Leverage the power of Neo4j to design, implement, and deliver top-notch projects About This BookUnderstand, in detail, the Pattern matching theory, and cypher optimizationUse Neo4j models combined with the power of Cypher to sketch and start working quicklyA fast-paced, example-oriented guide to help you integrate Neo4j in standard Java applicationsWho This Book Is ForIf you are an application developer or software architect who wants to dive into the Cypher language and learn the concepts of graph theory and graph-based data models, this is the book for you. Prior experience with a graph-based or NoSQL-based database is expected. Some knowledge of Java will be beneficial, as this will give you more insights into Neo4j's extensibility.What You Will Learn Install Neo4j as an embedded database or a server in minutes Import data into Neo4j from various data sources such as CSV and Excel using efficient batch import processes Model complex interactions and query Neo4j with its top-notch query language, Cypher Integrate Neo4j in your Java applications, using Neo4j APIs Leverage the mapping power of Spring Data Neo4j in your Java applications Write your own Neo4j extensions Deploy Neo4j within your infrastructure in minutes In DetailWith a rise in the popularity of NoSQL databases, Neo4j provides a viable alternative for storage, processing, and querying highly connected data natively in the form of graphs. It saves you the trouble of tabular data formats and join operations while providing a fast querying language called Cypher, which is declarative and works on the principle of pattern matching. It is an open source database and has been tailored to be integrated into a wide variety of scenarios, platforms, and tools.This book will guide you from the phase where you have to choose the appropriate license towards the deployment phase, instilling good design practices along the way. It will cover every required phase needed by a successful project. Using a fictitious project as a case study, you will learn the various implementation details and concepts of Neo4j that will help you develop your use cases quickly.

Neo4j Essentials

Computers & Technology

Gupta, Sumit

Programming Languages

Packt Pub Ltd

Develop exciting real-world Python-based web applications with Neo4j using frameworks such as Flask, Py2neo, and Django About This BookDevelop a set of common applications and solutions with Neo4j and PythonSecure and deploy the Neo4j database in productionA step-by-step guide on implementing and deploying interactive Python-based web applications on graph data modelWho This Book Is ForIf you are a Python developer and want to expand your understanding of Python-based web applications over Neo4j graph data models, this is the book for you.What You Will LearnUnderstand the licensing and installation of the Neo4j database and work with its various tools and utilitiesLearn the intricacies of Cypher as a graph query languageWork with Cypher to create and modify graph data modelsIntegrate Python and Neo4j using Py2neoDevelop REST-based services over social network data using Flask and object graph models over Neo4jIntegrate Django-based web applications over graph data models using NeomodelExplore different deployment models and their applicability with existing applicationsIn DetailPy2neo is a simple and pragmatic Python library that provides access to the popular graph database Neo4j via its RESTful web service interface. This brings with it a heavily refactored core, a cleaner API, better performance, and some new idioms.You will begin with licensing and installing Neo4j, learning the fundamentals of Cypher as a graph query language, and exploring Cypher optimizations. You will discover how to integrate with various Python frameworks such as Flask and its extensions: Py2neo, Neomodel, and Django. Finally, the deployment aspects of your Python-based Neo4j applications in a production environment is also covered. By sequentially working through the steps in each chapter, you will quickly learn and master the various implementation details and integrations of Python and Neo4j, helping you to develop your use cases more quickly.

Building Web Applications With Python and Neo4j

Networking & Cloud Computing

Building scalable and fault-tolerant streaming applications made easy with Spark streamingAbout This Book<ul>
<li>Process live data streams more efficiently with better fault recovery using Spark Streaming</li>
<li>Implement and deploy real-time log file analysis</li>
<li>Learn about integration with Advance Spark Libraries – GraphX, Spark SQL, and MLib.</li>
</ul>Who This Book Is ForThis book is intended for big data developers with basic knowledge of Scala but no knowledge of Spark. It will help you grasp the basics of developing real-time applications with Spark and understand efficient programming of core elements and applications.What You Will Learn<ul>
<li>Install and configure Spark and Spark Streaming to execute applications</li>
<li>Explore the architecture and components of Spark and Spark Streaming to use it as a base for other libraries</li>
<li>Process distributed log files in real-time to load data from distributed sources</li>
<li>Apply transformations on streaming data to use its functions</li>
<li>Integrate Apache Spark with the various advance libraries like MLib and GraphX</li>
<li>Apply production deployment scenarios to deploy your application</li>
</ul>In DetailUsing practical examples with easy-to-follow steps, this book will teach you how to build real-time applications with Spark Streaming.
Starting with installing and setting the required environment, you will write and execute your first program for Spark Streaming. This will be followed by exploring the architecture and components of Spark Streaming along with an overview of libraries/functions exposed by Spark. Next you will be taught about various client APIs for coding in Spark by using the use-case of distributed log file processing. You will then apply various functions to transform and enrich streaming data. Next you will learn how to cache and persist datasets. Moving on you will integrate Apache Spark with various other libraries/components of Spark like Mlib, GraphX, and Spark SQL. Finally, you will learn about deploying your application and cover the different scenarios ranging from standalone mode to distributed mode using Mesos, Yarn, and private data centers or on cloud infrastructure.Style and approachA Step-by-Step approach to learn Spark Streaming in a structured manner, with detailed explanation of basic and advance features in an easy-to-follow Style. Each topic is explained sequentially and supported with real world examples and executable code snippets that appeal to the needs of readers with the wide range of experiences.

Learning Real Time Processing With Spark Streaming

Programming

Design, process, and analyze large sets of complex data in real timeAbout This Book<ul>
<li>Get acquainted with transformations and database-level interactions, and ensure the reliability of messages processed using Storm</li>
<li>Implement strategies to solve the challenges of real-time data processing</li>
<li>Load datasets, build queries, and make recommendations using Spark SQL</li>
</ul>Who This Book Is ForIf you are a Big Data architect, developer, or a programmer who wants to develop applications/frameworks to implement real-time analytics using open source technologies, then this book is for you.What You Will Learn<ul>
<li>Explore big data technologies and frameworks</li>
<li>Work through practical challenges and use cases of real-time analytics versus batch analytics</li>
<li>Develop real-word use cases for processing and analyzing data in real-time using the programming paradigm of Apache Storm</li>
<li>Handle and process real-time transactional data</li>
<li>Optimize and tune Apache Storm for varied workloads and production deployments</li>
<li>Process and stream data with Amazon Kinesis and Elastic MapReduce</li>
<li>Perform interactive and exploratory data analytics using Spark SQL</li>
<li>Develop common enterprise architectures/applications for real-time and batch analytics</li>
</ul>In DetailEnterprise has been striving hard to deal with the challenges of data arriving in real time or near real time.
Although there are technologies such as Storm and Spark (and many more) that solve the challenges of real-time data, using the appropriate technology/framework for the right business use case is the key to success. This book provides you with the skills required to quickly design, implement and deploy your real-time analytics using real-world examples of big data use cases.
From the beginning of the book, we will cover the basics of varied real-time data processing frameworks and technologies. We will discuss and explain the differences between batch and real-time processing in detail, and will also explore the techniques and programming concepts using Apache Storm.
Moving on, we'll familiarize you with “Amazon Kinesis” for real-time data processing on cloud. We will further develop your understanding of real-time analytics through a comprehensive review of Apache Spark along with the high-level architecture and the building blocks of a Spark program.
You will learn how to transform your data, get an output from transformations, and persist your results using Spark RDDs, using an interface called Spark SQL to work with Spark.
At the end of this book, we will introduce Spark Streaming, the streaming library of Spark, and will walk you through the emerging Lambda Architecture (LA), which provides a hybrid platform for big data processing by combining real-time and precomputed batch data to provide a near real-time view of incoming data.Style and approachThis step-by-step is an easy-to-follow, detailed tutorial, filled with practical examples of basic and advanced features.
Each topic is explained sequentially and supported by real-world examples and executable code snippets.

Real-Time Big Data Analytics: Design, Process, and Analyze Large Sets of Complex Data in Real Time

Saxena, Shilpi

Learn how to bring your data to life with this hands-on guide to visual analytics with Tableau Key Features Master the fundamentals of Tableau Desktop and Tableau Prep Learn how to explore, analyze, and present data to provide business insights Build your experience and confidence with hands-on exercises and activities Book Description 
Learning Tableau has never been easier, thanks to this practical introduction to storytelling with data. The Tableau Workshop breaks down the analytical process into five steps: data preparation, data exploration, data analysis, interactivity, and distribution of dashboards. Each stage is addressed with a clear walkthrough of the key tools and techniques you'll need, as well as engaging real-world examples, meaningful data, and practical exercises to give you valuable hands-on experience. 
As you work through the book, you'll learn Tableau step by step, studying how to clean, shape, and combine data, as well as how to choose the most suitable charts for any given scenario. You'll load data from various sources and formats, perform data engineering to create new data that delivers deeper insights, and create interactive dashboards that engage end-users. 
All concepts are introduced with clear, simple explanations and demonstrated through realistic example scenarios. You'll simulate real-world data science projects with use cases such as traffic violations, urban populations, coffee store sales, and air travel delays. 
By the end of this Tableau book, you'll have the skills and knowledge to confidently present analytical results and make data-driven decisions. What you will learn Become an effective user of Tableau Prep and Tableau Desktop Load, combine, and process data for analysis and visualization Understand different types of charts and when to use them Perform calculations to engineer new data and unlock hidden insights Add interactivity to your visualizations to make them more engaging Create holistic dashboards that are detailed and user-friendly Who this book is for 
This book is for anyone who wants to get started on visual analytics with Tableau. If you're new to Tableau, this Workshop will get you up and running. If you already have some experience in Tableau, this book will help fill in any gaps, consolidate your understanding, and give you extra practice of key tools. Table of Contents Introduction: Visual Analytics with Tableau Data Preparation: Using Tableau Desktop Data Preparation: Using Tableau Prep Data Exploration: Comparison and Composition Data Exploration: Distributions and Relationships Data Exploration: Exploring Geographical Data Data Analysis: Creating and Using Calculations Data Analysis: Creating and Using Table Calculations Data Analysis: Creating and Using Level of Details (LOD) Calculations Dashboards and Storyboards Tableau Interactivity: Part 1

The Tableau Workshop: A practical guide to the art of data visualization with Tableau

Pinto, Sylvester

Sankhe-Savale, Shweta

Software

Packt Publishing