザノバ | wazanova
https://www.youtube.com/watch?v=1-3Ahy7Fxsc 1 comment | 1 point | by WazanovaNews ■ comment by Jshiike | 約2時間前 GoogleのJeff Dean（Senior Fellow, システム & インフラグループ）による、Velocity Conference 2014のキーノートスピーチです。 Jeffは、オブジェクト指向言語によるプログラムの最適化で博士号を取得。DEC/Compaqの研究所の勤務をへて、1999年にGoogleに入社。以降、BigTable / MapReduce / Spanner / Google Translate / Google Brainなど、同社の大規模分散システムの構築に一貫して携わってきています。 例えば、検索結果のレスポンスを向上させるには、そ
Midwest.io 2014 - Building a Production Machine Learning Infrastructure - Josh Wills
This talk was given at Midwest.io 2014. Cloudera's Data Science Team has a simple mission: build an analytics infrastructure so awesome that it makes Google's Ads Quality Team seethe with jealousy. To that end, I'll give an overview of Cloudera's current data science tools, including Oryx and Spark for building and serving machine learning models, Gertrude for multivariate testing, and Impala for
Inside Sibyl, Google's Massively Parallel Machine Learning Platform
Inside Sibyl, Google’s Massively Parallel Machine Learning Platform If you’ve ever wondered how your spam gets identified in Gmail or where personal video recommendations come from on YouTube, the answer is likely Sibyl, a massively parallel machine learning system that Google developed to make predictions and recommendations with user-specific data culled from its Internet applications. Dr. Tusha
Stack Overflowのアーキテクチャ - ワザノバ | wazanova
http://www.youtube.com/watch?v=OGi8FT2j8hE1 comment | 0 pointsドイツのハンブルグで開催されたDeveloper Conference 2013で、Stack Overflowのアーキテクチャが紹介されてます。 Stack Overflowのネットワークは、110 Q&Aサイト、430万ユーザ、質問760万件、回答1360万件、月間5億6千万ページビュー サーバ25台: ウェブサーバ11台（内9台でほぼトラフィックさばく）、ロードバランサ1台 (+ 予備1台)、DBノード4台、アプリサーバ3台、検索サーバ3台（Elasticsearch）、Redisサーバ２台（キャッシュ、メッセージング） 毎秒質問が投稿されているので、トップページには都度最新の質問を掲載するように更新はできないが、ユーザの回答パターン、質問閲覧パターン、好みのタ
Marco Cecconi The Architecture of Stack Overflow - Developer Conference 2013
Developer Conference, 7. November 2013 von 11.00 - 12.00 Uhr im Kinosaal 1 http://www.developer-conference.eu/
Registration: Machine learning & data conference | O'Reilly Strata
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Velocity NY 2013: Richard Cook, "Resilience In Complex Adaptive Systems"
Building a Data Lake on AWS
Dag Liodden & George Gemelos, Tapad // Data Driven #29 // Sep 2014 (Hosted by FirstMark Capital)
Five Pitfalls for Benchmarking Big Data Systems | USENIX
From the Lab to the Factory: Building a Production Machine Learning Infrastructure
Build software better, together
I253: Web Architecture | Just another blogs.ischool.berkeley.edu site