Data Analysis With Kibana

  data analysis with kibana: Kibana Essentials Yuvraj Gupta, 2015-11-06 Use the functionalities of Kibana to discover data and build attractive visualizations and dashboards for real-world scenarios About This Book Perform real-time data analytics and visualizations, on streaming data, using Kibana Build beautiful visualizations and dashboards with simplicity and ease without any type of coding involved Learn all the core concepts as well as detailed information about each component used in Kibana Who This Book Is For Whether you are new to the world of data analytics and data visualization or an expert, this book will provide you with the skills required to use Kibana with ease and simplicity for real-time data visualization of streaming data. This book is intended for those professionals who are interested in learning about Kibana,its installations, and how to use it . As Kibana provides a user-friendly web page, no prior experience is required. What You Will Learn Understand the basic concepts of elasticsearch used in Kibana along with step by step guide to install Kibana in Windows and Ubuntu Explore the functionality of all the components used in Kibana in detail, such as the Discover, Visualize, Dashboard,and Settings pages Analyze data using the powerful search capabilities of elasticsearch Understand the different types of aggregations used in Kibana for visualization Create and build different types of amazing visualizations and dashboards easily Create, save, share, embed, and customize the visualizations added to the dashboard Customize and tweak the advanced settings of Kibana to ensure ease of use In Detail With the increasing interest in data analytics and visualization of large data around the globe, Kibana offers the best features to analyze data and create attractive visualizations and dashboards through simple-to-use web pages. The variety of visualizations provided, combined with the powerful underlying elasticsearch capabilities will help professionals improve their skills with this technology. This book will help you quickly familiarize yourself to Kibana and will also help you to understand the core concepts of this technology to build visualizations easily. Starting with setting up of Kibana and elasticsearch in Windows and Ubuntu, you will then use the Discover page to analyse your data intelligently. Next, you will learn to use the Visualization page to create beautiful visualizations without the need for any coding. Then, you will learn how to use the Dashboard page to create a dashboard and instantly share and embed the dashboards. You will see how to tweak the basic and advanced settings provided in Kibana to manage searches, visualizations, and dashboards. Finally, you will use Kibana to build visualizations and dashboards for real-world scenarios. You will quickly master the functionalities and components used in Kibana to create amazing visualizations based on real-world scenarios. With ample screenshots to guide you through every step, this book will assist you in creating beautiful visualizations with ease. Style and approach This book is a comprehensive step-by-step guide to help you understand Kibana. It's explained in an easy-to-follow style along with supporting images. Every chapter is explained sequentially , covering the basics of each component of Kibana and providing detailed explanations of all the functionalities of Kibana that appeal.
  data analysis with kibana: Learning Kibana 7 Anurag Srivastava, 2019-07-19
  data analysis with kibana: Machine Learning with the Elastic Stack Rich Collier, Bahaaldine Azarmi, 2019-01-31 Leverage Elastic Stack’s machine learning features to gain valuable insight from your data Key FeaturesCombine machine learning with the analytic capabilities of Elastic StackAnalyze large volumes of search data and gain actionable insight from themUse external analytical tools with your Elastic Stack to improve its performanceBook Description Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure. By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly. What you will learnInstall the Elastic Stack to use machine learning featuresUnderstand how Elastic machine learning is used to detect a variety of anomaly typesApply effective anomaly detection to IT operations and security analyticsLeverage the output of Elastic machine learning in custom views, dashboards, and proactive alertingCombine your created jobs to correlate anomalies of different layers of infrastructureLearn various tips and tricks to get the most out of Elastic machine learningWho this book is for If you are a data professional eager to gain insight on Elasticsearch data without having to rely on a machine learning specialist or custom development, Machine Learning with the Elastic Stack is for you. Those looking to integrate machine learning within their search and analytics applications will also find this book very useful. Prior experience with the Elastic Stack is needed to get the most out of this book.
  data analysis with kibana: Learning Elastic Stack 7.0 Pranav Shukla, Sharath Kumar M N, 2019-05-31 A beginner's guide to storing, managing, and analyzing data with the updated features of Elastic 7.0 Key FeaturesGain access to new features and updates introduced in Elastic Stack 7.0Grasp the fundamentals of Elastic Stack including Elasticsearch, Logstash, and KibanaExplore useful tips for using Elastic Cloud and deploying Elastic Stack in production environmentsBook Description The Elastic Stack is a powerful combination of tools for techniques such as distributed search, analytics, logging, and visualization of data. Elastic Stack 7.0 encompasses new features and capabilities that will enable you to find unique insights into analytics using these techniques. This book will give you a fundamental understanding of what the stack is all about, and help you use it efficiently to build powerful real-time data processing applications. The first few sections of the book will help you understand how to set up the stack by installing tools, and exploring their basic configurations. You’ll then get up to speed with using Elasticsearch for distributed searching and analytics, Logstash for logging, and Kibana for data visualization. As you work through the book, you will discover the technique of creating custom plugins using Kibana and Beats. This is followed by coverage of the Elastic X-Pack, a useful extension for effective security and monitoring. You’ll also find helpful tips on how to use Elastic Cloud and deploy Elastic Stack in production environments. By the end of this book, you’ll be well versed with the fundamental Elastic Stack functionalities and the role of each component in the stack to solve different data processing problems. What you will learnInstall and configure an Elasticsearch architectureSolve the full-text search problem with ElasticsearchDiscover powerful analytics capabilities through aggregations using ElasticsearchBuild a data pipeline to transfer data from a variety of sources into Elasticsearch for analysisCreate interactive dashboards for effective storytelling with your data using KibanaLearn how to secure, monitor and use Elastic Stack’s alerting and reporting capabilitiesTake applications to an on-premise or cloud-based production environment with Elastic StackWho this book is for This book is for entry-level data professionals, software engineers, e-commerce developers, and full-stack developers who want to learn about Elastic Stack and how the real-time processing and search engine works for business analytics and enterprise search applications. Previous experience with Elastic Stack is not required, however knowledge of data warehousing and database concepts will be helpful.
  data analysis with kibana: Mastering Elastic Stack Yuvraj Gupta, Ravi Kumar Gupta, 2017-02-28 Get the most out of the Elastic Stack for various complex analytics using this comprehensive and practical guide About This Book Your one-stop solution to perform advanced analytics with Elasticsearch, Logstash, and Kibana Learn how to make better sense of your data by searching, analyzing, and logging data in a systematic way This highly practical guide takes you through an advanced implementation on the ELK stack in your enterprise environment Who This Book Is For This book cater to developers using the Elastic stack in their day-to-day work who are familiar with the basics of Elasticsearch, Logstash, and Kibana, and now want to become an expert at using the Elastic stack for data analytics. What You Will Learn Build a pipeline with help of Logstash and Beats to visualize Elasticsearch data in Kibana Use Beats to ship any type of data to the Elastic stack Understand Elasticsearch APIs, modules, and other advanced concepts Explore Logstash and it's plugins Discover how to utilize the new Kibana UI for advanced analytics See how to work with the Elastic Stack using other advanced configurations Customize the Elastic Stack and plugin development for each of the component Work with the Elastic Stack in a production environment Explore the various components of X-Pack in detail. In Detail Even structured data is useless if it can't help you to take strategic decisions and improve existing system. If you love to play with data, or your job requires you to process custom log formats, design a scalable analysis system, and manage logs to do real-time data analysis, this book is your one-stop solution. By combining the massively popular Elasticsearch, Logstash, Beats, and Kibana, elastic.co has advanced the end-to-end stack that delivers actionable insights in real time from almost any type of structured or unstructured data source. If your job requires you to process custom log formats, design a scalable analysis system, explore a variety of data, and manage logs, this book is your one-stop solution. You will learn how to create real-time dashboards and how to manage the life cycle of logs in detail through real-life scenarios. This book brushes up your basic knowledge on implementing the Elastic Stack and then dives deeper into complex and advanced implementations of the Elastic Stack. We'll help you to solve data analytics challenges using the Elastic Stack and provide practical steps on centralized logging and real-time analytics with the Elastic Stack in production. You will get to grip with advanced techniques for log analysis and visualization. Newly announced features such as Beats and X-Pack are also covered in detail with examples. Toward the end, you will see how to use the Elastic stack for real-world case studies and we'll show you some best practices and troubleshooting techniques for the Elastic Stack. Style and approach This practical guide shows you how to perform advanced analytics with the Elastic stack through real-world use cases. It includes common and some not so common scenarios to use the Elastic stack for data analysis.
  data analysis with kibana: Data Analytics and Computational Intelligence: Novel Models, Algorithms and Applications Gilberto Rivera, Laura Cruz-Reyes, Bernabé Dorronsoro, Alejandro Rosete, 2023-10-20 In the age of transformative artificial intelligence (AI), which has the potential to revolutionize our lives, this book provides a comprehensive exploration of successful research and applications in AI and data analytics. Covering innovative approaches, advanced algorithms, and data analysis methodologies, this book addresses complex problems across topics such as machine learning, pattern recognition, data mining, optimization, and predictive modeling. With clear explanations, practical examples, and cutting-edge research, this book seeks to expand the understanding of a wide readership, including students, researchers, practitioners, and technology enthusiasts eager to explore these exciting fields. Featuring real-world applications in education, health care, climate modeling, cybersecurity, smart transportation, conversational systems, and material analysis, among others, this book highlights how these technologies can drive innovation and generate competitive advantages.
  data analysis with kibana: Proceedings of Data Analytics and Management Abhishek Swaroop, Zdzislaw Polkowski, Sérgio Duarte Correia, Bal Virdee, 2024-02-06 This book includes original unpublished contributions presented at the International Conference on Data Analytics and Management (ICDAM 2023), held at London Metropolitan University, London, UK, during June 2023. The book covers the topics in data analytics, data management, big data, computational intelligence, and communication networks. The book presents innovative work by leading academics, researchers, and experts from industry which is useful for young researchers and students. The book is divided into four volumes.
  data analysis with kibana: Learning ELK Stack Saurabh Chhajed, 2015-11-26 Build mesmerizing visualizations, analytics, and logs from your data using Elasticsearch, Logstash, and Kibana About This Book Solve all your data analytics problems with the ELK stack Explore the power of Kibana4 search and visualizations built over Elasticsearch queries and learn about the features and plugins of Logstash Develop a complete data pipeline using the ELK stack Who This Book Is For If you are a developer or DevOps engineer interested in building a system that provides amazing insights and business metrics out of data sources, of various formats and types, using the open source technology stack that ELK provides, then this book is for you. Basic knowledge of Unix or any programming language will be helpful to make the most out of this book. What You Will Learn Install, configure, and run Elasticsearch, Logstash, and Kibana Understand the need for log analytics and the current challenges in log analysis Build your own data pipeline using the ELK stack Familiarize yourself with the key features of Logstash and the variety of input, filter, and output plugins it provides Build your own custom Logstash plugin Create actionable insights using charts, histograms, and quick search features in Kibana4 Understand the role of Elasticsearch in the ELK stack In Detail The ELK stack—Elasticsearch, Logstash, and Kibana, is a powerful combination of open source tools. Elasticsearch is for deep search and data analytics. Logstash is for centralized logging, log enrichment, and parsing. Kibana is for powerful and beautiful data visualizations. In short, the Elasticsearch ELK stack makes searching and analyzing data easier than ever before. This book will introduce you to the ELK (Elasticsearch, Logstash, and Kibana) stack, starting by showing you how to set up the stack by installing the tools, and basic configuration. You'll move on to building a basic data pipeline using the ELK stack. Next, you'll explore the key features of Logstash and its role in the ELK stack, including creating Logstash plugins, which will enable you to use your own customized plugins. The importance of Elasticsearch and Kibana in the ELK stack is also covered, along with various types of advanced data analysis, and a variety of charts, tables ,and maps. Finally, by the end of the book you will be able to develop full-fledged data pipeline using the ELK stack and have a solid understanding of the role of each of the components. Style and approach This book is a step-by-step guide, complete with various examples to solve your data analytics problems by using the ELK stack to explore and visualize data.
  data analysis with kibana: Elasticsearch in Action, Second Edition Madhusudhan Konda, 2023-10-31 Build powerful, production-ready search applications using the incredible features of Elasticsearch. In Elasticsearch in Action, Second Edition you will discover: Architecture, concepts, and fundamentals of Elasticsearch Installing, configuring, and running Elasticsearch and Kibana Creating an index with custom settings Data types, mapping fundamentals, and templates Fundamentals of text analysis and working with text analyzers Indexing, deleting, and updating documents Indexing data in bulk, and reindexing and aliasing operations Learning search concepts, relevancy scores, and similarity algorithms Elasticsearch in Action, Second Edition teaches you to build scalable search applications using Elasticsearch. This completely new edition explores Elasticsearch fundamentals from the ground up. You’ll deep dive into design principles, search architectures, and Elasticsearch’s essential APIs. Every chapter is clearly illustrated with diagrams and hands-on examples. You’ll even explore real-world use cases for full text search, data visualizations, and machine learning. Plus, its comprehensive nature means you’ll keep coming back to the book as a handy reference! Foreword by Shay Banon. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Create fully professional-grade search engines with Elasticsearch and Kibana! Rewritten for the latest version of Elasticsearch, this practical book explores Elasticsearch’s high-level architecture, reveals infrastructure patterns, and walks through the search and analytics capabilities of numerous Elasticsearch APIs. About the book Elasticsearch in Action, Second Edition teaches you how to add modern search features to websites and applications using Elasticsearch 8. In it, you’ll quickly progress from the basics of installation and configuring clusters, to indexing documents, advanced aggregations, and putting your servers into production. You’ll especially appreciate the mix of technical detail with techniques for designing great search experiences. What's inside Understanding search architecture Full text and term-level search queries Analytics and aggregations High-level visualizations in Kibana Configure, scale, and tune clusters About the reader For application developers comfortable with scripting and command-line applications. About the author Madhusudhan Konda is a full-stack lead engineer, architect, mentor, and conference speaker. He delivers live online training on Elasticsearch and the Elastic Stack. Table of Contents 1 Overview 2 Getting started 3 Architecture 4 Mapping 5 Working with documents 6 Indexing operations 7 Text analysis 8 Introducing search 9 Term-level search 10 Full-text searches 11 Compound queries 12 Advanced search 13 Aggregations 14 Administration 15 Performance and troubleshooting
  data analysis with kibana: AWS Certified Data Analytics Study Guide with Online Labs Asif Abbasi, 2021-04-13 Virtual, hands-on learning labs allow you to apply your technical skills in realistic environments. So Sybex has bundled AWS labs from XtremeLabs with our popular AWS Certified Data Analytics Study Guide to give you the same experience working in these labs as you prepare for the Certified Data Analytics Exam that you would face in a real-life application. These labs in addition to the book are a proven way to prepare for the certification and for work as an AWS Data Analyst. AWS Certified Data Analytics Study Guide: Specialty (DAS-C01) Exam is intended for individuals who perform in a data analytics-focused role. This UPDATED exam validates an examinee's comprehensive understanding of using AWS services to design, build, secure, and maintain analytics solutions that provide insight from data. It assesses an examinee's ability to define AWS data analytics services and understand how they integrate with each other; and explain how AWS data analytics services fit in the data lifecycle of collection, storage, processing, and visualization. The book focuses on the following domains: • Collection • Storage and Data Management • Processing • Analysis and Visualization • Data Security This is your opportunity to take the next step in your career by expanding and validating your skills on the AWS cloud. AWS is the frontrunner in cloud computing products and services, and the AWS Certified Data Analytics Study Guide: Specialty exam will get you fully prepared through expert content, and real-world knowledge, key exam essentials, chapter review questions, and much more. Written by an AWS subject-matter expert, this study guide covers exam concepts, and provides key review on exam topics. Readers will also have access to Sybex's superior online interactive learning environment and test bank, including chapter tests, practice exams, a glossary of key terms, and electronic flashcards. And included with this version of the book, XtremeLabs virtual labs that run from your browser. The registration code is included with the book and gives you 6 months of unlimited access to XtremeLabs AWS Certified Data Analytics Labs with 3 unique lab modules based on the book.
  data analysis with kibana: Big Data Analytics Ümit Demirbaga,
  data analysis with kibana: Elasticsearch: The Definitive Guide Clinton Gormley, Zachary Tong, 2015-01-23 Whether you need full-text search or real-time analytics of structured data—or both—the Elasticsearch distributed search engine is an ideal way to put your data to work. This practical guide not only shows you how to search, analyze, and explore data with Elasticsearch, but also helps you deal with the complexities of human language, geolocation, and relationships. If you’re a newcomer to both search and distributed systems, you’ll quickly learn how to integrate Elasticsearch into your application. More experienced users will pick up lots of advanced techniques. Throughout the book, you’ll follow a problem-based approach to learn why, when, and how to use Elasticsearch features. Understand how Elasticsearch interprets data in your documents Index and query your data to take advantage of search concepts such as relevance and word proximity Handle human language through the effective use of analyzers and queries Summarize and group data to show overall trends, with aggregations and analytics Use geo-points and geo-shapes—Elasticsearch’s approaches to geolocation Model your data to take advantage of Elasticsearch’s horizontal scalability Learn how to configure and monitor your cluster in production
  data analysis with kibana: Kibana 7 Quick Start Guide Anurag Srivastava, 2019-01-31 A quick start guide to visualize your Elasticsearch data Key Features Your hands-on guide to visualizing the Elasticsearch data as well as navigating the Elastic stack Work with different Kibana plugins and create effective machine learning jobs using Kibana Build effective dashboards and reports without any hassle Book Description The Elastic Stack is growing rapidly and, day by day, additional tools are being added to make it more effective. This book endeavors to explain all the important aspects of Kibana, which is essential for utilizing its full potential. This book covers the core concepts of Kibana, with chapters set out in a coherent manner so that readers can advance their learning in a step-by-step manner. The focus is on a practical approach, thereby enabling the reader to apply those examples in real time for a better understanding of the concepts and to provide them with the correct skills in relation to the tool. With its succinct explanations, it is quite easy for a reader to use this book as a reference guide for learning basic to advanced implementations of Kibana. The practical examples, such as the creation of Kibana dashboards from CSV data, application RDBMS data, system metrics data, log file data, APM agents, and search results, can provide readers with a number of different drop-off points from where they can fetch any type of data into Kibana for the purpose of analysis or dashboarding. What you will learn Explore how Logstash is configured to fetch CSV data Understand how to create index patterns in Kibana Become familiar with how to apply filters on data Discover how to create ML jobs Explore how to analyze APM data from APM agents Get to grips with how to save, share, inspect, and edit visualizations Understand how to find an anomaly in data Who this book is for Kibana 7 Quick Start Guide is for developers new to Kibana who want to learn the fundamentals of using the tool for visualization, as well as existing Elastic developers.
  data analysis with kibana: Smart Log Data Analytics Florian Skopik, Markus Wurzenberger, Max Landauer, 2021-08-28 This book provides insights into smart ways of computer log data analysis, with the goal of spotting adversarial actions. It is organized into 3 major parts with a total of 8 chapters that include a detailed view on existing solutions, as well as novel techniques that go far beyond state of the art. The first part of this book motivates the entire topic and highlights major challenges, trends and design criteria for log data analysis approaches, and further surveys and compares the state of the art. The second part of this book introduces concepts that apply character-based, rather than token-based, approaches and thus work on a more fine-grained level. Furthermore, these solutions were designed for “online use”, not only forensic analysis, but also process new log lines as they arrive in an efficient single pass manner. An advanced method for time series analysis aims at detecting changes in the overall behavior profile of an observed system and spotting trends and periodicities through log analysis. The third part of this book introduces the design of the AMiner, which is an advanced open source component for log data anomaly mining. The AMiner comes with several detectors to spot new events, new parameters, new correlations, new values and unknown value combinations and can run as stand-alone solution or as sensor with connection to a SIEM solution. More advanced detectors help to determines the characteristics of variable parts of log lines, specifically the properties of numerical and categorical fields. Detailed examples throughout this book allow the reader to better understand and apply the introduced techniques with open source software. Step-by-step instructions help to get familiar with the concepts and to better comprehend their inner mechanisms. A log test data set is available as free download and enables the reader to get the system up and running in no time. This book is designed for researchers working in the field of cyber security, and specifically system monitoring, anomaly detection and intrusion detection. The content of this book will be particularly useful for advanced-level students studying computer science, computer technology, and information systems. Forward-thinking practitioners, who would benefit from becoming familiar with the advanced anomaly detection methods, will also be interested in this book.
  data analysis with kibana: Learning Elasticsearch 7.x Anurag Srivastava, 2020-12-09 A step-by-step guide that will teach you how to use Elasticsearch in your application effectively Ê KEY FEATURESÊÊÊ _Ê Get familiar with the core concepts of Elasticsearch. _Ê Understand how the search engine works and how Elasticsearch is different from other similar tools. _Ê Learn to install Elasticsearch on different operating systems. _Ê Get familiar with the components of Elastic Stack such as Kibana, Logstash, and Beats, etc. _Ê Learn how to import data from different sources such as RDBMS, and files, etc DESCRIPTIONÊ In the modern Information Technology age, we are flooded with loads of data so we should know how to handle those data and transform them to fetch meaningful information. This book is here to help you manage the data using Elasticsearch. The book starts by covering the fundamentals of Elasticsearch and the concept behind it. After the introduction, you will learn how to install Elasticsearch on different platforms. You will then get to know about Index Management where you will learn to create, update, and delete Elasticsearch indices. Then you will understand how the Query DSL works and how to write some complex search queries using the Query DSL. After completing these basic features, you will move to some advanced topics. Under advanced topics, you will learn to handle Geodata which can be used to plot the data on a map. The book then focuses on Data Analysis using Aggregation.Ê You will then learn how to tune Elasticsearch performance. The book ends with a chapter on Elasticsearch administration. Ê WHAT YOU WILL LEARN Ê_Ê Learn how to create and manage a cluster _Ê Work with different components of Elastic Stack _Ê Review the list of top Information Security certifications. _Ê Get to know more about Elasticsearch Index Management. _Ê Understand how to improve the performance by tuning Elasticsearch Ê ÊWHO THIS BOOK IS FORÊ This book is for developers, architects, DBA, DevOps, and other readers who want to learn Elasticsearch efficiently and want to apply that in their application whether it is a new one or an existing one. It is also beneficial to those who want to play with their data using Elasticsearch. Basic computer programming is a prerequisite. Ê TABLE OF CONTENTS 1 Getting started with Elasticsearch 2 Installation Elasticsearch 3 Working with Elastic Stack 4 Preparing your data 5 Importing Data into Elasticsearch 6 Managing Your Index 7 Apply Search on Your Data 8 Handling Geo with Elasticsearch 9 Aggregating Your Data 10 Improving the Performance 11 Administer Elasticsearch
  data analysis with kibana: Development Methodologies for Big Data Analytics Systems Manuel Mora, Fen Wang, Jorge Marx Gomez, Hector Duran-Limon, 2023-11-03 This book presents research in big data analytics (BDA) for business of all sizes. The authors analyze problems presented in the application of BDA in some businesses through the study of development methodologies based on the three approaches – 1) plan-driven, 2) agile and 3) hybrid lightweight. The authors first describe BDA systems and how they emerged with the convergence of Statistics, Computer Science, and Business Intelligent Analytics with the practical aim to provide concepts, models, methods and tools required for exploiting the wide variety, volume, and velocity of available business internal and external data - i.e. Big Data – and provide decision-making value to decision-makers. The book presents high-quality conceptual and empirical research-oriented chapters on plan-driven, agile, and hybrid lightweight development methodologies and relevant supporting topics for BDA systems suitable to be used for large-, medium-, and small-sized business organizations.
  data analysis with kibana: Kibana 8.x – A Quick Start Guide to Data Analysis Krishna Shah, 2024-02-29 Uncover valuable business insights by leveraging the power of Kibana to navigate and interpret datasets for improved decision making Key Features Gain profound understanding of the end-to-end workings of Kibana Explore the powerful administration features in Kibana 8.x for managing and supporting data ingestion pipelines Build your own analytics and visualization solution from scratch Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionUnleash the full potential of Kibana—an indispensable tool for data analysts to seamlessly explore vast datasets, uncover key insights, identify trends and anomalies, and share results. This book guides you through its user-friendly interface, interactive visualizations, and robust features, including real-time data monitoring and advanced analytics, showing you how Kibana revolutionizes your approach to navigating and analyzing complex datasets. Starting with the foundational steps of installing, configuring, and running Kibana, this book progresses systematically to explain the search and data visualization capabilities for data stored in the Elasticsearch cluster. You’ll then delve into the practical details of creating data views and optimizing spaces to better organize the analysis environment. As you advance, you'll get to grips with using the discover interface and learn how to build different types of extensive visualizations using Lens. By the end of this book, you’ll have a complete understanding of how Kibana works, helping you leverage its capabilities to build an analytics and visualization solution from scratch for your data-driven use case.What you will learn Create visualizations using the Visualize interface in Kibana Build shareable search dashboards to drill down and perform advanced analysis and reporting Search data to make correlations and identify and explain trends Embed dashboards, share links, and export PNG, PDF, or CSV files and send as an attachment Configure and tweak advanced settings to best manage saved objects in Kibana Implement several types of aggregations working behind the scenes of extensive visualizations Who this book is for If you’re a data analyst or a data engineer, this book is for you. It’s also a useful resource to database administrators, analysts, and business users looking to build a foundation in creating intuitive dashboards using Kibana 8.x and data analysis techniques for improved decision making. Foundational knowledge of Elasticsearch fundamentals will provide an added advantage.
  data analysis with kibana: Threat Hunting with Elastic Stack Andrew Pease, 2021-07-23 Learn advanced threat analysis techniques in practice by implementing Elastic Stack security features Key FeaturesGet started with Elastic Security configuration and featuresLeverage Elastic Stack features to provide optimal protection against threatsDiscover tips, tricks, and best practices to enhance the security of your environmentBook Description Threat Hunting with Elastic Stack will show you how to make the best use of Elastic Security to provide optimal protection against cyber threats. With this book, security practitioners working with Kibana will be able to put their knowledge to work and detect malicious adversary activity within their contested network. You'll take a hands-on approach to learning the implementation and methodologies that will have you up and running in no time. Starting with the foundational parts of the Elastic Stack, you'll explore analytical models and how they support security response and finally leverage Elastic technology to perform defensive cyber operations. You'll then cover threat intelligence analytical models, threat hunting concepts and methodologies, and how to leverage them in cyber operations. After you've mastered the basics, you'll apply the knowledge you've gained to build and configure your own Elastic Stack, upload data, and explore that data directly as well as by using the built-in tools in the Kibana app to hunt for nefarious activities. By the end of this book, you'll be able to build an Elastic Stack for self-training or to monitor your own network and/or assets and use Kibana to monitor and hunt for adversaries within your network. What you will learnExplore cyber threat intelligence analytical models and hunting methodologiesBuild and configure Elastic Stack for cyber threat huntingLeverage the Elastic endpoint and Beats for data collectionPerform security data analysis using the Kibana Discover, Visualize, and Dashboard appsExecute hunting and response operations using the Kibana Security appUse Elastic Common Schema to ensure data uniformity across organizationsWho this book is for Security analysts, cybersecurity enthusiasts, information systems security staff, or anyone who works with the Elastic Stack for security monitoring, incident response, intelligence analysis, or threat hunting will find this book useful. Basic working knowledge of IT security operations and network and endpoint systems is necessary to get started.
  data analysis with kibana: Data Analytics for Intelligent Transportation Systems Mashrur Chowdhury, Kakan Dey, Amy Apon, 2024-11-02 Data Analytics for Intelligent Transportation Systems provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems (ITS), including the tools needed to implement these methods using big data analytics and other computing techniques. The book examines the major characteristics of connected transportation systems, along with the fundamental concepts of how to analyze the data they produce. It explores collecting, archiving, processing, and distributing the data, designing data infrastructures, data management and delivery systems, and the required hardware and software technologies. It presents extensive coverage of existing and forthcoming intelligent transportation systems and data analytics technologies. All fundamentals/concepts presented in this book are explained in the context of ITS. Users will learn everything from the basics of different ITS data types and characteristics to how to evaluate alternative data analytics for different ITS applications. They will discover how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications, along with key safety and environmental applications for both commercial and passenger vehicles, data privacy and security issues, and the role of social media data in traffic planning. Data Analytics for Intelligent Transportation Systems will prepare an educated ITS workforce and tool builders to make the vision for safe, reliable, and environmentally sustainable intelligent transportation systems a reality. It serves as a primary or supplemental textbook for upper-level undergraduate and graduate ITS courses and a valuable reference for ITS practitioners. - Utilizes real ITS examples to facilitate a quicker grasp of materials presented - Contains contributors from both leading academic and commercial domains - Explains how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications - Includes exercise problems in each chapter to help readers apply and master the learned fundamentals, concepts, and techniques - New to the second edition: Two new chapters on Quantum Computing in Data Analytics and Society and Environment in ITS Data Analytics
  data analysis with kibana: Elasticsearch 7 Quick Start Guide Anurag Srivastava, Douglas Miller, 2019-10-24 Get the most out of Elasticsearch 7’s new features to build, deploy, and manage efficient applications Key FeaturesDiscover the new features introduced in Elasticsearch 7Explore techniques for distributed search, indexing, and clusteringGain hands-on knowledge of implementing Elasticsearch for your enterpriseBook Description Elasticsearch is one of the most popular tools for distributed search and analytics. This Elasticsearch book highlights the latest features of Elasticsearch 7 and helps you understand how you can use them to build your own search applications with ease. Starting with an introduction to the Elastic Stack, this book will help you quickly get up to speed with using Elasticsearch. You'll learn how to install, configure, manage, secure, and deploy Elasticsearch clusters, as well as how to use your deployment to develop powerful search and analytics solutions. As you progress, you'll also understand how to troubleshoot any issues that you may encounter along the way. Finally, the book will help you explore the inner workings of Elasticsearch and gain insights into queries, analyzers, mappings, and aggregations as you learn to work with search results. By the end of this book, you'll have a basic understanding of how to build and deploy effective search and analytics solutions using Elasticsearch. What you will learnInstall Elasticsearch and use it to safely store data and retrieve it when neededWork with a variety of analyzers and filtersDiscover techniques to improve search results in ElasticsearchUnderstand how to perform metric and bucket aggregationsImplement best practices for moving clusters and applications to productionExplore various techniques to secure your Elasticsearch clustersWho this book is for This book is for software developers, engineers, data architects, system administrators, and anyone who wants to get up and running with Elasticsearch 7. No prior experience with Elasticsearch is required.
  data analysis with kibana: Big Data on Kubernetes Neylson Crepalde, 2024-07-19 Gain hands-on experience in building efficient and scalable big data architecture on Kubernetes, utilizing leading technologies such as Spark, Airflow, Kafka, and Trino Key Features Leverage Kubernetes in a cloud environment to integrate seamlessly with a variety of tools Explore best practices for optimizing the performance of big data pipelines Build end-to-end data pipelines and discover real-world use cases using popular tools like Spark, Airflow, and Kafka Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn today's data-driven world, organizations across different sectors need scalable and efficient solutions for processing large volumes of data. Kubernetes offers an open-source and cost-effective platform for deploying and managing big data tools and workloads, ensuring optimal resource utilization and minimizing operational overhead. If you want to master the art of building and deploying big data solutions using Kubernetes, then this book is for you. Written by an experienced data specialist, Big Data on Kubernetes takes you through the entire process of developing scalable and resilient data pipelines, with a focus on practical implementation. Starting with the basics, you’ll progress toward learning how to install Docker and run your first containerized applications. You’ll then explore Kubernetes architecture and understand its core components. This knowledge will pave the way for exploring a variety of essential tools for big data processing such as Apache Spark and Apache Airflow. You’ll also learn how to install and configure these tools on Kubernetes clusters. Throughout the book, you’ll gain hands-on experience building a complete big data stack on Kubernetes. By the end of this Kubernetes book, you’ll be equipped with the skills and knowledge you need to tackle real-world big data challenges with confidence.What you will learn Install and use Docker to run containers and build concise images Gain a deep understanding of Kubernetes architecture and its components Deploy and manage Kubernetes clusters on different cloud platforms Implement and manage data pipelines using Apache Spark and Apache Airflow Deploy and configure Apache Kafka for real-time data ingestion and processing Build and orchestrate a complete big data pipeline using open-source tools Deploy Generative AI applications on a Kubernetes-based architecture Who this book is for If you’re a data engineer, BI analyst, data team leader, data architect, or tech manager with a basic understanding of big data technologies, then this big data book is for you. Familiarity with the basics of Python programming, SQL queries, and YAML is required to understand the topics discussed in this book.
  data analysis with kibana: Deep Learning and Parallel Computing Environment for Bioengineering Systems Arun Kumar Sangaiah, 2019-07-26 Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations' needs as well as practitioners' innovative ideas. - Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems - Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems - Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data
  data analysis with kibana: Elastic Stack 8.x Cookbook Huage Chen, Yazid Akadiri, 2024-06-28 Unlock the full potential of Elastic Stack for search, analytics, security, and observability and manage substantial data workloads in both on-premise and cloud environments Key Features Explore the diverse capabilities of the Elastic Stack through a comprehensive set of recipes Build search applications, analyze your data, and observe cloud-native applications Harness powerful machine learning and AI features to create data science and search applications Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionLearn how to make the most of the Elastic Stack (ELK Stack) products—including Elasticsearch, Kibana, Elastic Agent, and Logstash—to take data reliably and securely from any source, in any format, and then search, analyze, and visualize it in real-time. This cookbook takes a practical approach to unlocking the full potential of Elastic Stack through detailed recipes step by step. Starting with installing and ingesting data using Elastic Agent and Beats, this book guides you through data transformation and enrichment with various Elastic components and explores the latest advancements in search applications, including semantic search and Generative AI. You'll then visualize and explore your data and create dashboards using Kibana. As you progress, you'll advance your skills with machine learning for data science, get to grips with natural language processing, and discover the power of vector search. The book covers Elastic Observability use cases for log, infrastructure, and synthetics monitoring, along with essential strategies for securing the Elastic Stack. Finally, you'll gain expertise in Elastic Stack operations to effectively monitor and manage your system.What you will learn Discover techniques for collecting data from diverse sources Visualize data and create dashboards using Kibana to extract business insights Explore machine learning, vector search, and AI capabilities of Elastic Stack Handle data transformation and data formatting Build search solutions from the ingested data Leverage data science tools for in-depth data exploration Monitor and manage your system with Elastic Stack Who this book is for This book is for Elastic Stack users, developers, observability practitioners, and data professionals ranging from beginner to expert level. If you’re a developer, you’ll benefit from the easy-to-follow recipes for using APIs and features to build powerful applications, and if you’re an observability practitioner, this book will help you with use cases covering APM, Kubernetes, and cloud monitoring. For data engineers and AI enthusiasts, the book covers dedicated recipes on vector search and machine learning. No prior knowledge of the Elastic Stack is required.
  data analysis with kibana: Advanced Elasticsearch 7.0 Wai Tak Wong, 2019-08-23 Master the intricacies of Elasticsearch 7.0 and use it to create flexible and scalable search solutions Key FeaturesMaster the latest distributed search and analytics capabilities of Elasticsearch 7.0Perform searching, indexing, and aggregation of your data at scaleDiscover tips and techniques for speeding up your search query performanceBook Description Building enterprise-grade distributed applications and executing systematic search operations call for a strong understanding of Elasticsearch and expertise in using its core APIs and latest features. This book will help you master the advanced functionalities of Elasticsearch and understand how you can develop a sophisticated, real-time search engine confidently. In addition to this, you'll also learn to run machine learning jobs in Elasticsearch to speed up routine tasks. You'll get started by learning to use Elasticsearch features on Hadoop and Spark and make search results faster, thereby improving the speed of query results and enhancing the customer experience. You'll then get up to speed with performing analytics by building a metrics pipeline, defining queries, and using Kibana for intuitive visualizations that help provide decision-makers with better insights. The book will later guide you through using Logstash with examples to collect, parse, and enrich logs before indexing them in Elasticsearch. By the end of this book, you will have comprehensive knowledge of advanced topics such as Apache Spark support, machine learning using Elasticsearch and scikit-learn, and real-time analytics, along with the expertise you need to increase business productivity, perform analytics, and get the very best out of Elasticsearch. What you will learnPre-process documents before indexing in ingest pipelinesLearn how to model your data in the real worldGet to grips with using Elasticsearch for exploratory data analysisUnderstand how to build analytics and RESTful servicesUse Kibana, Logstash, and Beats for dashboard applicationsGet up to speed with Spark and Elasticsearch for real-time analyticsExplore the basics of Spring Data Elasticsearch, and understand how to index, search, and query in a Spring applicationWho this book is for This book is for Elasticsearch developers and data engineers who want to take their basic knowledge of Elasticsearch to the next level and use it to build enterprise-grade distributed search applications. Prior experience of working with Elasticsearch will be useful to get the most out of this book.
  data analysis with kibana: Python for DevOps Noah Gift, Kennedy Behrman, Alfredo Deza, Grig Gheorghiu, 2019-12-12 Much has changed in technology over the past decade. Data is hot, the cloud is ubiquitous, and many organizations need some form of automation. Throughout these transformations, Python has become one of the most popular languages in the world. This practical resource shows you how to use Python for everyday Linux systems administration tasks with today’s most useful DevOps tools, including Docker, Kubernetes, and Terraform. Learning how to interact and automate with Linux is essential for millions of professionals. Python makes it much easier. With this book, you’ll learn how to develop software and solve problems using containers, as well as how to monitor, instrument, load-test, and operationalize your software. Looking for effective ways to get stuff done in Python? This is your guide. Python foundations, including a brief introduction to the language How to automate text, write command-line tools, and automate the filesystem Linux utilities, package management, build systems, monitoring and instrumentation, and automated testing Cloud computing, infrastructure as code, Kubernetes, and serverless Machine learning operations and data engineering from a DevOps perspective Building, deploying, and operationalizing a machine learning project
  data analysis with kibana: ⬆️ Amazon Web Services Certified (AWS Certified) Data Analytics Specialty (DAS-C01) Practice Tests Exams 83 Questions & Answers PDF Daniel Danielecki, 2023-11-01 ⌛️ Short and to the point; why should you buy the PDF with these Practice Tests Exams: 1. Always happy to answer your questions on Google Play Books and outside :) 2. Failed? Please submit a screenshot of your exam result and request a refund; we'll always accept it. 3. Learn about topics, such as: - Active Directory; - Amazon Athena; - Amazon Aurora; - Amazon CloudWatch; - Amazon DynamoDB; - Amazon Elastic Compute Cloud (Amazon EC2); - Amazon Elastic Map Reduce (Amazon EMR); - Apache Kafka; - Amazon Kinesis; - Amazon OpenSearch Service; - Amazon QuickSight; - Amazon Redshift; - Amazon Relational Database Service (Amazon RDS); - Amazon Simple Storage Service (Amazon S3); - Apache Spark; - AWS CloudFormation; - AWS Command Line Interface (AWS CLI); - AWS Glue; - AWS Identity and Access Management (AWS IAM); - AWS Key Management Service (AWS KMS); - AWS Lambda; - Extract, Transform, Load (ETL); - Hadoop Distributed File System (HDFS); - Input/Output operations Per Second (IOPS); - Virtual Private Clouds (VPC); - Much More! 4. Questions are similar to the actual exam, without duplications (like in other courses ;-)). 5. These tests are not an Amazon Web Services Certified (AWS Certified) Data Analytics Specialty (DAS-C01) Exam Dump. Some people use brain dumps or exam dumps, but that's absurd, which we don't practice. 6. 83 unique questions.
  data analysis with kibana: Mastering Elasticsearch Saravanan Kuppusamy, 2024-06-05 Welcome to Mastering Elasticsearch: A Comprehensive Guide. If you're reading this book, it's because you've recognized Elasticsearch's immense potential and are eager to utilize its power for your projects and organization. This guide is designed for data engineers, developers, architects, and anyone seeking to navigate the intricacies of Elasticsearch, empowering you to extract valuable insights from data efficiently. Mastering Elasticsearch serves as your definitive guide to unlocking the full potential of this powerful search engine, known for its versatility in managing modern data. Whether you're a developer, data engineer, or system architect, this book provides the skills to leverage Elasticsearch’s capabilities, giving you a critical edge in search and data analytics. Why Elasticsearch? In today's digital landscape, the sheer volume of data generated every second is staggering. We face the challenge of searching, analyzing, and making sense of this data to deliver actionable insights. Elasticsearch, a cornerstone of the ELK (Elasticsearch, Logstash, Kibana) stack, has emerged as a leading search and analytics engine, renowned for its speed, scalability, and flexibility. It powers systems from full-text search to complex, real-time analytics, handling massive datasets and providing mission-critical support to global organizations. This book takes you on a journey through the vast capabilities of Elasticsearch, from foundational concepts to advanced implementations. Whether you're setting up your first cluster or looking to fine-tune existing deployments, this guide will offer insights tailored to your needs. Foundational Understanding: We'll begin with a robust introduction to Elasticsearch's architecture, terminology, and basic operations. You'll understand how Elasticsearch indexes, searches, and maps data to provide rapid search results. Cluster Architecture: Gain a thorough understanding of Elasticsearch’s distributed architecture, from nodes and shards to clusters, and how these elements work together for horizontal scaling. Indexing Techniques: Learn about creating, managing, and optimizing indices, the cornerstone of Elasticsearch data storage, for efficient search operations. Intermediate Techniques: Building on this foundation, we'll delve into more advanced features such as aggregations, data visualization, and effective index management. We'll discuss geo queries, nested data structures, and how to optimize queries to handle complex data types. Advanced Topics: In the final section, you'll encounter specialized topics like performance tuning, scaling Elasticsearch clusters, and developing custom plugins. We'll explore practical strategies for enhancing security, setting up monitoring, and employing machine learning features to identify patterns and trends in your data. Advanced Querying and Aggregation: Query DSL: Master Elasticsearch’s Query Domain-Specific Language, enabling you to construct sophisticated queries that handle nuanced search requirements with precision. Aggregations: Dive deep into aggregation frameworks that provide powerful tools for real-time analytics, including complex aggregations like nested, scripted, and pipeline. Data Ingestion and Integration: Ingestion Pipelines: Explore ways to seamlessly ingest and transform data with Elasticsearch’s ingest nodes and processors. External Integrations: Implement data ingestion strategies using Logstash, Beats, and other ETL solutions to connect with various data sources. Indexing Strategy: Optimize indexing through sharding, replication, and customized mapping. Caching and Memory: Leverage caching mechanisms and JVM tuning to reduce latency and boost throughput. Security Practices: Implement robust security through authentication, authorization, and encryption to safeguard sensitive data. Monitoring and Troubleshooting: Use Kibana and other tools for real-time monitoring and diagnostics, ensuring high availability and minimizing downtime. Case Studies: Examine case studies that showcase Elasticsearch’s versatility, from e-commerce search solutions to log analytics and beyond. This book aims to cater to both newcomers and seasoned Elasticsearch users. If you're starting out, we'll guide you through initial setup and offer step-by-step instructions to implement core features. Experienced users will find fresh insights, best practices, and advanced techniques to elevate their Elasticsearch knowledge. The book is structured to offer a comprehensive understanding of Elasticsearch while maintaining accessibility. Each chapter provides practical examples, code snippets, and exercises that reinforce key concepts. By working through the examples, you'll gain the confidence to tackle real-world Elasticsearch projects, whether for search, analytics, or application logging. I wrote this guide with the intention of creating a one-stop resource for all things Elasticsearch. With constant evolution in the software and big data landscape, it's essential to stay updated with the latest practices and developments. This guide aims to cover both tried-and-tested fundamentals and emerging trends to ensure you're well-prepared for the challenges ahead. Finally, thank you for choosing this book. I'm thrilled to share my knowledge and insights with you as you begin your journey toward Mastering the Elasticsearch. Let's work together to fully unlock this incredible technology, enabling us to build faster, smarter, and more efficient applications. By the end of Mastering Elasticsearch, you'll have the expertise needed to design, implement, and manage scalable and secure search applications. You'll gain both theoretical understanding and practical insights, enabling you to tailor Elasticsearch to your organization's unique data management needs.
  data analysis with kibana: Securing Networks with ELK Stack Ram Patel, 2024-06-19 Strengthening networks, redefining security: ELK Stack leading the charge KEY FEATURES ● This book provides a thorough examination of zero trust network architecture, ELK Stack, and Elastic Security, encompassing foundational principles and practical deployment strategies. ● Readers gain practical insights into building resilient zero trust networks, leveraging ELK Stack's capabilities for data gathering, visualization, and advanced analytics. ● Through real-world case studies and examples, the book illustrates how to integrate Zeek and Elastic Security effectively. DESCRIPTION Step into the dynamic world of zero trust network architecture with this comprehensive handbook. Starting with an exploration of zero trust principles, each chapter unveils new insights and practical strategies. From crafting strategic blueprints to implementing hands-on deployment tactics, discover the intricacies of building a resilient zero trust network capable of thwarting modern threats. Journey through the extensive capabilities of ELK Stack, essential for fortifying a zero trust paradigm. Learn the nuances of data acquisition strategies and efficient ingestion methods with ELK, enabling robust data visualization and dashboard creation using Kibana. Explore advanced functionalities like Machine Learning driven anomaly detection to enhance your defenses against emerging threats. Explore Elastic Security's suite, encompassing threat detection, incident response, and compliance reporting, crucial elements in strengthening network defenses. Utilize the transformative potential of Zeek in network security, from foundational principles to advanced integration with Elastic Security. Real-world case studies showcase the synergy between Zeek and Elastic Security, providing insights into future-proof network protection strategies. Arm yourself with the knowledge and tools necessary to navigate the evolving landscape of network security. Traverse the realms of zero trust architecture, ELK Stack, and Elastic Security, empowered by practical insights and real-world applications. WHAT YOU WILL LEARN ● Understanding the core principles and intricacies of zero trust network architecture. ● Designing and deploying a robust zero trust network using strategic methodologies. ● Leveraging ELK Stack's capabilities to support and enhance a zero trust approach. ● Implementing effective data gathering and ingestion strategies with ELK. ● Mastering data visualization and dashboard creation using Kibana for actionable insights. WHO THIS BOOK IS FOR The book is primarily aimed at security professionals, network architects, and IT managers who are responsible for securing their organization's network infrastructure and sensitive data. The book is suitable for both technical and non-technical readers. TABLE OF CONTENTS 1. Introduction to Zero Trust Network Architecture 2. Zero Trust Network Architecture: Design and Deployment Strategies 3. Zero Trust Network Architecture: Data Gathering Strategies 4. Overview of ELK Stack and its Capabilities 5. Design of ELK Stack Components 6. Data Ingestion with ELK 7. Data Visualization with ELK 8. Effective Dashboards with Kibana 9. Unlocking Insights: ELKʼs Machine Learning Capabilities 10. Introduction to Elastic Security 11. Threat Detection and Prevention 12. Incident Response and Investigation 13. Compliance and Reporting 14. Introduction to Zeek 15. Zeek Data Collection and Analysis 16. Unlocking Synergies: Zeek and Elastic Security Integration in Action 17. Future Directions for Elastic Security 18. A Unified Recap: Safeguarding Networks with ELK
  data analysis with kibana: Advanced Mastery of Elasticsearch: Innovative Search Solutions Explored Peter Jones, 2024-10-17 Unlock the full potential of Elasticsearch with our definitive guide, Advanced Mastery of Elasticsearch: Innovative Search Solutions Explored. This comprehensive book is crafted for professionals aspiring to enhance their skills in developing robust, scalable search and analytics solutions. Whether you're a software developer, data analyst, system administrator, or IT professional, this resource covers everything from setup, configuration, and cluster management to advanced querying, data indexing, and security. Delve deep into the core concepts of Elasticsearch architecture, uncover the intricacies of Query DSL, and master text analysis with analyzers, tokenizers, and filters. Discover best practices for managing large datasets, optimizing performance, and ensuring your deployments are secure and efficient. Each chapter is meticulously organized to build on your knowledge, offering detailed insights and practical examples to address real-world challenges. Advanced Mastery of Elasticsearch: Innovative Search Solutions Explored is more than a book; it's an indispensable resource guiding you through the creation of cutting-edge search and analytics implementations. Elevate your Elasticsearch expertise and revolutionize how you handle data in your organization.
  data analysis with kibana: Proceedings of the International Conference on Data Engineering and Communication Technology Suresh Chandra Satapathy, Vikrant Bhateja, Amit Joshi, 2016-08-23 This two-volume book contains research work presented at the First International Conference on Data Engineering and Communication Technology (ICDECT) held during March 10–11, 2016 at Lavasa, Pune, Maharashtra, India. The book discusses recent research technologies and applications in the field of Computer Science, Electrical and Electronics Engineering. The aim of the Proceedings is to provide cutting-edge developments taking place in the field data engineering and communication technologies which will assist the researchers and practitioners from both academia as well as industry to advance their field of study.
  data analysis with kibana: Software-Defined Networking and Security Dijiang Huang, Ankur Chowdhary, Sandeep Pisharody, 2018-12-07 Discusses virtual network security concepts Considers proactive security using moving target defense Reviews attack representation models based on attack graphs and attack trees Examines service function chaining in virtual networks with security considerations Recognizes machine learning and AI in network security
  data analysis with kibana: Big Data Analytics for Cyber-Physical Systems Guido Dartmann, Houbing Herbert Song, Anke Schmeink, 2019-07-16 Approx.374 pages
  data analysis with kibana: Modern Artificial Intelligence and Data Science 2024 Abdellah Idrissi,
  data analysis with kibana: Web Information Systems and Applications Xiang Zhao, Shiyu Yang, Xin Wang, Jianxin Li, 2022-12-07 This book constitutes the proceedings of the 19th International Conference on Web Information Systems and Applications, WISA 2022, held in Dalian, China, in September 2022. The 45 full papers and 19 short papers presented were carefully reviewed and selected from 212 submissions. The papers are grouped in topical sections on knowledge graph, natural language processing, world wide web, machine learning, query processing and algorithm, recommendation, data privacy and security, and blockchain.
  data analysis with kibana: Scalable Data Architecture with Java Sinchan Banerjee, 2022-09-30 Orchestrate data architecting solutions using Java and related technologies to evaluate, recommend and present the most suitable solution to leadership and clients Key FeaturesLearn how to adapt to the ever-evolving data architecture technology landscapeUnderstand how to choose the best suited technology, platform, and architecture to realize effective business valueImplement effective data security and governance principlesBook Description Java architectural patterns and tools help architects to build reliable, scalable, and secure data engineering solutions that collect, manipulate, and publish data. This book will help you make the most of the architecting data solutions available with clear and actionable advice from an expert. You'll start with an overview of data architecture, exploring responsibilities of a Java data architect, and learning about various data formats, data storage, databases, and data application platforms as well as how to choose them. Next, you'll understand how to architect a batch and real-time data processing pipeline. You'll also get to grips with the various Java data processing patterns, before progressing to data security and governance. The later chapters will show you how to publish Data as a Service and how you can architect it. Finally, you'll focus on how to evaluate and recommend an architecture by developing performance benchmarks, estimations, and various decision metrics. By the end of this book, you'll be able to successfully orchestrate data architecture solutions using Java and related technologies as well as to evaluate and present the most suitable solution to your clients. What you will learnAnalyze and use the best data architecture patterns for problemsUnderstand when and how to choose Java tools for a data architectureBuild batch and real-time data engineering solutions using JavaDiscover how to apply security and governance to a solutionMeasure performance, publish benchmarks, and optimize solutionsEvaluate, choose, and present the best architectural alternativesUnderstand how to publish Data as a Service using GraphQL and a REST APIWho this book is for Data architects, aspiring data architects, Java developers and anyone who wants to develop or optimize scalable data architecture solutions using Java will find this book useful. A basic understanding of data architecture and Java programming is required to get the best from this book.
  data analysis with kibana: Mastering Python Networking Eric Chou, Michael Kennedy, Mandy Whaley, 2020-01-30 New edition of the bestselling guide to mastering Python Networking, updated to Python 3 and including the latest on network data analysis, Cloud Networking, Ansible 2.8, and new libraries Key FeaturesExplore the power of Python libraries to tackle difficult network problems efficiently and effectively, including pyATS, Nornir, and Ansible 2.8Use Python and Ansible for DevOps, network device automation, DevOps, and software-defined networkingBecome an expert in implementing advanced network-related tasks with Python 3Book Description Networks in your infrastructure set the foundation for how your application can be deployed, maintained, and serviced. Python is the ideal language for network engineers to explore tools that were previously available to systems engineers and application developers. In Mastering Python Networking, Third edition, you'll embark on a Python-based journey to transition from traditional network engineers to network developers ready for the next-generation of networks. This new edition is completely revised and updated to work with Python 3. In addition to new chapters on network data analysis with ELK stack (Elasticsearch, Logstash, Kibana, and Beats) and Azure Cloud Networking, it includes updates on using newer libraries such as pyATS and Nornir, as well as Ansible 2.8. Each chapter is updated with the latest libraries with working examples to ensure compatibility and understanding of the concepts. Starting with a basic overview of Python, the book teaches you how it can interact with both legacy and API-enabled network devices. You will learn to leverage high-level Python packages and frameworks to perform network automation tasks, monitoring, management, and enhanced network security followed by Azure and AWS Cloud networking. Finally, you will use Jenkins for continuous integration as well as testing tools to verify your network. What you will learnUse Python libraries to interact with your networkIntegrate Ansible 2.8 using Python to control Cisco, Juniper, and Arista network devicesLeverage existing Flask web frameworks to construct high-level APIsLearn how to build virtual networks in the AWS & Azure CloudLearn how to use Elastic Stack for network data analysisUnderstand how Jenkins can be used to automatically deploy changes in your networkUse PyTest and Unittest for Test-Driven Network Development in networking engineering with PythonWho this book is for Mastering Python Networking, Third edition is for network engineers, developers, and SREs who want to use Python for network automation, programmability, and data analysis. Basic familiarity with Python programming and networking-related concepts such as Transmission Control Protocol/Internet Protocol (TCP/IP) will be useful.
  data analysis with kibana: Practical Data Analysis Dhiraj Bhuyan, 2019-11-30 “Practical Data Analysis – Using Python & Open Source Technology” uses a case-study based approach to explore some of the real-world applications of open source data analysis tools and techniques. Specifically, the following topics are covered in this book: 1. Open Source Data Analysis Tools and Techniques. 2. A Beginner’s Guide to “Python” for Data Analysis. 3. Implementing Custom Search Engines On The Fly. 4. Visualising Missing Data. 5. Sentiment Analysis and Named Entity Recognition. 6. Automatic Document Classification, Clustering and Summarisation. 7. Fraud Detection Using Machine Learning Techniques. 8. Forecasting - Using Data to Map the Future. 9. Continuous Monitoring and Real-Time Analytics. 10. Creating a Robot for Interacting with Web Applications. Free samples of the book is available at - http://timesofdatascience.com
  data analysis with kibana: Mastering Microservices with Java Sourabh Sharma, 2019-02-26 Master the art of implementing scalable and reactive microservices in your production environment with Java 11 Key FeaturesUse domain-driven designs to build microservicesExplore various microservices design patterns such as service discovery, registration, and API GatewayUse Kafka, Avro, and Spring Streams to implement event-based microservicesBook Description Microservices are key to designing scalable, easy-to-maintain applications. This latest edition of Mastering Microservices with Java, works on Java 11. It covers a wide range of exciting new developments in the world of microservices, including microservices patterns, interprocess communication with gRPC, and service orchestration. This book will help you understand how to implement microservice-based systems from scratch. You'll start off by understanding the core concepts and framework, before focusing on the high-level design of large software projects. You'll then use Spring Security to secure microservices and test them effectively using REST Java clients and other tools. You will also gain experience of using the Netflix OSS suite, comprising the API Gateway, service discovery and registration, and Circuit Breaker. Additionally, you'll be introduced to the best patterns, practices, and common principles of microservice design that will help you to understand how to troubleshoot and debug the issues faced during development. By the end of this book, you'll have learned how to build smaller, lighter, and faster services that can be implemented easily in a production environment. What you will learnUse domain-driven designs to develop and implement microservicesUnderstand how to implement microservices using Spring BootExplore service orchestration and distributed transactions using the SagasDiscover interprocess communication using REpresentational State Transfer (REST) and eventsGain knowledge of how to implement and design reactive microservicesDeploy and test various microservicesWho this book is for This book is designed for Java developers who are familiar with microservices architecture and now want to effectively implement microservices at an enterprise level. Basic knowledge and understanding of core microservice elements and applications is necessary.
  data analysis with kibana: Jenkins, Docker, and Kubernetes: Mastering DevOps Automation Peter Jones, 2024-10-15 Jenkins, Docker, and Kubernetes: Mastering DevOps Automation is a comprehensive guide tailored for professionals eager to master the intricacies of automation within the DevOps ecosystem. This indispensable resource meticulously delves into the integration and effective utilization of Jenkins, Docker, and Kubernetes—the leading trio at the heart of the DevOps transformation. Through a focus on practical applications, readers will navigate the journey of installing, configuring, and optimizing these tools to design robust CI/CD pipelines, streamline software development processes, and deploy applications with unparalleled precision and efficiency. From the basics of containerization to managing containers at scale, and from securing CI/CD pipelines to implementing sophisticated deployment strategies, this book covers it all. Whether you're a software developer, IT professional, or dedicated DevOps practitioner, Jenkins, Docker, and Kubernetes: Mastering DevOps Automation empowers you to enhance your skills, ensuring seamless, high-quality software delivery in today’s fast-paced digital environment. Harness the power of automation and transform your development workflow with this essential guide.
  data analysis with kibana: Brain-Inspired Cognitive Architectures for Artificial Intelligence: BICA*AI 2020 Alexei V. Samsonovich, Ricardo R. Gudwin, Alexandre da Silva Simões, 2020-12-08 The book focuses on original approaches intended to support the development of biologically inspired cognitive architectures. It bridges together different disciplines, from classical artificial intelligence to linguistics, from neuro- and social sciences to design and creativity, among others. The chapters, based on contributions presented at the Eleventh Annual Meeting of the BICA Society, held on November 10-14, 2020, in Natal, Brazil, discuss emerging methods, theories and ideas towards the realization of general-purpose humanlike artificial intelligence or fostering a better understanding of the ways the human mind works. All in all, the book provides engineers, mathematicians, psychologists, computer scientists and other experts with a timely snapshot of recent research and a source of inspiration for future developments in the broadly intended areas of artificial intelligence and biological inspiration.
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)

Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …

Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …

Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …

Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …

Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …

Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …

Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …

Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …

Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …

Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)

Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …

Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …

Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …

Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …

Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …

Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …

Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …

Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …

Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …