A system for processing streaming data in real time
Apache™ Storm adds reliable real-time data processing capabilities to Enterprise Hadoop. Storm on YARN is powerful for scenarios requiring real-time analytics, machine learning and continuous monitoring of operations.
Storm integrates with YARN via Apache Slider, YARN manages Storm while also considering cluster resources for data governance, security and operations components of a modern data architecture.
Storm is a distributed real-time computation system for processing large volumes of high-velocity data. Storm is extremely fast, with the ability to process over a million records per second per node on a cluster of modest size. Enterprises harness this speed and combine it with other data access applications in Hadoop to prevent undesirable events or to optimize positive outcomes.
Some of specific new business opportunities include: real-time customer service management, data monetization, operational dashboards, or cyber security analytics and threat detection.
Here are some typical “prevent” and “optimize” use cases for Storm.
|“Prevent” Use Cases||“Optimize” Use Cases|
Five characteristics make Storm ideal for real-time data processing workloads. Storm is:
A storm cluster has three sets of nodes:
Five key abstractions help to understand how Storm processes data:
Storm users define topologies for how to process the data when it comes streaming in from the spout. When the data comes in, it is processed and the results are passed into Hadoop.
Learn more about how the community is working to integrate Storm with Hadoop and improve its readiness for the enterprise.
Hortonworks is focused on developer productivity, enterprise readiness and operational simplicity of Storm.
For more info: Announcement Apache Storm 1.0
The Apache Storm open source community has already begun working on those themes.
|Apache Storm Version||Enhancements||HDP Versions||HDF Versions|
Introduction Hadoop has always been associated with BigData, yet the perception is it’s only suitable for high latency, high throughput queries. With the contribution of the community, you can use Hadoop interactively for data exploration and visualization. In this tutorial you’ll learn how to analyze large datasets using Apache Hive LLAP on Amazon Web Services […]
多くのお客様から非常によくいただくリクエストは、たとえばスキャンした PNG ファイルのテキストなど、画像ファイル中でテキストをインデックスすることです。このチュートリアルでは、それを SOLR を使って行う方法を段階的に説明します。前提条件：Hortonworks Sandbox がダウンロードされていること、「HDP Sandbox のコツを学ぶ」のチュートリアルを完了していること。ステップバイステップ・ガイド […]
Introduction In this tutorial, you will learn about the different features available in the HDF sandbox. HDF stands for Hortonworks DataFlow. HDF was built to make processing data-in-motion an easier task while also directing the data from source to the destination. You will learn about quick links to access these tools that way when you […]
はじめに：JReport は、Apache Hive の JDBC ドライバを使用して Hortonworks Data Platform 2.3 からデータを簡単に抽出し可視化することができる、組み込み BI レポーティングツールです。レポート、ダッシュボード、データ分析を作成することが可能で、後で自分のアプリケーションに組み込むこともできます。このチュートリアルでは、次のステップをご説明します[...]
The Hortonworks Sandbox is delivered as a Dockerized container with the most common ports already opened and forwarded for you. If you would like to open even more ports, check out this tutorial.
Introduction R is a popular tool for statistics and data analysis. It has rich visualization capabilities and a large collection of libraries that have been developed and maintained by the R developer community. One drawback to R is that it’s designed to run on in-memory data, which makes it unsuitable for large datasets. Spark is […]
Apache Zeppelin on HDP 2.4.2 Author: Vinay Shukla In March 2016 we delivered the second technical preview of Apache Zeppelin, on HDP 2.4. Meanwhile we and the Zeppelin community have continued to add new features to Zeppelin. These features are now available in the final technical preview of Apache Zeppelin. This technical preview works with […]
Welcome to the Hortonworks Sandbox! Look at the attached sections for sandbox documentation.
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