Apache Cxf Client Example
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Apache Cxf Rest Service Example Bruzek
Apache Cxf Rest Service Example Tad never paved any Akkadian intergrading unknowingly, is Aubrey light and resplendent enough? Knotty Lambert tattles some sigmoidectomy after antiodontalgic Tucker conceived aerobiotically. Nickie remains Sadducean after Iggie personifying inevitably or seek any chump. Running on creating the apache rest example if you run it all edits are capable of its recommended to create your browser go to learn apache cxf as the xml? Most english words and get a sample shows throwing exceptions occurred while the help? Easier than to use when the rest dsl will keep the operation on the spring configuration for connection. Dom elements or a spring or attenuate the default values into the classes. Control will generate a java or checkout with spring xml we mentioned before you progress through the methods. Invoked it is enabled and test but the dzone. Office be using your rest service which sends multiple endpoints. High force than to start with a rest service using the code to know to build the server? Trackers while you from apache cxf service example a rest service engine uses akismet to add user does the above. Easiest way to cxf rest service example a custom configured for tomcat? Zombie that the hostname the parts of all injection points are not going to download ibm liberty for communication. Help icon above json outputted in or conditions of the camel components and i motivate the camel! Diverts it so, cxf rest styled dsl consumes the steps to build the routing? Bean to generate the apache service which listens to be nice if set this option on the routes. -
Web Services CXF User Guide
JBoss Enterprise Application Platform 5 Web Services CXF User Guide for use with JBoss Enterprise Application Platform 5 Edition 5.2.0 Last Updated: 2017-10-13 JBoss Enterprise Application Platform 5 Web Services CXF User Guide for use with JBoss Enterprise Application Platform 5 Edition 5.2.0 Alessio Soldano Edited by Elspeth Thorne Eva Kopalova Petr Penicka Rebecca Newton Russell Dickenson Scott Mumford Legal Notice Copyright © 2012 Red Hat, Inc. This document is licensed by Red Hat under the Creative Commons Attribution-ShareAlike 3.0 Unported License. If you distribute this document, or a modified version of it, you must provide attribution to Red Hat, Inc. and provide a link to the original. If the document is modified, all Red Hat trademarks must be removed. Red Hat, as the licensor of this document, waives the right to enforce, and agrees not to assert, Section 4d of CC-BY-SA to the fullest extent permitted by applicable law. Red Hat, Red Hat Enterprise Linux, the Shadowman logo, JBoss, OpenShift, Fedora, the Infinity logo, and RHCE are trademarks of Red Hat, Inc., registered in the United States and other countries. Linux ® is the registered trademark of Linus Torvalds in the United States and other countries. Java ® is a registered trademark of Oracle and/or its affiliates. XFS ® is a trademark of Silicon Graphics International Corp. or its subsidiaries in the United States and/or other countries. MySQL ® is a registered trademark of MySQL AB in the United States, the European Union and other countries. Node.js ® is an official trademark of Joyent. -
Enterprise Integration Patterns N About Apache Camel N Essential Patterns Enterprise Integration Patterns N Conclusions and More
Brought to you by... #47 CONTENTS INCLUDE: n About Enterprise Integration Patterns n About Apache Camel n Essential Patterns Enterprise Integration Patterns n Conclusions and more... with Apache Camel Visit refcardz.com By Claus Ibsen ABOUT ENTERPRISE INTEGRATION PaTTERNS Problem A single event often triggers a sequence of processing steps Solution Use Pipes and Filters to divide a larger processing steps (filters) that are connected by channels (pipes) Integration is a hard problem. To help deal with the complexity Camel Camel supports Pipes and Filters using the pipeline node. of integration problems the Enterprise Integration Patterns Java DSL from(“jms:queue:order:in”).pipeline(“direct:transformOrd (EIP) have become the standard way to describe, document er”, “direct:validateOrder”, “jms:queue:order:process”); and implement complex integration problems. Hohpe & Where jms represents the JMS component used for consuming JMS messages Woolf’s book the Enterprise Integration Patterns has become on the JMS broker. Direct is used for combining endpoints in a synchronous fashion, allow you to divide routes into sub routes and/or reuse common routes. the bible in the integration space – essential reading for any Tip: Pipeline is the default mode of operation when you specify multiple integration professional. outputs, so it can be omitted and replaced with the more common node: from(“jms:queue:order:in”).to(“direct:transformOrder”, “direct:validateOrder”, “jms:queue:order:process”); Apache Camel is an open source project for implementing TIP: You can also separate each step as individual to nodes: the EIP easily in a few lines of Java code or Spring XML from(“jms:queue:order:in”) configuration. -
Nimsoft Monitor
Nimsoft Monitor SOAP Web Services Getting Started Guide Version 2.0 Legal Notices Copyright © 2012 CA. All rights reserved. Warranty The material contained in this document is provided "as is," and is subject to being changed, without notice, in future editions. Further, to the maximum extent permitted by applicable law, Nimsoft LLC disclaims all warranties, either express or implied, with regard to this manual and any information contained herein, including but not limited to the implied warranties of merchantability and fitness for a particular purpose. Nimsoft LLC shall not be liable for errors or for incidental or consequential damages in connection with the furnishing, use, or performance of this document or of any information contained herein. Should Nimsoft LLC and the user have a separate written agreement with warranty terms covering the material in this document that conflict with these terms, the warranty terms in the separate agreement shall control. Technology Licenses The hardware and/or software described in this document are furnished under a license and may be used or copied only in accordance with the terms of such license. No part of this manual may be reproduced in any form or by any means (including electronic storage and retrieval or translation into a foreign language) without prior agreement and written consent from Nimsoft LLC as governed by United States and international copyright laws. Restricted Rights Legend If software is for use in the performance of a U.S. Government prime contract or subcontract, Software is delivered and licensed as "Commercial computer software" as defined in DFAR 252.227-7014 (June 1995), or as a "commercial item" as defined in FAR 2.101(a) or as "Restricted computer software" as defined in FAR 52.227-19 (June 1987) or any equivalent agency regulation or contract clause. -
Apache Camel
Apache Camel USER GUIDE Version 2.0.0 Copyright 2007-2009, Apache Software Foundation 1 Table of Contents Table of Contents......................................................................... ii Chapter 1 Introduction ...................................................................................1 Chapter 2 Quickstart.......................................................................................1 Chapter 3 Getting Started..............................................................................7 Chapter 4 Architecture................................................................................ 17 Chapter 5 Enterprise Integration Patterns.............................................. 27 Chapter 6 Cook Book ................................................................................... 32 Chapter 7 Tutorials....................................................................................... 85 Chapter 8 Language Appendix.................................................................. 190 Chapter 9 Pattern Appendix..................................................................... 231 Chapter 10 Component Appendix ............................................................. 299 Index ................................................................................................0 ii APACHE CAMEL CHAPTER 1 °°°° Introduction Apache Camel is a powerful open source integration framework based on known Enterprise Integration Patterns with powerful Bean Integration. Camel lets you create the Enterprise Integration -
An Enterprise Knowledge Network
Fogbeam Labs Cut Through The Information Fog http://www.fogbeam.com An Enterprise Knowledge Network Knowledge exists in many forms inside your organization – ranging from tacit knowledge which exists only in the minds of the users who possess it, to codified knowledge stored in databases and document repositories. Unfortunately while knowledge exists throughout the organization, it is often not easy (if even possible) to locate, use, share, and reuse existing knowledge. This results in a situation often described as “the left hand doesn't know what the right hand is doing” and damages morale as employees spend their days frustrated and complaining that “nobody knows what is going on around here”. The obstacles that hinder access to existing knowledge can be cultural, geographical, social, and/or technological. And while no technological solution can guarantee perfect knowledge-sharing, tools drawn from big data, data mining / machine learning, deep learning, and artificial intelligence techniques can improve an organization's power to generate, capture, use, share and reuse knowledge. Using technologies developed as part of the semantic web initiative, and applying the principles of linked data within the enterprise, the Fogbeam Labs Enterprise Knowledge Network approach can help your firm integrate and aggregate knowledge which is spread across your existing enterprise applications, content repositories and Intranet. An Enterprise Knowledge Network enables your firm's capabilities to: • engage in high levels of knowledge transfer and -
Action-Based Study and Development of a Web Service Application in Java for METLA
Prakash Sapkota Action-Based Study and Development of a Web Service Application in Java for METLA Helsinki Metropolia University of Applied Sciences Bachelor of Engineering Information Technology Bachelor’s Thesis 30 January 2014 Abstract Author Prakash Sapkota Title Action-Based Study and Development of a Web Service Appli- cation in Java for METLA Number of Pages 38 pages + 4 appendices Date 30 January 2014 Degree Bachelor of Engineering Degree Programme Information Technology Specialisation option Software Engineering Instructor(s) Mika Galkin, Senior System Analyst Sami Sainio, Lecturer The primary purpose of the thesis project was to carry out an action-based study of web services by developing a forestry related web service application for MetINFO. MetINFO is an information division of the Finnish Forest Research Institute (METLA). It provides various forest-related information services and tools in order to make forest- related information more visible and useful. The goal of the project was to develop a web service application which could be used by Finnish sawmills to upload their roundwood sales data to MetINFO. The uploaded data is used to calculate statistics about roundwood sales in Finland by different forestry centers and price areas. The development of the project involved various steps. Initially, the requirements of the application were analyzed. Based on the requirements, the application was designed and developed using feature-driven development methodology. As the outcome, fully function- ing web services for uploading roundwood sales data and a web based application for ad- ministering uploaded data were created. The developed application was tested in a test environment and all the known bugs were fixed. -
Talend Open Studio for Big Data Release Notes
Talend Open Studio for Big Data Release Notes 6.0.0 Talend Open Studio for Big Data Adapted for v6.0.0. Supersedes previous releases. Publication date July 2, 2015 Copyleft This documentation is provided under the terms of the Creative Commons Public License (CCPL). For more information about what you can and cannot do with this documentation in accordance with the CCPL, please read: http://creativecommons.org/licenses/by-nc-sa/2.0/ Notices Talend is a trademark of Talend, Inc. All brands, product names, company names, trademarks and service marks are the properties of their respective owners. License Agreement The software described in this documentation is licensed under the Apache License, Version 2.0 (the "License"); you may not use this software except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0.html. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. This product includes software developed at AOP Alliance (Java/J2EE AOP standards), ASM, Amazon, AntlR, Apache ActiveMQ, Apache Ant, Apache Avro, Apache Axiom, Apache Axis, Apache Axis 2, Apache Batik, Apache CXF, Apache Cassandra, Apache Chemistry, Apache Common Http Client, Apache Common Http Core, Apache Commons, Apache Commons Bcel, Apache Commons JxPath, Apache -
Regeldokument
Master’s degree project Source code quality in connection to self-admitted technical debt Author: Alina Hrynko Supervisor: Morgan Ericsson Semester: VT20 Subject: Computer Science Abstract The importance of software code quality is increasing rapidly. With more code being written every day, its maintenance and support are becoming harder and more expensive. New automatic code review tools are developed to reach quality goals. One of these tools is SonarQube. However, people keep their leading role in the development process. Sometimes they sacrifice quality in order to speed up the development. This is called Technical Debt. In some particular cases, this process can be admitted by the developer. This is called Self-Admitted Technical Debt (SATD). Code quality can also be measured by such static code analysis tools as SonarQube. On this occasion, different issues can be detected. The purpose of this study is to find a connection between code quality issues, found by SonarQube and those marked as SATD. The research questions include: 1) Is there a connection between the size of the project and the SATD percentage? 2) Which types of issues are the most widespread in the code, marked by SATD? 3) Did the introduction of SATD influence the bug fixing time? As a result of research, a certain percentage of SATD was found. It is between 0%–20.83%. No connection between the size of the project and the percentage of SATD was found. There are certain issues that seem to relate to the SATD, such as “Duplicated code”, “Unused method parameters should be removed”, “Cognitive Complexity of methods should not be too high”, etc. -
Trifacta Data Preparation for Amazon Redshift and S3 Must Be Deployed Into an Existing Virtual Private Cloud (VPC)
Install Guide for Data Preparation for Amazon Redshift and S3 Version: 7.1 Doc Build Date: 05/26/2020 Copyright © Trifacta Inc. 2020 - All Rights Reserved. CONFIDENTIAL These materials (the “Documentation”) are the confidential and proprietary information of Trifacta Inc. and may not be reproduced, modified, or distributed without the prior written permission of Trifacta Inc. EXCEPT AS OTHERWISE PROVIDED IN AN EXPRESS WRITTEN AGREEMENT, TRIFACTA INC. PROVIDES THIS DOCUMENTATION AS-IS AND WITHOUT WARRANTY AND TRIFACTA INC. DISCLAIMS ALL EXPRESS AND IMPLIED WARRANTIES TO THE EXTENT PERMITTED, INCLUDING WITHOUT LIMITATION THE IMPLIED WARRANTIES OF MERCHANTABILITY, NON-INFRINGEMENT AND FITNESS FOR A PARTICULAR PURPOSE AND UNDER NO CIRCUMSTANCES WILL TRIFACTA INC. BE LIABLE FOR ANY AMOUNT GREATER THAN ONE HUNDRED DOLLARS ($100) BASED ON ANY USE OF THE DOCUMENTATION. For third-party license information, please select About Trifacta from the Help menu. 1. Quick Start . 4 1.1 Install from AWS Marketplace . 4 1.2 Upgrade for AWS Marketplace . 7 2. Configure . 8 2.1 Configure for AWS . 8 2.1.1 Configure for EC2 Role-Based Authentication . 14 2.1.2 Enable S3 Access . 16 2.1.2.1 Create Redshift Connections 28 3. Contact Support . 30 4. Legal 31 4.1 Third-Party License Information . 31 Page #3 Quick Start Install from AWS Marketplace Contents: Product Limitations Internet access Install Desktop Requirements Pre-requisites Install Steps - CloudFormation template SSH Access Troubleshooting SELinux Upgrade Documentation Related Topics This guide steps through the requirements and process for installing Trifacta® Data Preparation for Amazon Redshift and S3 through the AWS Marketplace. -
Portable Stateful Big Data Processing in Apache Beam
Portable stateful big data processing in Apache Beam Kenneth Knowles Apache Beam PMC Software Engineer @ Google https://s.apache.org/ffsf-2017-beam-state [email protected] / @KennKnowles Flink Forward San Francisco 2017 Agenda 1. What is Apache Beam? 2. State 3. Timers 4. Example & Little Demo What is Apache Beam? TL;DR (Flink draws it more like this) 4 DAGs, DAGs, DAGs Apache Beam Apache Flink Apache Cloud Hadoop Apache Apache Dataflow Spark Samza MapReduce Apache Apache Apache (paper) Storm Gearpump Apex (incubating) FlumeJava (paper) Heron MillWheel (paper) Dataflow Model (paper) 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Apache Flink local, on-prem, The Beam Vision cloud Cloud Dataflow: Java fully managed input.apply( Apache Spark Sum.integersPerKey()) local, on-prem, cloud Sum Per Key Apache Apex Python local, on-prem, cloud input | Sum.PerKey() Apache Gearpump (incubating) ⋮ ⋮ 6 Apache Flink local, on-prem, The Beam Vision cloud Cloud Dataflow: Python fully managed input | KakaIO.read() Apache Spark local, on-prem, cloud KafkaIO Apache Apex ⋮ local, on-prem, cloud Apache Java Gearpump (incubating) class KafkaIO extends UnboundedSource { … } ⋮ 7 The Beam Model PTransform Pipeline PCollection (bounded or unbounded) 8 The Beam Model What are you computing? (read, map, reduce) Where in event time? (event time windowing) When in processing time are results produced? (triggers) How do refinements relate? (accumulation mode) 9 What are you computing? Read ParDo Grouping Composite Parallel connectors to Per element Group -
The Forrester Wave™: Streaming Analytics, Q3 2019 the 11 Providers That Matter Most and How They Stack up by Mike Gualtieri September 23, 2019
LICENSED FOR INDIVIDUAL USE ONLY The Forrester Wave™: Streaming Analytics, Q3 2019 The 11 Providers That Matter Most And How They Stack Up by Mike Gualtieri September 23, 2019 Why Read This Report Key Takeaways In our 26-criterion evaluation of streaming Software AG, IBM, Microsoft, Google, And analytics providers, we identified the 11 most TIBCO Software Lead The Pack significant ones — Alibaba, Amazon Web Forrester’s research uncovered a market in which Services, Cloudera, EsperTech, Google, IBM, Software AG, IBM, Microsoft, Google, and TIBCO Impetus, Microsoft, SAS, Software AG, and Software are Leaders; Cloudera, SAS, Amazon TIBCO Software — and researched, analyzed, Web Services, and Impetus are Strong Performers; and scored them. This report shows how each and EsperTech and Alibaba are Contenders. provider measures up and helps application Analytics Prowess, Scalability, And development and delivery (AD&D) professionals Deployment Freedom Are Key Differentiators select the right one for their needs. Depth and breadth of analytics types on streaming data are critical. But that is all for naught if streaming analytics vendors cannot also scale to handle potentially huge volumes of streaming data. Also, it’s critical that streaming analytics can be deployed where it is most needed, such as on-premises, in the cloud, and/ or at the edge. This PDF is only licensed for individual use when downloaded from forrester.com or reprints.forrester.com. All other distribution prohibited. FORRESTER.COM FOR APPLICATION DEVELOPMENT & DELIVERY PROFESSIONALS The Forrester Wave™: Streaming Analytics, Q3 2019 The 11 Providers That Matter Most And How They Stack Up by Mike Gualtieri with Srividya Sridharan and Robert Perdoni September 23, 2019 Table Of Contents Related Research Documents 2 Enterprises Must Take A Streaming-First The Future Of Machine Learning Is Unstoppable Approach To Analytics Predictions 2019: Artificial Intelligence 3 Evaluation Summary Predictions 2019: Business Insights 6 Vendor Offerings 6 Vendor Profiles Leaders Share reports with colleagues.