Leveraging Stream Processing Engines in Support of Physiological Data Processing
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Working with Storm Topologies Date of Publish: 2018-08-13
Apache Storm 3 Working with Storm Topologies Date of Publish: 2018-08-13 http://docs.hortonworks.com Contents Packaging Storm Topologies................................................................................... 3 Deploying and Managing Apache Storm Topologies............................................4 Configuring the Storm UI.................................................................................................................................... 4 Using the Storm UI.............................................................................................................................................. 5 Monitoring and Debugging an Apache Storm Topology......................................6 Enabling Dynamic Log Levels.............................................................................................................................6 Setting and Clearing Log Levels Using the Storm UI.............................................................................6 Setting and Clearing Log Levels Using the CLI..................................................................................... 7 Enabling Topology Event Logging......................................................................................................................7 Configuring Topology Event Logging.....................................................................................................8 Enabling Event Logging...........................................................................................................................8 -
Apache Flink™: Stream and Batch Processing in a Single Engine
Apache Flink™: Stream and Batch Processing in a Single Engine Paris Carboney Stephan Ewenz Seif Haridiy Asterios Katsifodimos* Volker Markl* Kostas Tzoumasz yKTH & SICS Sweden zdata Artisans *TU Berlin & DFKI parisc,[email protected] fi[email protected] fi[email protected] Abstract Apache Flink1 is an open-source system for processing streaming and batch data. Flink is built on the philosophy that many classes of data processing applications, including real-time analytics, continu- ous data pipelines, historic data processing (batch), and iterative algorithms (machine learning, graph analysis) can be expressed and executed as pipelined fault-tolerant dataflows. In this paper, we present Flink’s architecture and expand on how a (seemingly diverse) set of use cases can be unified under a single execution model. 1 Introduction Data-stream processing (e.g., as exemplified by complex event processing systems) and static (batch) data pro- cessing (e.g., as exemplified by MPP databases and Hadoop) were traditionally considered as two very different types of applications. They were programmed using different programming models and APIs, and were exe- cuted by different systems (e.g., dedicated streaming systems such as Apache Storm, IBM Infosphere Streams, Microsoft StreamInsight, or Streambase versus relational databases or execution engines for Hadoop, including Apache Spark and Apache Drill). Traditionally, batch data analysis made up for the lion’s share of the use cases, data sizes, and market, while streaming data analysis mostly served specialized applications. It is becoming more and more apparent, however, that a huge number of today’s large-scale data processing use cases handle data that is, in reality, produced continuously over time. -
Declarative Languages for Big Streaming Data a Database Perspective
Tutorial Declarative Languages for Big Streaming Data A database Perspective Riccardo Tommasini Sherif Sakr University of Tartu Unversity of Tartu [email protected] [email protected] Emanuele Della Valle Hojjat Jafarpour Politecnico di Milano Confluent Inc. [email protected] [email protected] ABSTRACT sources and are pushed asynchronously to servers which are The Big Data movement proposes data streaming systems to responsible for processing them [13]. tame velocity and to enable reactive decision making. However, To facilitate the adoption, initially, most of the big stream approaching such systems is still too complex due to the paradigm processing systems provided their users with a set of API for shift they require, i.e., moving from scalable batch processing to implementing their applications. However, recently, the need for continuous data analysis and pattern detection. declarative stream processing languages has emerged to simplify Recently, declarative Languages are playing a crucial role in common coding tasks; making code more readable and main- fostering the adoption of Stream Processing solutions. In partic- tainable, and fostering the development of more complex appli- ular, several key players introduce SQL extensions for stream cations. Thus, Big Data frameworks (e.g., Flink [9], Spark [3], 1 processing. These new languages are currently playing a cen- Kafka Streams , and Storm [19]) are starting to develop their 2 3 4 tral role in fostering the stream processing paradigm shift. In own SQL-like approaches (e.g., Flink SQL , Beam SQL , KSQL ) this tutorial, we give an overview of the various languages for to declaratively tame data velocity. declarative querying interfaces big streaming data. -
DSP Frameworks DSP Frameworks We Consider
Università degli Studi di Roma “Tor Vergata” Dipartimento di Ingegneria Civile e Ingegneria Informatica DSP Frameworks Corso di Sistemi e Architetture per Big Data A.A. 2017/18 Valeria Cardellini DSP frameworks we consider • Apache Storm (with lab) • Twitter Heron – From Twitter as Storm and compatible with Storm • Apache Spark Streaming (lab) – Reduce the size of each stream and process streams of data (micro-batch processing) • Apache Flink • Apache Samza • Cloud-based frameworks – Google Cloud Dataflow – Amazon Kinesis Streams Valeria Cardellini - SABD 2017/18 1 Apache Storm • Apache Storm – Open-source, real-time, scalable streaming system – Provides an abstraction layer to execute DSP applications – Initially developed by Twitter • Topology – DAG of spouts (sources of streams) and bolts (operators and data sinks) Valeria Cardellini - SABD 2017/18 2 Stream grouping in Storm • Data parallelism in Storm: how are streams partitioned among multiple tasks (threads of execution)? • Shuffle grouping – Randomly partitions the tuples • Field grouping – Hashes on a subset of the tuple attributes Valeria Cardellini - SABD 2017/18 3 Stream grouping in Storm • All grouping (i.e., broadcast) – Replicates the entire stream to all the consumer tasks • Global grouping – Sends the entire stream to a single task of a bolt • Direct grouping – The producer of the tuple decides which task of the consumer will receive this tuple Valeria Cardellini - SABD 2017/18 4 Storm architecture • Master-worker architecture Valeria Cardellini - SABD 2017/18 5 Storm -
Apache Storm Tutorial
Apache Storm Apache Storm About the Tutorial Storm was originally created by Nathan Marz and team at BackType. BackType is a social analytics company. Later, Storm was acquired and open-sourced by Twitter. In a short time, Apache Storm became a standard for distributed real-time processing system that allows you to process large amount of data, similar to Hadoop. Apache Storm is written in Java and Clojure. It is continuing to be a leader in real-time analytics. This tutorial will explore the principles of Apache Storm, distributed messaging, installation, creating Storm topologies and deploy them to a Storm cluster, workflow of Trident, real-time applications and finally concludes with some useful examples. Audience This tutorial has been prepared for professionals aspiring to make a career in Big Data Analytics using Apache Storm framework. This tutorial will give you enough understanding on creating and deploying a Storm cluster in a distributed environment. Prerequisites Before proceeding with this tutorial, you must have a good understanding of Core Java and any of the Linux flavors. Copyright & Disclaimer © Copyright 2014 by Tutorials Point (I) Pvt. Ltd. All the content and graphics published in this e-book are the property of Tutorials Point (I) Pvt. Ltd. The user of this e-book is prohibited to reuse, retain, copy, distribute or republish any contents or a part of contents of this e-book in any manner without written consent of the publisher. We strive to update the contents of our website and tutorials as timely and as precisely as possible, however, the contents may contain inaccuracies or errors. -
Kodai: a Software Architecture and Implementation for Segmentation
University of Rhode Island DigitalCommons@URI Open Access Master's Theses 2017 Kodai: A Software Architecture and Implementation for Segmentation Rick Rejeleene University of Rhode Island, [email protected] Follow this and additional works at: https://digitalcommons.uri.edu/theses Recommended Citation Rejeleene, Rick, "Kodai: A Software Architecture and Implementation for Segmentation" (2017). Open Access Master's Theses. Paper 1107. https://digitalcommons.uri.edu/theses/1107 This Thesis is brought to you for free and open access by DigitalCommons@URI. It has been accepted for inclusion in Open Access Master's Theses by an authorized administrator of DigitalCommons@URI. For more information, please contact [email protected]. KODAI: A SOFTWARE ARCHITECTURE AND IMPLEMENTATION FOR SEGMENTATION BY RICK REJELEENE A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS OF MASTER OF SCIENCE IN COMPUTER SCIENCE UNIVERSITY OF RHODE ISLAND 2017 MASTER OF SCIENCE THESIS OF RICK REJELEENE APPROVED: Thesis Committee: Major Professor: Joan Peckham Lisa DiPippo Ruby Dholakia Nasser H Zawia DEAN OF GRADUATE COMMITTEE UNIVERSITY OF RHODE ISLAND 2017 ABSTRACT The purpose of this thesis is to design and implement a software architecture for segmen- tation models to improve revenues for a supermarket. This tool supports analysis of su- permarket products and generates results to interpret consumer behavior, to give busi- nesses deeper insights into targeted consumer markets. The software design developed is named as Kodai. Kodai is horizontally reusable and can be adapted across various indus- tries. This software framework allows testing a hypothesis to address the problem of in- creasing revenues in supermarkets. Kodai has several advantages, such as analyzing and visualizing data, and as a result, businesses can make better decisions. -
DARPA and Data: a Portfolio Overview
DARPA and Data: A Portfolio Overview William C. Regli Special Assistant to the Director Defense Advanced Research Projects Agency Brian Pierce Director Information Innovation Office Defense Advanced Research Projects Agency Fall 2017 Distribution Statement “A” (Approved for Public Release, Distribution Unlimited) 1 DARPA Dreams of Data • Investments over the past decade span multiple DARPA Offices and PMs • Information Innovation (I2O): Software Systems, AI, Data Analytics • Defense Sciences (DSO): Domain-driven problems (chemistry, social science, materials science, engineering design) • Microsystems Technology (MTO): New hardware to support these processes (neuromorphic processor, graph processor, learning systems) • Products include DARPA Program testbeds, data and software • The DARPA Open Catalog • Testbeds include those in big data, cyber-defense, engineering design, synthetic bio, machine reading, among others • Multiple layers and qualities of data are important • Important for reproducibility; important as fuel for future DARPA programs • Beyond public data to include “raw” data, process/workflow data • Data does not need to be organized to be useful or valuable • Software tools are getting better eXponentially, ”raw” data can be processed • Changing the economics (Forensic Data Curation) • Its about optimizing allocation of attention in human-machine teams Distribution Statement “A” (Approved for Public Release, Distribution Unlimited) 2 Working toward Wisdom Wisdom: sound judgment - governance Abstraction Wisdom (also Understanding: -
STORM: Refinement Types for Secure Web Applications
STORM: Refinement Types for Secure Web Applications Nico Lehmann Rose Kunkel Jordan Brown Jean Yang UC San Diego UC San Diego Independent Akita Software Niki Vazou Nadia Polikarpova Deian Stefan Ranjit Jhala IMDEA Software Institute UC San Diego UC San Diego UC San Diego Abstract Centralizing policy specification is not a new idea. Several We present STORM, a web framework that allows developers web frameworks (e.g., HAILS [4], JACQUELINE [5], and to build MVC applications with compile-time enforcement of LWEB [6]) already do this. These frameworks, however, have centrally specified data-dependent security policies. STORM two shortcomings that have hindered their adoption. First, ensures security using a Security Typed ORM that refines the they enforce policies at run-time, typically using dynamic (type) abstractions of each layer of the MVC API with logical information flow control (IFC). While dynamic enforcement assertions that describe the data produced and consumed by is better than no enforcement, dynamic IFC imposes a high the underlying operation and the users allowed access to that performance overhead, since the system must be modified to data. To evaluate the security guarantees of STORM, we build a track the provenance of data and restrict where it is allowed formally verified reference implementation using the Labeled to flow. More importantly, certain policy violations are only IO (LIO) IFC framework. We present case studies and end-to- discovered once the system is deployed, at which point they end applications that show how STORM lets developers specify may be difficult or expensive to fix, e.g., on applications diverse policies while centralizing the trusted code to under 1% running on IoT devices [7]. -
FOSS Philosophy 6 the FOSS Development Method 7
1 Published by the United Nations Development Programme’s Asia-Pacific Development Information Programme (UNDP-APDIP) Kuala Lumpur, Malaysia www.apdip.net Email: [email protected] © UNDP-APDIP 2004 The material in this book may be reproduced, republished and incorporated into further works provided acknowledgement is given to UNDP-APDIP. For full details on the license governing this publication, please see the relevant Annex. ISBN: 983-3094-00-7 Design, layout and cover illustrations by: Rezonanze www.rezonanze.com PREFACE 6 INTRODUCTION 6 What is Free/Open Source Software? 6 The FOSS philosophy 6 The FOSS development method 7 What is the history of FOSS? 8 A Brief History of Free/Open Source Software Movement 8 WHY FOSS? 10 Is FOSS free? 10 How large are the savings from FOSS? 10 Direct Cost Savings - An Example 11 What are the benefits of using FOSS? 12 Security 13 Reliability/Stability 14 Open standards and vendor independence 14 Reduced reliance on imports 15 Developing local software capacity 15 Piracy, IPR, and the WTO 16 Localization 16 What are the shortcomings of FOSS? 17 Lack of business applications 17 Interoperability with proprietary systems 17 Documentation and “polish” 18 FOSS SUCCESS STORIES 19 What are governments doing with FOSS? 19 Europe 19 Americas 20 Brazil 21 Asia Pacific 22 Other Regions 24 What are some successful FOSS projects? 25 BIND (DNS Server) 25 Apache (Web Server) 25 Sendmail (Email Server) 25 OpenSSH (Secure Network Administration Tool) 26 Open Office (Office Productivity Suite) 26 LINUX 27 What is Linux? -
HDP 3.1.4 Release Notes Date of Publish: 2019-08-26
Release Notes 3 HDP 3.1.4 Release Notes Date of Publish: 2019-08-26 https://docs.hortonworks.com Release Notes | Contents | ii Contents HDP 3.1.4 Release Notes..........................................................................................4 Component Versions.................................................................................................4 Descriptions of New Features..................................................................................5 Deprecation Notices.................................................................................................. 6 Terminology.......................................................................................................................................................... 6 Removed Components and Product Capabilities.................................................................................................6 Testing Unsupported Features................................................................................ 6 Descriptions of the Latest Technical Preview Features.......................................................................................7 Upgrading to HDP 3.1.4...........................................................................................7 Behavioral Changes.................................................................................................. 7 Apache Patch Information.....................................................................................11 Accumulo........................................................................................................................................................... -
Multimedia Systems
MVC Design Pattern Introduction to MVC and compare it with others Gholamhossein Tavasoli @ ZNU Separation of Concerns o All these methods do only one thing “Separation of Concerns” or “Layered Architecture” but in their own way. o All these concepts are pretty old, like idea of MVC was tossed in 1970s. o All these patterns forces a separation of concerns, it means domain model and controller logic are decoupled from user interface (view). As a result maintenance and testing of the application become simpler and easier. MVC Pattern Architecture o MVC stands for Model-View-Controller Explanation of Modules: Model o The Model represents a set of classes that describe the business logic i.e. business model as well as data access operations i.e. data model. o It also defines business rules for data means how the data can be created, sotored, changed and manipulated. Explanation of Modules: View o The View represents the UI components like CSS, jQuery, HTML etc. o It is only responsible for displaying the data that is received from the controller as the result. o This also transforms the model(s) into UI. o Views can be nested Explanation of Modules: Controller o The Controller is responsible to process incoming requests. o It receives input from users via the View, then process the user's data with the help of Model and passing the results back to the View. o Typically, it acts as the coordinator between the View and the Model. o There is One-to-Many relationship between Controller and View, means one controller can handle many views. -
After the Storm After the Storm the Jobs and Skills That Will Drive the Post-Pandemic Recovery
After the Storm After the Storm The Jobs and Skills that will Drive the Post-Pandemic Recovery February 2021 © Burning Glass Technologies 2021 1 After the Storm Table of Contents Table of Contents 1. Executive Summary pg 3 2. Introduction pg 6 3. The Readiness Economy pg 14 4. The Logistics Economy pg 16 5. The Green Economy pg 19 6. The Remote Economy pg 22 7. The Automated Economy pg 24 8. Implications pg 27 9. Methodology pg 29 10. Appendix pg 30 By Matthew Sigelman, Scott Bittle, Nyerere Hodge, Layla O’Kane, and Bledi Taska, with Joshua Bodner, Julia Nitschke, and Rucha Vankudre ©© BurningBurning GlassGlass TechnologiesTechnologies 20212021 22 After the Storm Executive Summary 1 Executive Summary The recession left in the wake of the • These jobs represent significant fractions COVID-19 pandemic is unprecedented. of the labor market: currently 13% of The changes have been so profound that demand and 10% of employment, but fundamental patterns of how we work, in addition they are important inflection produce, move, and sell will never be the points for the economy. A shortage same. of talent in these fields could set back broader recovery if organizations can’t If the U.S. is going to have a recovery that cope with these demands. not only brings the economy back to where • Jobs in these new “economies” are it was but also ensures a more equitable projected to grow at almost double the future, it is crucial to understand what jobs rate of the job market overall (15% vs. and skills are likely to drive the recovery.