データ分析 “人気”と“おすすめ”は何が違う? 博報堂とヤフーが探る「みんな」の正体 2019.06.04 Insight for D終了のご案内 2020年7月2日を持ちましてInsight for Dを終了させていただくことにいたしました。今後はYahoo! JAPANの最新マーケティング情報にて、みなさまに役立つ情報発信を行ってまいります。
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A/B testing is an indispensable driver behind LinkedIn’s data culture. We run more than 200 experiments in parallel daily and that number is growing quickly. We realized early on that ad hoc testing would only take us so far. We needed to change our approach to allow us to quickly quantify the impact of any A/B test in a scientific and controlled manner across LinkedIn.com and our apps. All of the
Choosing an HDFS data storage format- Avro vs. Parquet and more - StampedeCon 2015 At the StampedeCon 2015 Big Data Conference: Picking your distribution and platform is just the first decision of many you need to make in order to create a successful data ecosystem. In addition to things like replication factor and node configuration, the choice of file format can have a profound impact on cluster
Understanding how Parquet integrates with Avro, Thrift and Protocol Buffers Parquet is a new columnar storage format that come out of a collaboration between Twitter and Cloudera. Parquet’s generating a lot of excitement in the community for good reason - it’s shaping up to be the next big thing for data storage in Hadoop for a number of reasons: It’s a sophisticated columnar file format, which me
March 2, 2016 Volume 14, issue 1 PDF Borg, Omega, and Kubernetes Lessons learned from three container-management systems over a decade Brendan Burns, Brian Grant, David Oppenheimer, Eric Brewer, and John Wilkes, Google Inc. Though widespread interest in software containers is a relatively recent phenomenon, at Google we have been managing Linux containers at scale for more than ten years and built
Disclaimer: The opinions expressed here are my own and do not necessarily represent those of current or past employers. Recent CommentsRaffaele Santopaolo on David Patterson Retires After 40 YearsJames Hamilton on Seagate HAMRTom Davies on Seagate HAMRMatt on Seagate HAMRJames Hamilton on Seagate HAMRMatt on Seagate HAMRJames Hamilton on Cost of Power in Large-Scale Data CentersChris K. on Cost of
By Will Moss At Airbnb we are always trying to learn more about our users and improve their experience on the site. Much of that learning and improvement comes through the deployment of controlled experiments. If you haven’t already read our other post about experimentation I highly recommend you do it, but I will summarize the two main points: (1) running controlled experiments is the best way to
In our previous post, we discussed the motivation for doing A/B testing at Twitter, and how A/B testing helps us innovate. We will now describe how the backend of Twitter’s A/B system is implemented. Overview The Twitter experimentation tool, Duck Duck Goose (DDG for short), was first created in 2010. It has evolved into a system that is capable of aggregating many terabytes of data such as Tweets
Power, minimal detectable effect, and bucket size estimation in A/B tests Bucket size estimation for A/B experiments In previous posts, we discussed how to detect potentially biased experiments, and explored the implications of using multiple control groups. This post describes how Twitter’s A/B testing framework, DDG, addresses one of the most common questions we hear from experimenters, product
The Apache Software Foundation(ASF)のApache Lucyプロジェクトは2月3日、「Apache Lucy 0.3」を公開した。C言語で記述された全文検索エンジンライブラリで、動的言語からの利用も考慮されている。 Apache LucyはJavaで実装された検索エンジンライブラリ「Apache Lucene」をベースにC言語で実装された検索エンジンライブラリ。LuceneとファイルフォーマットやAPIの互換性はなく、そのため「ゆるい(loose)」ポートと言われている。RubyやPerlといった動的言語からの利用もターゲットとされており、Perl向けのバインディングなども含まれている。 Apache Lucy 0.3ではバグ修正のほか、パーサージェネレータの「Lemon」、それにUTF-8エンコードされたUnicode文字列を処理する「utf8proc」
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