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Apache Solr: A Practical Approach to Enterprise Search
by Dikshant ShahiBuild an enterprise search engine using Apache Solr: index and search documents; ingest data from varied sources; apply various text processing techniques; utilize different search capabilities; and customize Solr to retrieve the desired results. Apache Solr: A Practical Approach to Enterprise Search explains each essential concept--backed by practical and industry examples--to help you attain expert-level knowledge. The book, which assumes a basic knowledge of Java, starts with an introduction to Solr, followed by steps to setting it up, indexing your first set of documents, and searching them. It then introduces you to information retrieval and its implementation in Apache Solr; this will help you understand your search problem, decide the approach to build an effective solution, and use various metrics to evaluate the results. The book next covers the schema design and techniques to build a text analysis chain for cleansing, normalizing and enriching your documents and addressing different types of search queries. It describes various popular matching techniques which are generally applied to improve the precision and recall of searches. You will learn the end-to-end process of data ingestion from varied sources, metadata extraction, pre-processing and transformation of content, various search components, query parsers and other advanced search capabilities. After covering out-of-the-box features, Solr expert Dikshant Shahi dives into ways you can customize Solr for your business and its specific requirements, along with ways to plug in your own components. Most important, you will learn about implementations for Solr scoring, factors affecting the document score, and tuning the score for the application at hand. The book explains why textual scoring is not sufficient for practical ranking of documents and ways to integrate real-world factors for contributing to the document ranking. You'll see how to influence user experience by providing suggestions and recommendations. You'll also see integration of Solr with important related technologies such as OpenNLP and Tika. Additionally, you will learn about scaling Solr using SolrCloud. This book concludes with coverage of semantic search capabilities, which is crucial for taking the search experience to the next level. By the end of Apache Solr, you will be proficient in designing and developing your search engine. What you'll learn How to develop a search engine using Solr How to implement information retrieval concepts How to master search engine internals How to build your search strategy Hot to customize Solr for your unique search problem How to make your search engine intelligent and self-learning Who this book is for Professionals into data mining, data management, or Web development. Table of Contents Chapter 1: Introduction to Search Engines and Solr Chapter 2: Solr Setup and Administration Chapter 3: Understanding Information Retrieval Chapter 4: Search Strategy and Schema Design Chapter 5: Indexing Data Chapter 6: Searching Solr Chapter 7: Advanced Querying Chapter 8: Customizing Document Ranking Chapter 9: Going Beyond Text Matching Chapter 10: SolrCloud Chapter 11: Practical Search Examples
Apache Solr 3.1 Cookbook
by Rafa KuThis book is part of Packt's Cookbook series; each chapter looks at a different aspect of working with Apache Solr. The recipes deal with common problems of working with Solr by using easy-to-understand, real-life examples. The book is not in any way a complete Apache Solr reference and you should see it as a helping hand when things get rough on your journey with Apache Solr.Developers who are working with Apache Solr and would like to know how to combat common problems will find this book of great use. Knowledge of Apache Lucene would be a bonus but is not required.
Apache Solr 3 Enterprise Search Server
by Eric Pugh David SmileyThe book is written as a reference guide. It includes fully working examples based on a real-world public data set.This book is for developers who want to learn how to use Apache Solr in their applications. Only basic programming skills are needed.
Apache Solr 4 Cookbook
by Rafal KucApache Solr 4 Cookbook is written in a helpful, practical style with numerous hands-on recipes to help you master Apache Solr to get more precise search results and analysis, higher performance, and reliability. This book is for developers who wish to learn how to master Apache Solr 4. This book will specifically appeal to developers who wish to quickly get to grips with the changes and new features of Apache Solr 4. This book is also handy as a practical guide to solving common problems and issues when using Apache Solr.
Apache Solr Beginner's Guide
by Alfredo SerafiniWritten in a friendly, example-driven format, the book includes plenty of step-by-step instructions and examples that are designed to help you get started with Apache Solr.This book is an entry level text into the wonderful world of Apache Solr. The book will center around a couple of simple projects such as setting up Solr and all the stuff that comes with customizing the Solr schema and configuration. This book is for developers looking to start using Apache Solr who are stuck or intimidated by the difficulty of setting it up and using it.For anyone wanting to embed a search engine in their site to help users navigate around the mammoth data available this book is an ideal starting point. Moreover, if you are a data architect or a project manager and want to make some key design decisions, you will find that every example included in the book contains ideas usable in real-world contexts.
Apache Solr Enterprise Search Server - Third Edition
by David SmileyThis book is for developers who want to learn how to get the most out of Solr in their applications, whether you are new to the field, have used Solr but don't know everything, or simply want a good reference. It would be helpful to have some familiarity with basic programming concepts, but no prior experience is required.
Apache Solr Essentials
by Andrea GazzariniIf you are a competent developer with experience of working with technologies similar to Apache Solr and want to develop efficient search applications, then this book is for you. Familiarity with the Java programming language is required.
Apache Solr for Indexing Data
by Sachin Handiekar Anshul JohriEnhance your Solr indexing experience with advanced techniques and the built-in functionalities available in Apache Solr About This Book * Learn about distributed indexing and real-time optimization to change index data on fly * Index data from various sources and web crawlers using built-in analyzers and tokenizers * This step-by-step guide is packed with real-life examples on indexing data Who This Book Is For This book is for developers who want to increase their experience of indexing in Solr by learning about the various index handlers, analyzers, and methods available in Solr. Beginner level Solr development skills are expected. What You Will Learn * Get to know the basic features of Solr indexing and the analyzers/tokenizers available * Index XML/JSON data in Solr using the HTTP Post tool and CURL command * Work with Data Import Handler to index data from a database * Use Apache Tika with Solr to index word documents, PDFs, and much more * Utilize Apache Nutch and Solr integration to index crawled data from web pages * Update indexes in real-time data feeds * Discover techniques to index multi-language and distributed data in Solr * Combine the various indexing techniques into a real-life working example of an online shopping web application In Detail Apache Solr is a widely used, open source enterprise search server that delivers powerful indexing and searching features. These features help fetch relevant information from various sources and documentation. Solr also combines with other open source tools such as Apache Tika and Apache Nutch to provide more powerful features. This fast-paced guide starts by helping you set up Solr and get acquainted with its basic building blocks, to give you a better understanding of Solr indexing. You'll quickly move on to indexing text and boosting the indexing time. Next, you'll focus on basic indexing techniques, various index handlers designed to modify documents, and indexing a structured data source through Data Import Handler. Moving on, you will learn techniques to perform real-time indexing and atomic updates, as well as more advanced indexing techniques such as de-duplication. Later on, we'll help you set up a cluster of Solr servers that combine fault tolerance and high availability. You will also gain insights into working scenarios of different aspects of Solr and how to use Solr with e-commerce data. By the end of the book, you will be competent and confident working with indexing and will have a good knowledge base to efficiently program elements. Style and approach This fast-paced guide is packed with examples that are written in an easy-to-follow style, and are accompanied by detailed explanation. Working examples are included to help you get better results for your applications.
Apache Solr High Performance
by Surendra MohanThis book is an easy-to-follow guide, full of hands-on, real-world examples. Each topic is explained and demonstrated in a specific and user-friendly flow, from search optimization using Solr to Deployment of Zookeeper applications. This book is ideal for Apache Solr developers and want to learn different techniques to optimize Solr performance with utmost efficiency, along with effectively troubleshooting the problems that usually occur while trying to boost performance. Familiarity with search servers and database querying is expected.
Apache Solr PHP Integration
by Jayant KumarThis book is full of step-by-step example-oriented tutorials which will show readers how to integrate Solr in PHP applications using the available libraries, and boost the inherent search facilities that Solr offers.If you are a developer who knows PHP and is interested in integrating search into your applications, this is the book for you. No advanced knowledge of Solr is required. Very basic knowledge of system commands and the command-line interface on both Linux and Windows is required. You should also be familiar with the concept of Web servers.
Apache Solr Search Patterns
by Jayant KumarThis book is for developers who already know how to use Solr and are looking at procuring advanced strategies for improving their search using Solr. This book is also for people who work with analytics to generate graphs and reports using Solr. Moreover, if you are a search architect who is looking forward to scale your search using Solr, this is a must have book for you. It would be helpful if you are familiar with the Java programming language.
Apache Spark 2 for Beginners
by Rajanarayanan ThottuvaikkatumanaDevelop large-scale distributed data processing applications using Spark 2 in Scala and Python About This Book * This book offers an easy introduction to the Spark framework published on the latest version of Apache Spark 2 * Perform efficient data processing, machine learning and graph processing using various Spark components * A practical guide aimed at beginners to get them up and running with Spark Who This Book Is For If you are an application developer, data scientist, or big data solutions architect who is interested in combining the data processing power of Spark from R, and consolidating data processing, stream processing, machine learning, and graph processing into one unified and highly interoperable framework with a uniform API using Scala or Python, this book is for you. What You Will Learn * Get to know the fundamentals of Spark 2 and the Spark programming model using Scala and Python * Know how to use Spark SQL and DataFrames using Scala and Python * Get an introduction to Spark programming using R * Perform Spark data processing, charting, and plotting using Python * Get acquainted with Spark stream processing using Scala and Python * Be introduced to machine learning using Spark MLlib * Get started with graph processing using the Spark GraphX * Bring together all that you've learned and develop a complete Spark application In Detail Spark is one of the most widely-used large-scale data processing engines and runs extremely fast. It is a framework that has tools that are equally useful for application developers as well as data scientists. This book starts with the fundamentals of Spark 2 and covers the core data processing framework and API, installation, and application development setup. Then the Spark programming model is introduced through real-world examples followed by Spark SQL programming with DataFrames. An introduction to SparkR is covered next. Later, we cover the charting and plotting features of Python in conjunction with Spark data processing. After that, we take a look at Spark's stream processing, machine learning, and graph processing libraries. The last chapter combines all the skills you learned from the preceding chapters to develop a real-world Spark application. By the end of this book, you will have all the knowledge you need to develop efficient large-scale applications using Apache Spark. Style and approach Learn about Spark's infrastructure with this practical tutorial. With the help of real-world use cases on the main features of Spark we offer an easy introduction to the framework.
Apache Spark 2.x Cookbook
by Rishi YadavOver 70 recipes to help you use Apache Spark as your single big data computing platform and master its libraries About This Book • This book contains recipes on how to use Apache Spark as a unified compute engine • Cover how to connect various source systems to Apache Spark • Covers various parts of machine learning including supervised/unsupervised learning & recommendation engines Who This Book Is For This book is for data engineers, data scientists, and those who want to implement Spark for real-time data processing. Anyone who is using Spark (or is planning to) will benefit from this book. The book assumes you have a basic knowledge of Scala as a programming language. What You Will Learn • Install and configure Apache Spark with various cluster managers & on AWS • Set up a development environment for Apache Spark including Databricks Cloud notebook • Find out how to operate on data in Spark with schemas • Get to grips with real-time streaming analytics using Spark Streaming & Structured Streaming • Master supervised learning and unsupervised learning using MLlib • Build a recommendation engine using MLlib • Graph processing using GraphX and GraphFrames libraries • Develop a set of common applications or project types, and solutions that solve complex big data problems In Detail While Apache Spark 1.x gained a lot of traction and adoption in the early years, Spark 2.x delivers notable improvements in the areas of API, schema awareness, Performance, Structured Streaming, and simplifying building blocks to build better, faster, smarter, and more accessible big data applications. This book uncovers all these features in the form of structured recipes to analyze and mature large and complex sets of data. Starting with installing and configuring Apache Spark with various cluster managers, you will learn to set up development environments. Further on, you will be introduced to working with RDDs, DataFrames and Datasets to operate on schema aware data, and real-time streaming with various sources such as Twitter Stream and Apache Kafka. You will also work through recipes on machine learning, including supervised learning, unsupervised learning & recommendation engines in Spark. Last but not least, the final few chapters delve deeper into the concepts of graph processing using GraphX, securing your implementations, cluster optimization, and troubleshooting. Style and approach This book is packed with intuitive recipes supported with line-by-line explanations to help you understand Spark 2.x's real-time processing capabilities and deploy scalable big data solutions. This is a valuable resource for data scientists and those working on large-scale data projects.
Apache Spark 2.x for Java Developers
by Sourav Gulati Sumit KumarUnleash the data processing and analytics capability of Apache Spark with the language of choice: Java About This Book • Perform big data processing with Spark—without having to learn Scala! • Use the Spark Java API to implement efficient enterprise-grade applications for data processing and analytics • Go beyond mainstream data processing by adding querying capability, Machine Learning, and graph processing using Spark Who This Book Is For If you are a Java developer interested in learning to use the popular Apache Spark framework, this book is the resource you need to get started. Apache Spark developers who are looking to build enterprise-grade applications in Java will also find this book very useful. What You Will Learn • Process data using different file formats such as XML, JSON, CSV, and plain and delimited text, using the Spark core Library. • Perform analytics on data from various data sources such as Kafka, and Flume using Spark Streaming Library • Learn SQL schema creation and the analysis of structured data using various SQL functions including Windowing functions in the Spark SQL Library • Explore Spark Mlib APIs while implementing Machine Learning techniques to solve real-world problems • Get to know Spark GraphX so you understand various graph-based analytics that can be performed with Spark In Detail Apache Spark is the buzzword in the big data industry right now, especially with the increasing need for real-time streaming and data processing. While Spark is built on Scala, the Spark Java API exposes all the Spark features available in the Scala version for Java developers. This book will show you how you can implement various functionalities of the Apache Spark framework in Java, without stepping out of your comfort zone. The book starts with an introduction to the Apache Spark 2.x ecosystem, followed by explaining how to install and configure Spark, and refreshes the Java concepts that will be useful to you when consuming Apache Spark's APIs. You will explore RDD and its associated common Action and Transformation Java APIs, set up a production-like clustered environment, and work with Spark SQL. Moving on, you will perform near-real-time processing with Spark streaming, Machine Learning analytics with Spark MLlib, and graph processing with GraphX, all using various Java packages. By the end of the book, you will have a solid foundation in implementing components in the Spark framework in Java to build fast, real-time applications. Style and approach This practical guide teaches readers the fundamentals of the Apache Spark framework and how to implement components using the Java language. It is a unique blend of theory and practical examples, and is written in a way that will gradually build your knowledge of Apache Spark.
Apache Spark 2.x Machine Learning Cookbook
by Siamak Amirghodsi Meenakshi Rajendran Broderick Hall Shuen MeiSimplify machine learning model implementations with Spark About This Book • Solve the day-to-day problems of data science with Spark • This unique cookbook consists of exciting and intuitive numerical recipes • Optimize your work by acquiring, cleaning, analyzing, predicting, and visualizing your data Who This Book Is For This book is for Scala developers with a fairly good exposure to and understanding of machine learning techniques, but lack practical implementations with Spark. A solid knowledge of machine learning algorithms is assumed, as well as hands-on experience of implementing ML algorithms with Scala. However, you do not need to be acquainted with the Spark ML libraries and ecosystem. What You Will Learn • Get to know how Scala and Spark go hand-in-hand for developers when developing ML systems with Spark • Build a recommendation engine that scales with Spark • Find out how to build unsupervised clustering systems to classify data in Spark • Build machine learning systems with the Decision Tree and Ensemble models in Spark • Deal with the curse of high-dimensionality in big data using Spark • Implement Text analytics for Search Engines in Spark • Streaming Machine Learning System implementation using Spark In Detail Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks. This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we'll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems. Style and approach This book is packed with intuitive recipes supported with line-by-line explanations to help you understand how to optimize your work flow and resolve problems when working with complex data modeling tasks and predictive algorithms. This is a valuable resource for data scientists and those working on large scale data projects.
Apache Spark Deep Learning Cookbook: Over 80 recipes that streamline deep learning in a distributed environment with Apache Spark
by Amrith Ravindra Ahmed SherifA solution-based guide to put your deep learning models into production with the power of Apache SparkKey FeaturesDiscover practical recipes for distributed deep learning with Apache SparkLearn to use libraries such as Keras and TensorFlow Solve problems in order to train your deep learning models on Apache SparkBook DescriptionWith deep learning gaining rapid mainstream adoption in modern-day industries, organizations are looking for ways to unite popular big data tools with highly efficient deep learning libraries. As a result, this will help deep learning models train with higher efficiency and speed. With the help of the Apache Spark Deep Learning Cookbook, you’ll work through specific recipes to generate outcomes for deep learning algorithms, without getting bogged down in theory. From setting up Apache Spark for deep learning to implementing types of neural net, this book tackles both common and not so common problems to perform deep learning on a distributed environment. In addition to this, you’ll get access to deep learning code within Spark that can be reused to answer similar problems or tweaked to answer slightly different problems. You will also learn how to stream and cluster your data with Spark. Once you have got to grips with the basics, you’ll explore how to implement and deploy deep learning models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in Spark, using popular libraries such as TensorFlow and Keras.By the end of the book, you'll have the expertise to train and deploy efficient deep learning models on Apache Spark.What you will learn Set up a fully functional Spark environment Understand practical machine learning and deep learning concepts Apply built-in machine learning libraries within Spark Explore libraries that are compatible with TensorFlow and Keras Explore NLP models such as Word2vec and TF-IDF on Spark Organize dataframes for deep learning evaluation Apply testing and training modeling to ensure accuracy Access readily available code that may be reusableWho this book is forIf you’re looking for a practical and highly useful resource for implementing efficiently distributed deep learning models with Apache Spark, then the Apache Spark Deep Learning Cookbook is for you. Knowledge of the core machine learning concepts and a basic understanding of the Apache Spark framework is required to get the best out of this book. Additionally, some programming knowledge in Python is a plus.
Apache Spark Graph Processing
by Rindra RamamonjisonThis book is for data scientists and big data developers who want to learn the processing and analyzing graph datasets at scale. Basic programming experience with Scala is assumed. Basic knowledge of Spark is assumed.
Apache Spark Machine Learning Blueprints
by Alex LiuDevelop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide About This Book * Customize Apache Spark and R to fit your analytical needs in customer research, fraud detection, risk analytics, and recommendation engine development * Develop a set of practical Machine Learning applications that can be implemented in real-life projects * A comprehensive, project-based guide to improve and refine your predictive models for practical implementation Who This Book Is For If you are a data scientist, a data analyst, or an R and SPSS user with a good understanding of machine learning concepts, algorithms, and techniques, then this is the book for you. Some basic understanding of Spark and its core elements and application is required. What You Will Learn * Set up Apache Spark for machine learning and discover its impressive processing power * Combine Spark and R to unlock detailed business insights essential for decision making * Build machine learning systems with Spark that can detect fraud and analyze financial risks * Build predictive models focusing on customer scoring and service ranking * Build a recommendation systems using SPSS on Apache Spark * Tackle parallel computing and find out how it can support your machine learning projects * Turn open data and communication data into actionable insights by making use of various forms of machine learning In Detail There's a reason why Apache Spark has become one of the most popular tools in Machine Learning - its ability to handle huge datasets at an impressive speed means you can be much more responsive to the data at your disposal. This book shows you Spark at its very best, demonstrating how to connect it with R and unlock maximum value not only from the tool but also from your data. Packed with a range of project "blueprints" that demonstrate some of the most interesting challenges that Spark can help you tackle, you'll find out how to use Spark notebooks and access, clean, and join different datasets before putting your knowledge into practice with some real-world projects, in which you will see how Spark Machine Learning can help you with everything from fraud detection to analyzing customer attrition. You'll also find out how to build a recommendation engine using Spark's parallel computing powers. Style and approach This book offers a step-by-step approach to setting up Apache Spark, and use other analytical tools with it to process Big Data and build machine learning projects.The initial chapters focus more on the theory aspect of machine learning with Spark, while each of the later chapters focuses on building standalone projects using Spark.
Apache Spark Quick Start Guide: Quickly learn the art of writing efficient big data applications with Apache Spark
by Shrey Mehrotra Akash GradeA practical guide for solving complex data processing challenges by applying the best optimizations techniques in Apache Spark. Key Features Learn about the core concepts and the latest developments in Apache Spark Master writing efficient big data applications with Spark's built-in modules for SQL, Streaming, Machine Learning and Graph analysis Get introduced to a variety of optimizations based on the actual experience Book Description Apache Spark is a flexible framework that allows processing of batch and real-time data. Its unified engine has made it quite popular for big data use cases. This book will help you to get started with Apache Spark 2.0 and write big data applications for a variety of use cases. It will also introduce you to Apache Spark – one of the most popular Big Data processing frameworks. Although this book is intended to help you get started with Apache Spark, but it also focuses on explaining the core concepts. This practical guide provides a quick start to the Spark 2.0 architecture and its components. It teaches you how to set up Spark on your local machine. As we move ahead, you will be introduced to resilient distributed datasets (RDDs) and DataFrame APIs, and their corresponding transformations and actions. Then, we move on to the life cycle of a Spark application and learn about the techniques used to debug slow-running applications. You will also go through Spark's built-in modules for SQL, streaming, machine learning, and graph analysis. Finally, the book will lay out the best practices and optimization techniques that are key for writing efficient Spark applications. By the end of this book, you will have a sound fundamental understanding of the Apache Spark framework and you will be able to write and optimize Spark applications. What you will learn Learn core concepts such as RDDs, DataFrames, transformations, and more Set up a Spark development environment Choose the right APIs for your applications Understand Spark's architecture and the execution flow of a Spark application Explore built-in modules for SQL, streaming, ML, and graph analysis Optimize your Spark job for better performance Who this book is for If you are a big data enthusiast and love processing huge amount of data, this book is for you. If you are data engineer and looking for the best optimization techniques for your Spark applications, then you will find this book helpful. This book also helps data scientists who want to implement their machine learning algorithms in Spark. You need to have a basic understanding of any one of the programming languages such as Scala, Python or Java.
Apache Sqoop Cookbook
by Jarek Jarcec Cecho Kathleen TingIntegrating data from multiple sources is essential in the age of big data, but it can be a challenging and time-consuming task. This handy cookbook provides dozens of ready-to-use recipes for using Apache Sqoop, the command-line interface application that optimizes data transfers between relational databases and Hadoop. Sqoop is both powerful and bewildering, but with this cookbook's problem-solution-discussion format, you'll quickly learn how to deploy and then apply Sqoop in your environment. The authors provide MySQL, Oracle, and PostgreSQL database examples on GitHub that you can easily adapt for SQL Server, Netezza, Teradata, or other relational systems. Transfer data from a single database table into your Hadoop ecosystem Keep table data and Hadoop in sync by importing data incrementally Import data from more than one database table Customize transferred data by calling various database functions Export generated, processed, or backed-up data from Hadoop to your database Run Sqoop within Oozie, Hadoop's specialized workflow scheduler Load data into Hadoop's data warehouse (Hive) or database (HBase) Handle installation, connection, and syntax issues common to specific database vendors
Apache Sqoop Cookbook: Unlocking Hadoop for Your Relational Database
by Kathleen Ting Jarek Jarcec CechoIntegrating data from multiple sources is essential in the age of big data, but it can be a challenging and time-consuming task. This handy cookbook provides dozens of ready-to-use recipes for using Apache Sqoop, the command-line interface application that optimizes data transfers between relational databases and Hadoop.Sqoop is both powerful and bewildering, but with this cookbook’s problem-solution-discussion format, you’ll quickly learn how to deploy and then apply Sqoop in your environment. The authors provide MySQL, Oracle, and PostgreSQL database examples on GitHub that you can easily adapt for SQL Server, Netezza, Teradata, or other relational systems.Transfer data from a single database table into your Hadoop ecosystemKeep table data and Hadoop in sync by importing data incrementallyImport data from more than one database tableCustomize transferred data by calling various database functionsExport generated, processed, or backed-up data from Hadoop to your databaseRun Sqoop within Oozie, Hadoop’s specialized workflow schedulerLoad data into Hadoop’s data warehouse (Hive) or database (HBase)Handle installation, connection, and syntax issues common to specific database vendors
Apache Struts 2 Web Application Development
by Dave NewtonThis book takes a clear approach, focusing on one topic per chapter, but interspersing other issues in the mainline text and in chapter detours. Taking a practical approach, it discusses agile web development using Struts 2, with plenty of examples for better understanding. This book is for Java developers who are interested in developing web applications using Struts. If you need a comprehensive introduction to Struts 2.1, along with the most important aspects of additional web application development technologies, agile programming practices, tool creation, and application life cycle management this book is for you. You don't need to know JavaScript and CSS to use this book as the author will teach you the required basics. If you are a Struts 1 or WebWork user and wish to go ahead and migrate to Struts 2, this practical guide is also for you.
Apache Superset Quick Start Guide: Develop interactive visualizations by creating user-friendly dashboards
by Shashank ShekharIntegrate open source data analytics and build business intelligence on SQL databases with Apache Superset. The quick, intuitive nature for data visualization in a web application makes it easy for creating interactive dashboards. Key Features Work with Apache Superset's rich set of data visualizations Create interactive dashboards and data storytelling Easily explore data Book Description Apache Superset is a modern, open source, enterprise-ready business intelligence (BI) web application. With the help of this book, you will see how Superset integrates with popular databases like Postgres, Google BigQuery, Snowflake, and MySQL. You will learn to create real time data visualizations and dashboards on modern web browsers for your organization using Superset. First, we look at the fundamentals of Superset, and then get it up and running. You'll go through the requisite installation, configuration, and deployment. Then, we will discuss different columnar data types, analytics, and the visualizations available. You'll also see the security tools available to the administrator to keep your data safe. You will learn how to visualize relationships as graphs instead of coordinates on plain orthogonal axes. This will help you when you upload your own entity relationship dataset and analyze the dataset in new, different ways. You will also see how to analyze geographical regions by working with location data. Finally, we cover a set of tutorials on dashboard designs frequently used by analysts, business intelligence professionals, and developers. What you will learn Get to grips with the fundamentals of data exploration using Superset Set up a working instance of Superset on cloud services like Google Compute Engine Integrate Superset with SQL databases Build dashboards with Superset Calculate statistics in Superset for numerical, categorical, or text data Understand visualization techniques, filtering, and grouping by aggregation Manage user roles and permissions in Superset Work with SQL Lab Who this book is for This book is for data analysts, BI professionals, and developers who want to learn Apache Superset. If you want to create interactive dashboards from SQL databases, this book is what you need. Working knowledge of Python will be an advantage but not necessary to understand this book.
Apache Tactics 1830-86
by Robert Watt Adam HookThe Apache culture of the latter half of the 19th century blended together the lifestyles of the Great Plains, Great Basin and the South-West, but it was their warfare that captured the imagination. This book reveals the skillful tactics of the Apache people as they raided and eluded the much larger and better-equipped US government forces. Drawing on primary research conducted in the deserts of New Mexico and Arizona, this book reveals the small-unit warfare of the Apache tribes as they attempted to preserve their freedom, and in particular the actions of the most famous member of the Apache tribes - Geronimo.From the Trade Paperback edition.
Apache Tears (Panorama of the Old West #18)
by Georgina Gentry"Nobody does it like Georgina Gentry" (Barbra Critiques). Now, the award-winning author of Comanche Cowboy and Cheyenne Song thrills readers once again with the unforgettable story of a love and wild and untamed as the American West. A BATTLE OF WILLS Spirited heiress Libbie Winters was horrified to find herself in the midst of an Apache rebellion--and amazed to discover that her captor, a virile half-breed scout named Cougar, was the same man who offered her his prized Apache Tears necklace a year before. And though she was promised in marriage to a vengeful Cavalry officer, Libbie could not forget her powerful attraction to the fearless Apache hero. A BLAZE OF PASSION Believing she had rejected his gift, Cougar's heart was hardened toward the haughty white girl he now held for ransom. Yet he could not deny the desire this flame-haired beauty aroused in him, or the love he longed to give her. He knew it was impossible to hold the fiery woman whose spirit was as free as his own--unless Libbie made the dangerous choice to embrace the passion burning between them... "You'll sing the praises of Cheyenne Song. It's Gentry's best book yet!" - Janelle Taylor