DynamoDBを使ったKPI保存システム | GREE Engineering
こんにちは！データエンジニアリングチームの長谷川と田畠です。 グリーのデータ分析基盤を良くする仕事をしています。 これはGREE Advent Calendar 2015の6日目の記事です。昨日のRobinさんの記事はVR開発に取り組んだことがあるからこそ書ける記事で、みなさんにとっても新鮮だったのではないでしょうか？ 今日はゲームから少し離れて、KPIダッシュボードのバックエンドをオンプレミスからAWSに移行した話をしたいと思います。最後までお付き合いよろしくお願いします。 はじめに KPI分析を定常的に行うことは、プロダクトの改善のために非常に重要であり、グリーにおいても多くのプロダクトがKPIを閲覧/分析するためのシステムを利用しています。 社内の分析環境では、KPIのデータは時系列データベースに保存されており、内製のグラフ描画ツールにアクセスすることでグラフとして閲覧することができ
Paradigm4 is Transforming the Pace of Daily Research
The right cohorts. The right druggable targets. Fast. “We’ve discovered insights that we wouldn’t have known without this system. Paradigm4’s platform is the foundation of our target discovery efforts. There’s just no way we would have been able to do this without P4. The alternatives are just not equipped to be able to go into the data in the way that we did.”
Introducing and Open Sourcing Ambry - LinkedIn’s New Distributed Object Store
Media content has become ubiquitous around the web and almost all of Linkedin's new features interact with media in some form or the other. Profile photos, email attachments, logos, and influencer posts are a few examples of where photos, videos, PDFs, and other media types get uploaded and displayed to the end user. These media types get stored on our backend and are predominantly served by our C
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“Give me a lever long enough… and I shall move the world” — Archimedes Parallelism is computing’s leverage, a force multiplier acting against the weight of big data. Cloud-hosted, horizontally scalable systems have the power to move even planetary sized data sets with speed. This blog post discusses our efforts to lift one such data set, achieving a scan rate of 26 billions records per second, wi
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Introducing Druid: Real-Time Analytics at a Billion Rows Per Second April 30th, 2011 Eric Tschetter Here at Metamarkets we have developed a web-based analytics console that supports drill-downs and roll-ups of high dimensional data sets – comprising billions of events – in real-time. This is the first of two blog posts introducing Druid, the data store that powers our console. Over the last twelve
App Engine datastore tip: monotonically increasing values are bad - Ikai Says...
When saving entities to App Engine’s datastore at a high write rate, avoid monotonically increasing values such as timestamps. Generally speaking, you don’t have to worry about this sort of thing until your application hits 100s of queries per second. Once you’re in that ballpark, you may want to examine potential hotspots in your application that can increase datastore latency. To explain why thi
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GitHub - threatgrid/asami: A graph store for Clojure and ClojureScript
http://dprg.cs.uiuc.edu/docs/SIGMOD2016-a/ambry.pdf
Distributed Database - Apache Ignite®
Datavore | Stanford Vis Group
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Ticket Servers: Distributed Unique Primary Keys on the Cheap | code.flickr.com