21St Century Open-Source Data Flows
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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. -
Use Splunk with Big Data Repositories Like Spark, Solr, Hadoop and Nosql Storage
Copyright © 2016 Splunk Inc. Use Splunk With Big Data Repositories Like Spark, Solr, Hadoop And Nosql Storage Raanan Dagan, May Long Big Data Architect, Splunk Disclaimer During the course of this presentaon, we may make forward looking statements regarding future events or the expected performance of the company. We cauJon you that such statements reflect our current expectaons and esJmates based on factors currently known to us and that actual events or results could differ materially. For important factors that may cause actual results to differ from those contained in our forward-looking statements, please review our filings with the SEC. The forward- looking statements made in the this presentaon are being made as of the Jme and date of its live presentaon. If reviewed aer its live presentaon, this presentaon may not contain current or accurate informaon. We do not assume any obligaon to update any forward looking statements we may make. In addiJon, any informaon about our roadmap outlines our general product direcJon and is subject to change at any Jme without noJce. It is for informaonal purposes only and shall not, be incorporated into any contract or other commitment. Splunk undertakes no obligaon either to develop the features or funcJonality described or to include any such feature or funcJonality in a future release. 2 Agenda Use Cases: Fraud With Solr, Splunk, And Splunk AnalyJcs For Hadoop Business AnalyJcs With Cassandra, Splunk Cloud, And Splunk AnalyJcs For Hadoop Document Classificaon With Spark And Splunk Network IT With Kaa And Splunk Kaa Add On Demo 3 Fraud With Solr, Splunk, And Splunk AnalyJcs For Hadoop Use Case: Fraud – Why Apache Solr Apache Solr is an open source enterprise search plaorm from the Apache Lucene API. -
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 -
Personal Information Backgrounds Abilities Highlights
Yi Du Computer Network Information Center, Chinese Academy of Sciences Phone:+86-15810134970 Email：[email protected] Homepage: http://yiducn.github.io/ Personal Information Gender： Male Date of Graduate：July, 2013 Address： 4# Building, South 4th Street Zhong Guan Cun. Beijing, P.R.China. 100190 Backgrounds 2015.12~Now Department of Big Data Technology and Application Development, Computer Network Information Center Beijing, China Job Title: Associate Professor Research Interest: Data Mining, Visual Analytics 2015.09~2016.09 School of Electrical and Computer Engineering, Purdue University USA Job Title: Visiting Scholar Research Interest: Spatio-temporal Visualization, Visual Analytics 2013.09~2015.12 Scientific Data Center, Computer Network Information Center, CAS Beijing, China Job Title: Assistant Professor Research Interest: Data Processing, Data Visualization, HCI 2008.09~2013.09 Institute of Software Chinese Academy of Sciences Beijing, China Major: Computer Applied Technology Doctoral Degree Research Interest: Human Computer Interaction(HCI), Information Visualization 2004.08-2008.07 Shandong University Jinan Major: Software Engineering Bachelor's Degree Abilities Master the design and development of data science system, including data collecting, wrangling, analyzing, mining, visualizing and interacting. Master analyzing and mining of large-scale spatio-temporal data. Experiences in coding with Java, JavaScript. Familiar with Python and C++. Experiences in MongoDB, DB2. Familiar with MS SQLServer and Oracle. Experiences in traditional data mining and machine learning algorithms. Familiar with Hadoop, Titan, Kylin, etc. Highlights Participated in an open source project Gephi, contributed several plugins with over 3000 lines of code. An analytics platform named DVIZ, in which I played the leading role, gained several prizes in China. -
Hortonworks Cybersecurity Platform Administration (April 24, 2018)
Hortonworks Cybersecurity Platform Administration (April 24, 2018) docs.cloudera.com Hortonworks Cybersecurity April 24, 2018 Platform Hortonworks Cybersecurity Platform: Administration Copyright © 2012-2018 Hortonworks, Inc. Some rights reserved. Hortonworks Cybersecurity Platform (HCP) is a modern data application based on Apache Metron, powered by Apache Hadoop, Apache Storm, and related technologies. HCP provides a framework and tools to enable greater efficiency in Security Operation Centers (SOCs) along with better and faster threat detection in real-time at massive scale. It provides ingestion, parsing and normalization of fully enriched, contextualized data, threat intelligence feeds, triage and machine learning based detection. It also provides end user near real-time dashboarding. Based on a strong foundation in the Hortonworks Data Platform (HDP) and Hortonworks DataFlow (HDF) stacks, HCP provides an integrated advanced platform for security analytics. Please visit the Hortonworks Data Platform page for more information on Hortonworks technology. For more information on Hortonworks services, please visit either the Support or Training page. Feel free to Contact Us directly to discuss your specific needs. Except where otherwise noted, this document is licensed under Creative Commons Attribution ShareAlike 4.0 License. http://creativecommons.org/licenses/by-sa/4.0/legalcode ii Hortonworks Cybersecurity April 24, 2018 Platform Table of Contents 1. HCP Information Roadmap ......................................................................................... -
My Steps to Learn About Apache Nifi
My steps to learn about Apache NiFi Paulo Jerônimo, 2018-05-24 05:36:18 WEST Table of Contents Introduction. 1 About this document . 1 About me . 1 Videos with a technical background . 2 Lab 1: Running Apache NiFi inside a Docker container . 3 Prerequisites . 3 Start/Restart. 3 Access to the UI . 3 Status. 3 Stop . 3 Lab 2: Running Apache NiFi locally . 5 Prerequisites . 5 Installation. 5 Start . 5 Access to the UI . 5 Status. 5 Stop . 6 Lab 3: Building a simple Data Flow . 7 Prerequisites . 7 Step 1 - Create a Nifi docker container with default parameters . 7 Step 2 - Access the UI and create two processors . 7 Step 3 - Add and configure processor 1 (GenerateFlowFile) . 7 Step 4 - Add and configure processor 2 (Putfile) . 10 Step 5 - Connect the processors . 12 Step 6 - Start the processors. 14 Step 7 - View the generated logs . 14 Step 8 - Stop the processors . 15 Step 9 - Stop and destroy the docker container . 15 Conclusions . 15 All references . 16 Introduction Recently I had work to produce a document with a comparison between two tools for Cloud Data Flow. I didn’t have any knowledge of this kind of technology before creating this document. Apache NiFi is one of the tools in my comparison document. So, here I describe some of my procedures to learn about it and take my own preliminary conclusions. I followed many steps on my own desktop (a MacBook Pro computer) to accomplish this task. This document shows you what I did. Basically, to learn about Apache NiFi in order to do a comparison with other tool: • I saw some videos about it. -
Handling Data Flows of Streaming Internet of Things Data
IT16048 Examensarbete 30 hp Juni 2016 Handling Data Flows of Streaming Internet of Things Data Yonatan Kebede Serbessa Masterprogram i datavetenskap Master Programme in Computer Science i Abstract Handling Data Flows of Streaming Internet of Things Data Yonatan Kebede Serbessa Teknisk- naturvetenskaplig fakultet UTH-enheten Streaming data in various formats is generated in a very fast way and these data needs to be processed and analyzed before it becomes useless. The technology currently Besöksadress: existing provides the tools to process these data and gain more meaningful Ångströmlaboratoriet Lägerhyddsvägen 1 information out of it. This thesis has two parts: theoretical and practical. The Hus 4, Plan 0 theoretical part investigates what tools are there that are suitable for stream data flow processing and analysis. In doing so, it starts with studying one of the main Postadress: streaming data source that produce large volumes of data: Internet of Things. In this, Box 536 751 21 Uppsala the technologies behind it, common use cases, challenges, and solutions are studied. Then it is followed by overview of selected tools namely Apache NiFi, Apache Spark Telefon: Streaming and Apache Storm studying their key features, main components, and 018 – 471 30 03 architecture. After the tools are studied, 5 parameters are selected to review how Telefax: each tool handles these parameters. This can be useful for considering choosing 018 – 471 30 00 certain tool given the parameters and the use case at hand. The second part of the thesis involves Twitter data analysis which is done using Apache NiFi, one of the tools Hemsida: studied. The purpose is to show how NiFi can be used for processing data starting http://www.teknat.uu.se/student from ingestion to finally sending it to storage systems. -
Realization of EAI Patterns with Apache Camel
Institut für Architektur von Anwendungssystemen Universität Stuttgart Universitätsstraße 38 70569 Stuttgart Studienarbeit Nr. 2127 Realization of EAI Patterns with Apache Camel Pascal Kolb Studiengang: Informatik Prüfer: Prof. Dr. Frank Leymann Betreuer: Dipl.‐Inf. Thorsten Scheibler begonnen am: 26.10.2007 beendet am: 26.04.2008 CR‐Klassifikation D.2.11, D.3, H.4.1 Table of Contents Table of Listings ............................................................................................................. vii 1 Introduction ............................................................................................................. 1 1.1 Task Description ................................................................................................................................. 1 1.2 Structure of this thesis ....................................................................................................................... 2 2 Apache Camel Fundamentals ................................................................................... 3 2.1 Introduction into Apache Camel ........................................................................................................ 3 2.2 Apache Camel’s Architecture ............................................................................................................. 4 2.2.1 Camel Components and Endpoints............................................................................................ 4 2.2.2 Camel Exchange and Message .................................................................................................. -
Hdf® Stream Developer 3 Days
TRAINING OFFERING | DEV-371 HDF® STREAM DEVELOPER 3 DAYS This course is designed for Data Engineers, Data Stewards and Data Flow Managers who need to automate the flow of data between systems as well as create real-time applications to ingest and process streaming data sources using Hortonworks Data Flow (HDF) environments. Specific technologies covered include: Apache NiFi, Apache Kafka and Apache Storm. The course will culminate in the creation of a end-to-end exercise that spans this HDF technology stack. PREREQUISITES Students should be familiar with programming principles and have previous experience in software development. First-hand experience with Java programming and developing within an IDE are required. Experience with Linux and a basic understanding of DataFlow tools and would be helpful. No prior Hadoop experience required. TARGET AUDIENCE Developers, Data & Integration Engineers, and Architects who need to automate data flow between systems and/or develop streaming applications. FORMAT 50% Lecture/Discussion 50% Hands-on Labs AGENDA SUMMARY Day 1: Introduction to HDF Components, Apache NiFi dataflow development Day 2: Apache Kafka, NiFi integration with HDF/HDP, Apache Storm architecture Day 3: Storm management options, multi-language support, Kafka integration DAY 1 OBJECTIVES • Introduce HDF’s components; Apache NiFi, Apache Kafka, and Apache Storm • NiFi architecture, features, and characteristics • NiFi user interface; processors and connections in detail • NiFi dataflow assembly • Processor Groups and their elements -
UIMA Asynchronous Scaleout Written and Maintained by the Apache UIMA™ Development Community
UIMA Asynchronous Scaleout Written and maintained by the Apache UIMA™ Development Community Version 2.10.3 Copyright © 2006, 2018 The Apache Software Foundation License and Disclaimer. The ASF licenses this documentation to you under the Apache License, Version 2.0 (the "License"); you may not use this documentation except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, this documentation and its contents are distributed under the License 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. Trademarks. All terms mentioned in the text that are known to be trademarks or service marks have been appropriately capitalized. Use of such terms in this book should not be regarded as affecting the validity of the the trademark or service mark. Publication date March, 2018 Table of Contents 1. Overview - Asynchronous Scaleout ................................................................................. 1 1.1. Terminology ....................................................................................................... 1 1.2. AS versus CPM .................................................................................................. 2 1.3. Design goals for Asynchronous Scaleout ............................................................... 3 1.4. AS Concepts ..................................................................................................... -
Hortonworks Dataflow Getting Started (January 31, 2018)
Hortonworks DataFlow Getting Started (January 31, 2018) docs.cloudera.com Hortonworks DataFlow January 31, 2018 Hortonworks DataFlow: Getting Started Copyright © 2012-2018 Hortonworks, Inc. Some rights reserved. Except where otherwise noted, this document is licensed under Creative Commons Attribution ShareAlike 4.0 License. http://creativecommons.org/licenses/by-sa/4.0/legalcode ii Hortonworks DataFlow January 31, 2018 Table of Contents 1. Getting Started with Apache NiFi ................................................................................ 1 1.1. Getting Started with Apache NiFi ...................................................................... 1 1.1.1. Terminology Used in This Guide ............................................................. 1 1.1.2. Starting NiFi ........................................................................................... 1 1.1.3. I Started NiFi. Now What? ..................................................................... 2 1.1.4. What Processors are Available ................................................................ 7 1.1.5. Working With Attributes ...................................................................... 13 1.1.6. Custom Properties Within Expression Language .................................... 16 1.1.7. Working With Templates ...................................................................... 17 1.1.8. Monitoring NiFi .................................................................................... 18 1.1.9. Data Provenance .................................................................................