The Evolution of Massive-Scale Data Processing Tyler Akidau Staff Software Engineer @takidau With slides by Frances Perry (@francesjperry) and Kenn Knowles (@kennknowles). Strata + Hadoop World London 2016 1 https://goo.gl/VlVAEp A high-level tour of MapReduce, Apache Hadoop, Google Flume, Apache...
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The what, where, when, and how of unbounded data processing. Introduction Welcome back! If you missed my previous post, The world beyond batch: Streaming 101, I strongly recommend you take the time to read that one first. It lays the necessary foundation for the topics I’ll be covering in this post, and I’ll be assuming you’re already familiar with the terminology and notions introduced there. Cav
There are many technologies for streaming data: simple event processors, stream processors, and complex event processors. Even within the open-source community there is a bewildering amount of options with sometimes few major differences that are not well documented or easy to find. That’s why I’ve decided to create an overview of Apache streaming technologies, including Flume , NiFi , Apex , Spa
Kafka Streams is a new stream processing library natively integrated with Kafka. It has a very low barrier to entry, easy operationalization, and a natural DSL for writing stream processing applications. As such it is the most convenient yet scalable option to analyze, transform, or otherwise process data that is backed by Kafka. We will provide the audience with an overview of Kafka Streams inclu
13. Transformation Meaning map(func) Return a new DStream by passing each element of the source DStream through a function func. flatMap(func) Similar to map, but each input item can be mapped to 0 or more output items. filter(func) Return a new DStream by selecting only the records of the source DStream on which func returns true. repartition(numPartitions) Changes the level of parallelism in thi
Slides of QCon London 2016 talk. How stream processing is used within the Uber's Marketplace system to solve a wide range problems, including but not limited to realtime indexing and querying of geospatial time series, aggregation and computing of streaming data, and extracting patterns from data streams. In addition, it will touch upon various TimeSeries analysis and predictions. The underlying s
オフィシャルサイト： http://sensorbee.io/ GitHub： https://github.com/sensorbee/sensorbee とりあえず、公式サイトの英語を日本語でサマってみました。 オフィシャルサイトでは、"An open source stream processing engine for IoT"、"Stream processing in IoT environment"。 GitHubのREADME.mdでは、"Lightweight stream processing engine for IoT"、と書いてあるので、「IoT向けの軽量なストリーム処理エンジン」と思われる。 特徴としては、低レイテンシが謳われている。 また、次の３つを軸に設計が行われているらしい。 ステートフル 機械学習などでは非構造化データから有益な情報を抽出したりするが、S
5. 5 Stream • Sequence of events/records/data • Features – High throughput/volume – Continuous ingestion • Use cases – Log/event collection – IoT (sensor data, image, etc.) Ex. 1 KB * 10K record/s * 365 days = 300 TB / year Stream => Big Data 6. 6 Stream processing • Event processing – Process each event one by one • Graph for distributed event processing – Ex. Apache Storm • Micro-batch processin
Back in January of 2014 we wrote about the need for better visibility into our complex operational environments: The core of the message in that post was about the need for fine-grained, contextual and scalable insights into the experiences of our customers and behaviors of our services. While our execution has evolved somewhat differently from our original vision, the underlying principles behind
2022-06-25 IT Development 1892 Southern Street, New York 09:00AM-07:00PM Register Target audience: Marketing, advertising and PR specialists of development companies, product and project managers responsible for developing and bringing new services and information systems to market.Entrepreneurs who create promising it services and programs.Marketing managers of companies that have considered or a
米IBMは2016年2月19日（米国時間）、ストリーミングデータ分析機能をIoT（Internet of Things）デバイスに組み込む技術「Quarks」をオープンソースコミュニティーに公開したと発表した。現在の最新版はQuarks v0.3.0で、Raspberry Pi B／Pi2 Bを含むJava 8／7およびAndroidで動作テストが行われている。 Quarksは、各種の機器や乗り物、システム、アプライアンス、センサー、スマートメーターなどからの継続的なIoTデータストリームを、IoTゲートウェイやデバイス上で分析するためのAPIと軽量ランタイムを提供する。Quarksを使うことで、デバイスやゲートウェイで分析処理を行う一方、中央サーバのデータ分析システムと連携できるため、処理速度を速めつつ通信量を削減できる利点があるという。また、多様なデバイス間でも、共通のストリーミング分
The popularity of stream data platforms is skyrocketing. Several companies are transitioning parts of their data infrastructure to a streaming paradigm as a solution to increasing demands for real-time access to information. Infrastructures based on streaming data not only enable new types of latency-critical applications and give more actual operational insights through more up-to-date views of t
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