Google BigQuery Analytics


Author: Jordan Tigani,Siddartha Naidu
Publisher: John Wiley & Sons
ISBN: 1118824792
Category: Computers
Page: 528
View: 6211

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How to effectively use BigQuery, avoid common mistakes, and execute sophisticated queries against large datasets Google BigQuery Analytics is the perfect guide for business and data analysts who want the latest tips on running complex queries and writing code to communicate with the BigQuery API. The book uses real-world examples to demonstrate current best practices and techniques, and also explains and demonstrates streaming ingestion, transformation via Hadoop in Google Compute engine, AppEngine datastore integration, and using GViz with Tableau to generate charts of query results. In addition to the mechanics of BigQuery, the book also covers the architecture of the underlying Dremel query engine, providing a thorough understanding that leads to better query results. Features a companion website that includes all code and data sets from the book Uses real-world examples to explain everything analysts need to know to effectively use BigQuery Includes web application examples coded in Python

Learning Google BigQuery

A beginner's guide to mining massive datasets through interactive analysis
Author: Thirukkumaran Haridass,Eric Brown
Publisher: Packt Publishing Ltd
ISBN: 1787286290
Category: Computers
Page: 264
View: 8594

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Get a fundamental understanding of how Google BigQuery works by analyzing and querying large datasets About This Book Get started with BigQuery API and write custom applications using it Learn how BigQuery API can be used for storing, managing, and query massive datasets with ease A practical guide with examples and use-cases to teach you everything you need to know about Google BigQuery Who This Book Is For If you are a developer, data analyst, or a data scientist looking to run complex queries over thousands of records in seconds, this book will help you. No prior experience of working with BigQuery is assumed. What You Will Learn Get a hands-on introduction to Google Cloud Platform and its services Understand the different data types supported by Google BigQuery Migrate your enterprise data to BigQuery and query it using the legacy and standard SQL techniques Use partition tables in your project and query external data sources and wild card tables Create tables and data sets dynamically using the BigQuery API Perform real-time inserting of records for analytics using Python and C# Visualize your BigQuery data by connecting it to third party tools such as Tableau and R Master the Google Cloud Pub/Sub for implementing real-time reporting and analytics of your Big Data In Detail Google BigQuery is a popular cloud data warehouse for large-scale data analytics. This book will serve as a comprehensive guide to mastering BigQuery, and how you can utilize it to quickly and efficiently get useful insights from your Big Data. You will begin with getting a quick overview of the Google Cloud Platform and the various services it supports. Then, you will be introduced to the Google BigQuery API and how it fits within in the framework of GCP. The book covers useful techniques to migrate your existing data from your enterprise to Google BigQuery, as well as readying and optimizing it for analysis. You will perform basic as well as advanced data querying using BigQuery, and connect the results to various third party tools for reporting and visualization purposes such as R and Tableau. If you're looking to implement real-time reporting of your streaming data running in your enterprise, this book will also help you. This book also provides tips, best practices and mistakes to avoid while working with Google BigQuery and services that interact with it. By the time you're done with it, you will have set a solid foundation in working with BigQuery to solve even the trickiest of data problems. Style and Approach This book follows a step-by-step approach to teach readers the concepts of Google BigQuery using SQL. To explain various data querying processes, large-scale datasets are used wherever required.

Practical Google Analytics and Google Tag Manager for Developers


Author: Jonathan Weber
Publisher: Apress
ISBN: 1484202651
Category: Computers
Page: 269
View: 9910

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Whether you’re a marketer with development skills or a full-on web developer/analyst, Practical Google Analytics and Google Tag Manager for Developers shows you how to implement Google Analytics using Google Tag Manager to jumpstart your web analytics measurement. There’s a reason that so many organizations use Google Analytics. Effective collection of data with Google Analytics can reduce customer acquisition costs, provide priceless feedback on new product initiatives, and offer insights that will grow a customer or client base. So where does Google Tag Manager fit in? Google Tag Manager allows for unprecedented collaboration between marketing and technical teams, lightning fast updates to your site, and standardization of the most common tags for on-site tracking an d marketing efforts. To achieve the rich data you're really after to better serve your users’ needs, you'll need the tools Google Tag Manager provides for a best-in-class implementation of Google Analytics measurement on your site. Written by data evangelist and Google Analytics expert Jonathan Weber and the team at LunaMetrics, this book offers foundational knowledge, a collection of practical Google Tag Manager recipes, well-tested best practices, and troubleshooting tips to get your implementation in tip-top condition. It covers topics including: • Google Analytics implementation via Google Tag Manager • How to customize Google Analytics for your unique situation • Using Google Tag Manager to track and analyze interactions across multiple devices and touch points • How to extract data from Google Analytics and use Google BigQuery to analyze Big Data questions What You'll Learn Implementation approaches for Google Analytics, including common pitfalls and troubleshooting strategies. How to use tools like Google Tag Manager and jQuery to jumpstart your Google Analytics implementation. How to track metrics beyond page views to other critical user interactions, such as clicks on outbound links or downloads, scrolling and page engagement, usage of AJAX forms, and much more. How to incorporate additional, customized data into Google Analytics to track individual users or enrich data about their behavior. Who This Book Is For Web developers, data analysts, and marketers with a basic familiarity with Google Analytics from an end-user perspective, as well as some knowledge of HTML and JavaScript.

Cloud Analytics with Google Cloud Platform

An end-to-end guide to processing and analyzing big data using Google Cloud Platform
Author: Sanket Thodge
Publisher: Packt Publishing Ltd
ISBN: 1788838599
Category: Computers
Page: 282
View: 6265

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Combine the power of analytics and cloud computing for faster and efficient insights Key Features Master the concept of analytics on the cloud: and how organizations are using it Learn the design considerations and while applying a cloud analytics solution Design an end-to-end analytics pipeline on the cloud Book Description With the ongoing data explosion, more and more organizations all over the world are slowly migrating their infrastructure to the cloud. These cloud platforms also provide their distinct analytics services to help you get faster insights from your data. This book will give you an introduction to the concept of analytics on the cloud, and the different cloud services popularly used for processing and analyzing data. If you’re planning to adopt the cloud analytics model for your business, this book will help you understand the design and business considerations to be kept in mind, and choose the best tools and alternatives for analytics, based on your requirements. The chapters in this book will take you through the 70+ services available in Google Cloud Platform and their implementation for practical purposes. From ingestion to processing your data, this book contains best practices on building an end-to-end analytics pipeline on the cloud by leveraging popular concepts such as machine learning and deep learning. By the end of this book, you will have a better understanding of cloud analytics as a concept as well as a practical know-how of its implementation What you will learn Explore the basics of cloud analytics and the major cloud solutions Learn how organizations are using cloud analytics to improve the ROI Explore the design considerations while adopting cloud services Work with the ingestion and storage tools of GCP such as Cloud Pub/Sub Process your data with tools such as Cloud Dataproc, BigQuery, etc Over 70 GCP tools to build an analytics engine for cloud analytics Implement machine learning and other AI techniques on GCP Who this book is for This book is targeted at CIOs, CTOs, and even analytics professionals looking for various alternatives to implement their analytics pipeline on the cloud. Data professionals looking to get started with cloud-based analytics will also find this book useful. Some basic exposure to cloud platforms such as GCP will be helpful, but not mandatory.

Advances in Internet, Data & Web Technologies

The 6th International Conference on Emerging Internet, Data & Web Technologies (EIDWT-2018)
Author: Leonard Barolli,Fatos Xhafa,Nadeem Javaid,Evjola Spaho,Vladi Kolici
Publisher: Springer
ISBN: 3319759280
Category: Computers
Page: 1076
View: 4737

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This book presents original contributions on the theories and practices of emerging Internet, data and Web technologies and their applicability in businesses, engineering and academia, focusing on advances in the life-cycle exploitation of data generated from the digital ecosystem data technologies that create value, e.g. for businesses, toward a collective intelligence approach. The Internet has become the most proliferative platform for emerging large-scale computing paradigms. Among these, data and web technologies are two of the most prominent paradigms and are found in a variety of forms, such as data centers, cloud computing, mobile cloud, and mobile Web services. These technologies together create a digital ecosystem whose cornerstone is the data cycle, from capturing to processing, analyzing and visualizing. The investigation of various research and development issues in this digital ecosystem are made more pressing by the ever-increasing requirements of real-world applications that are based on storing and processing large amounts of data. The book is a valuable resource for researchers, software developers, practitioners and students interested in the field of data and web technologies.

Data Just Right

Introduction to Large-Scale Data & Analytics
Author: Michael Manoochehri
Publisher: Addison-Wesley
ISBN: 0133359077
Category: Computers
Page: 256
View: 4301

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Making Big Data Work: Real-World Use Cases and Examples, Practical Code, Detailed Solutions Large-scale data analysis is now vitally important to virtually every business. Mobile and social technologies are generating massive datasets; distributed cloud computing offers the resources to store and analyze them; and professionals have radically new technologies at their command, including NoSQL databases. Until now, however, most books on “Big Data” have been little more than business polemics or product catalogs. Data Just Right is different: It’s a completely practical and indispensable guide for every Big Data decision-maker, implementer, and strategist. Michael Manoochehri, a former Google engineer and data hacker, writes for professionals who need practical solutions that can be implemented with limited resources and time. Drawing on his extensive experience, he helps you focus on building applications, rather than infrastructure, because that’s where you can derive the most value. Manoochehri shows how to address each of today’s key Big Data use cases in a cost-effective way by combining technologies in hybrid solutions. You’ll find expert approaches to managing massive datasets, visualizing data, building data pipelines and dashboards, choosing tools for statistical analysis, and more. Throughout, the author demonstrates techniques using many of today’s leading data analysis tools, including Hadoop, Hive, Shark, R, Apache Pig, Mahout, and Google BigQuery. Coverage includes Mastering the four guiding principles of Big Data success—and avoiding common pitfalls Emphasizing collaboration and avoiding problems with siloed data Hosting and sharing multi-terabyte datasets efficiently and economically “Building for infinity” to support rapid growth Developing a NoSQL Web app with Redis to collect crowd-sourced data Running distributed queries over massive datasets with Hadoop, Hive, and Shark Building a data dashboard with Google BigQuery Exploring large datasets with advanced visualization Implementing efficient pipelines for transforming immense amounts of data Automating complex processing with Apache Pig and the Cascading Java library Applying machine learning to classify, recommend, and predict incoming information Using R to perform statistical analysis on massive datasets Building highly efficient analytics workflows with Python and Pandas Establishing sensible purchasing strategies: when to build, buy, or outsource Previewing emerging trends and convergences in scalable data technologies and the evolving role of the Data Scientist

Google Analytics Breakthrough

From Zero to Business Impact
Author: Feras Alhlou,Shiraz Asif,Eric Fettman
Publisher: John Wiley & Sons
ISBN: 1119144019
Category: Business & Economics
Page: 624
View: 5945

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A complete, start-to-finish guide to Google Analytics instrumentation and reporting Google Analytics Breakthrough is a much-needed comprehensive resource for the world's most widely adopted analytics tool. Designed to provide a complete, best-practices foundation in measurement strategy, implementation, reporting, and optimization, this book systematically demystifies the broad range of Google Analytics features and configurations. Throughout the end-to-end learning experience, you'll sharpen your core competencies, discover hidden functionality, learn to avoid common pitfalls, and develop next-generation tracking and analysis strategies so you can understand what is helping or hindering your digital performance and begin driving more success. Google Analytics Breakthrough offers practical instruction and expert perspectives on the full range of implementation and reporting skills: Learn how to campaign-tag inbound links to uncover the email, social, PPC, and banner/remarketing traffic hiding as other traffic sources and to confidently measure the ROI of each marketing channel Add event tracking to capture the many important user interactions that Google Analytics does not record by default, such as video plays, PDF downloads, scrolling, and AJAX updates Master Google Tag Manager for greater flexibility and process control in implementation Set up goals and Enhanced Ecommerce tracking to measure performance against organizational KPIs and configure conversion funnels to isolate drop-off Create audience segments that map to your audience constituencies, amplify trends, and help identify optimization opportunities Populate custom dimensions that reflect your organization, your content, and your visitors so Google Analytics can speak your language Gain a more complete view of customer behavior with mobile app and cross-device tracking Incorporate related tools and techniques: third-party data visualization, CRM integration for long-term value and lead qualification, marketing automation, phone conversion tracking, usability, and A/B testing Improve data storytelling and foster analytics adoption in the enterprise Millions of organizations have installed Google Analytics, including an estimated 67 percent of Fortune 500 companies, but deficiencies plague most implementations, and inadequate reporting practices continue to hinder meaningful analysis. By following the strategies and techniques in Google Analytics Breakthrough, you can address the gaps in your own still set, transcend the common limitations, and begin using Google Analytics for real competitive advantage. Critical contributions from industry luminaries such as Brian Clifton, Tim Ash, Bryan and Jeffrey Eisenberg, and Jim Sterne – and a foreword by Avinash Kaushik – enhance the learning experience and empower you to drive consistent, real-world improvement through analytics.

Google Analytics e Google Tag Manager para Desenvolvedores

Prefácio de Daniel Waisberg, especialista em web analytics, Google
Author: Jonathan Weber
Publisher: Novatec Editora
ISBN: 8575225022
Category: Business & Economics
Page: 352
View: 2018

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Quer você seja um profissional de marketing com habilidades de desenvolvimento ou um analista/desenvolvedor web pleno, este livro mostra como implementar o Google Analytics usando o Google Tag Manager para alavancar seu trabalho de web analytics. Quer você esteja começando do zero em um novo site, quer esteja fazendo a reengenharia ou aprimorando uma conta do Google Analytics que você herdou, este livro fornece as ferramentas de que você precisa. Há uma razão para tantas organizações usarem o Google Analytics. A coleta efetiva de dados de web analytics por meio do Google Analytics pode reduzir os custos de aquisição de clientes, converter visitantes em clientes, fornecer feedback valioso sobre novas iniciativas de produtos e oferecer ideias que vão fazer crescer sua base de clientes. Então, como o Google Tag Manager se enquadra nisso? Com uma lista crescente de recursos e a rápida adoção em todos os setores, o Google Tag Manager permite a colaboração sem precedentes entre marketing e equipes técnicas, atualizações relâmpago de seu site e a padronização das tags mais comuns para os esforços internos da empresa em rastreamento e marketing. Este livro mostra que, para conseguir os dados ricos que você está realmente buscando a fim de melhor atender às necessidades dos seus usuários, você precisa das ferramentas que o Google Tag Manager fornece para uma implementação profissional de um sistema de medição do Google Analytics em seu site. Escrito pelo “evangelista de dados” e especialista em Google Analytics Jonathan Weber e a equipe da LunaMetrics, este livro oferece conhecimento fundamental, uma coleção de receitas práticas do Google Tag Manager, as melhores práticas comprovadas e dicas de solução de problemas para colocar sua implementação em excelentes condições. Este livro aborda, entre outros assuntos: • Como implementar o Google Analytics via Google Tag Manager • Como personalizar o Google Analytics para sua situação específica • Como usar o Google Tag Manager para rastrear e analisar as interações em vários dispositivos e pontos de contato • Como extrair dados do Google Analytics e usar o Google BigQuery para analisar questões de grandes volumes de dados (Big Data)

Big Data Science & Analytics

A Hands-On Approach
Author: Arshdeep Bahga,Vijay Madisetti
Publisher: Vpt
ISBN: 9780996025546
Category: Computers
Page: 544
View: 5645

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Big data is defined as collections of datasets whose volume, velocity or variety is so large that it is difficult to store, manage, process and analyze the data using traditional databases and data processing tools. We have written this textbook to meet this need at colleges and universities, and also for big data service providers.

Pro Spark Streaming

The Zen of Real-Time Analytics Using Apache Spark
Author: Zubair Nabi
Publisher: Apress
ISBN: 148421479X
Category: Computers
Page: 230
View: 2915

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Learn the right cutting-edge skills and knowledge to leverage Spark Streaming to implement a wide array of real-time, streaming applications. This book walks you through end-to-end real-time application development using real-world applications, data, and code. Taking an application-first approach, each chapter introduces use cases from a specific industry and uses publicly available datasets from that domain to unravel the intricacies of production-grade design and implementation. The domains covered in Pro Spark Streaming include social media, the sharing economy, finance, online advertising, telecommunication, and IoT. In the last few years, Spark has become synonymous with big data processing. DStreams enhance the underlying Spark processing engine to support streaming analysis with a novel micro-batch processing model. Pro Spark Streaming by Zubair Nabi will enable you to become a specialist of latency sensitive applications by leveraging the key features of DStreams, micro-batch processing, and functional programming. To this end, the book includes ready-to-deploy examples and actual code. Pro Spark Streaming will act as the bible of Spark Streaming. What You'll Learn Discover Spark Streaming application development and best practices Work with the low-level details of discretized streams Optimize production-grade deployments of Spark Streaming via configuration recipes and instrumentation using Graphite, collectd, and Nagios Ingest data from disparate sources including MQTT, Flume, Kafka, Twitter, and a custom HTTP receiver Integrate and couple with HBase, Cassandra, and Redis Take advantage of design patterns for side-effects and maintaining state across the Spark Streaming micro-batch model Implement real-time and scalable ETL using data frames, SparkSQL, Hive, and SparkR Use streaming machine learning, predictive analytics, and recommendations Mesh batch processing with stream processing via the Lambda architecture Who This Book Is For Data scientists, big data experts, BI analysts, and data architects.

Building Big Data and Analytics Solutions in the Cloud


Author: Wei-Dong Zhu,Manav Gupta,Ven Kumar,Sujatha Perepa,Arvind Sathi,Craig Statchuk,IBM Redbooks
Publisher: IBM Redbooks
ISBN: 0738453994
Category: Computers
Page: 101
View: 4986

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Big data is currently one of the most critical emerging technologies. Organizations around the world are looking to exploit the explosive growth of data to unlock previously hidden insights in the hope of creating new revenue streams, gaining operational efficiencies, and obtaining greater understanding of customer needs. It is important to think of big data and analytics together. Big data is the term used to describe the recent explosion of different types of data from disparate sources. Analytics is about examining data to derive interesting and relevant trends and patterns, which can be used to inform decisions, optimize processes, and even drive new business models. With today's deluge of data comes the problems of processing that data, obtaining the correct skills to manage and analyze that data, and establishing rules to govern the data's use and distribution. The big data technology stack is ever growing and sometimes confusing, even more so when we add the complexities of setting up big data environments with large up-front investments. Cloud computing seems to be a perfect vehicle for hosting big data workloads. However, working on big data in the cloud brings its own challenge of reconciling two contradictory design principles. Cloud computing is based on the concepts of consolidation and resource pooling, but big data systems (such as Hadoop) are built on the shared nothing principle, where each node is independent and self-sufficient. A solution architecture that can allow these mutually exclusive principles to coexist is required to truly exploit the elasticity and ease-of-use of cloud computing for big data environments. This IBM® RedpaperTM publication is aimed at chief architects, line-of-business executives, and CIOs to provide an understanding of the cloud-related challenges they face and give prescriptive guidance for how to realize the benefits of big data solutions quickly and cost-effectively.

Digital Exhaust

What Everyone Should Know About Big Data, Digitization and Digitally Driven Innovation
Author: Dale Neef
Publisher: Pearson Education
ISBN: 013383834X
Category: Business & Economics
Page: 320
View: 6805

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Will "Big Data" supercharge the economy, tyrannize us, or both? Data Exhaust is the definitive primer for everyone who wants to understand all the implications of Big Data, digitally driven innovation, and the accelerating Internet Economy. Renowned digital expert Dale Neef clearly explains: What Big Data really is, and what's new and different about it How Big Data works, and what you need to know about Big Data technologies Where the data is coming from: how Big Data integrates sources ranging from social media to machine sensors, smartphones to financial transactions How companies use Big Data analytics to gain a more nuanced, accurate picture of their customers, their own performance, and the newest trends How governments and individual citizens can also benefit from Big Data How to overcome obstacles to success with Big Data – including poor data that can magnify human error A realistic assessment of Big Data threats to employment and personal privacy, now and in the future Neef places the Big Data phenomenon where it belongs: in the context of the broader global shift to the Internet economy, with all that implies. By doing so, he helps businesses plan Big Data strategy more effectively – and helps citizens and policymakers identify sensible policies for preventing its misuse. By conservative estimate, the global Big Data market will soar past $50 billion by 2018. But those direct expenses represent just the "tip of the iceberg" when it comes to Big Data's impact. Big Data is now of acute strategic interest for every organization that aims to succeed – and it is equally important to everyone else. Whoever you are, Data Exhaust tells you exactly what you need to know about Big Data – and what to do about it, too.

Big Data Application Architecture Q&A

A Problem - Solution Approach
Author: Nitin Sawant,Himanshu Shah
Publisher: Apress
ISBN: 1430262931
Category: Computers
Page: 172
View: 8707

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Big Data Application Architecture Pattern Recipes provides an insight into heterogeneous infrastructures, databases, and visualization and analytics tools used for realizing the architectures of big data solutions. Its problem-solution approach helps in selecting the right architecture to solve the problem at hand. In the process of reading through these problems, you will learn harness the power of new big data opportunities which various enterprises use to attain real-time profits. Big Data Application Architecture Pattern Recipes answers one of the most critical questions of this time 'how do you select the best end-to-end architecture to solve your big data problem?'. The book deals with various mission critical problems encountered by solution architects, consultants, and software architects while dealing with the myriad options available for implementing a typical solution, trying to extract insight from huge volumes of data in real–time and across multiple relational and non-relational data types for clients from industries like retail, telecommunication, banking, and insurance. The patterns in this book provide the strong architectural foundation required to launch your next big data application. The architectures for realizing these opportunities are based on relatively less expensive and heterogeneous infrastructures compared to the traditional monolithic and hugely expensive options that exist currently. This book describes and evaluates the benefits of heterogeneity which brings with it multiple options of solving the same problem, evaluation of trade-offs and validation of 'fitness-for-purpose' of the solution.

Hands-On Machine Learning on Google Cloud Platform

Implementing smart and efficient analytics using Cloud ML Engine
Author: Giuseppe Ciaburro,Kishore Ayyadevara,Alexis Perrier
Publisher: Packt Publishing Ltd
ISBN: 1788398874
Category: Computers
Page: 500
View: 2900

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Unleash Google's Cloud Platform to build, train and optimize machine learning models Key Features Get well versed in GCP pre-existing services to build your own smart models A comprehensive guide covering aspects from data processing, analyzing to building and training ML models A practical approach to produce your trained ML models and port them to your mobile for easy access Book Description Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at scale but also host them in the cloud to make predictions. This book is focused on making the most of the Google Machine Learning Platform for large datasets and complex problems. You will learn from scratch how to create powerful machine learning based applications for a wide variety of problems by leveraging different data services from the Google Cloud Platform. Applications include NLP, Speech to text, Reinforcement learning, Time series, recommender systems, image classification, video content inference and many other. We will implement a wide variety of deep learning use cases and also make extensive use of data related services comprising the Google Cloud Platform ecosystem such as Firebase, Storage APIs, Datalab and so forth. This will enable you to integrate Machine Learning and data processing features into your web and mobile applications. By the end of this book, you will know the main difficulties that you may encounter and get appropriate strategies to overcome these difficulties and build efficient systems. What you will learn Use Google Cloud Platform to build data-based applications for dashboards, web, and mobile Create, train and optimize deep learning models for various data science problems on big data Learn how to leverage BigQuery to explore big datasets Use Google’s pre-trained TensorFlow models for NLP, image, video and much more Create models and architectures for Time series, Reinforcement Learning, and generative models Create, evaluate, and optimize TensorFlow and Keras models for a wide range of applications Who this book is for This book is for data scientists, machine learning developers and AI developers who want to learn Google Cloud Platform services to build machine learning applications. Since the interaction with the Google ML platform is mostly done via the command line, the reader is supposed to have some familiarity with the bash shell and Python scripting. Some understanding of machine learning and data science concepts will be handy

Data Science on the Google Cloud Platform

Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning
Author: Valliappa Lakshmanan
Publisher: "O'Reilly Media, Inc."
ISBN: 1491974532
Category: Computers
Page: 410
View: 793

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Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guide shows developers entering the data science field how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP. Through the course of the book, you’ll work through a sample business decision by employing a variety of data science approaches. Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science. You’ll learn how to: Automate and schedule data ingest, using an App Engine application Create and populate a dashboard in Google Data Studio Build a real-time analysis pipeline to carry out streaming analytics Conduct interactive data exploration with Google BigQuery Create a Bayesian model on a Cloud Dataproc cluster Build a logistic regression machine-learning model with Spark Compute time-aggregate features with a Cloud Dataflow pipeline Create a high-performing prediction model with TensorFlow Use your deployed model as a microservice you can access from both batch and real-time pipelines

The Little Book of Cloud Computing, 2013 Edition

Including Coverage of Big Data Tools
Author: Lars Nielsen
Publisher: N.A
ISBN: 9780615751122
Category: Computers
Page: 106
View: 2246

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This concise, jargon-free volume fully explains the state-of-the-art (and the state of the industry) in Cloud Computing.

Machine Learning with Spark


Author: Rajdeep Dua,Manpreet Singh Ghotra,Nick Pentreath
Publisher: Packt Publishing Ltd
ISBN: 1785886428
Category: Computers
Page: 532
View: 5958

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Create scalable machine learning applications to power a modern data-driven business using Spark 2.x About This Book Get to the grips with the latest version of Apache Spark Utilize Spark's machine learning library to implement predictive analytics Leverage Spark's powerful tools to load, analyze, clean, and transform your data Who This Book Is For If you have a basic knowledge of machine learning and want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and Python languages. What You Will Learn Get hands-on with the latest version of Spark ML Create your first Spark program with Scala and Python Set up and configure a development environment for Spark on your own computer, as well as on Amazon EC2 Access public machine learning datasets and use Spark to load, process, clean, and transform data Use Spark's machine learning library to implement programs by utilizing well-known machine learning models Deal with large-scale text data, including feature extraction and using text data as input to your machine learning models Write Spark functions to evaluate the performance of your machine learning models In Detail This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML. Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML. By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business. Style and approach This practical tutorial with real-world use cases enables you to develop your own machine learning systems with Spark. The examples will help you combine various techniques and models into an intelligent machine learning system.

Google Cloud Platform in Action


Author: John J. Geewax,John Geewax
Publisher: Pearson Professional
ISBN: 9781617293528
Category: Computers
Page: 550
View: 682

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Cloud services make it easy to get infrastructure in a flexible and on-demand way. While there are many cloud services to choose from, Google Cloud Platform offers unique services that let you focus on building powerful applications. Google Cloud Services in Action teaches readers to build and launch web applications that scale while leveraging the Google Cloud Platform. Readers begin with the basics, learning how cloud services work, and the specifics of the Google Cloud Platform. The book includes hands-on step-by-step instruction on deploying applications, handling large amounts of data, and much more. By the end, readers will know how to build, leverage, and deploy cloud-based applications so web applications get started more quickly, suffer fewer disasters, and require less maintenance. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Machine Learning with Spark - Second Edition


Author: Rajdeep Dua,Manpreet Singh Ghotra,Nick Pentreath
Publisher: N.A
ISBN: 9781785889936
Category:
Page: 572
View: 7876

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Develop intelligent machine learning systems with SparkAbout This Book*Get to the grips with the latest version of Apache Spark*Utilize Spark's machine learning library to implement predictive analytics*Leverage Spark's powerful tools to load, analyze, clean, and transform your dataWho This Book Is ForIf you have a basic knowledge of machine learning and want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and Python languages.What You Will Learn*Get hands-on with the latest version of Spark ML*Create your first Spark program with Scala and Python*Set up and configure a development environment for Spark on your own computer, as well as on Amazon EC2*Access public machine learning datasets and use Spark to load, process, clean, and transform data*Use Spark's machine learning library to implement programs by utilizing well-known machine learning models*Deal with large-scale text data, including feature extraction and using text data as input to your machine learning models*Write Spark functions to evaluate the performance of your machine learning modelsIn DetailSpark ML is the machine learning module of Spark. It uses in-memory RDDs to process machine learning models faster for clustering, classification, and regression.This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML.Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML.

Docker: Creating Structured Containers


Author: Pethuru Raj,Jeeva S. Chelladhurai,Vinod Singh,Shrikrishna Holla,Oskar Hane,Neependra Khare,Rajdeep Dua,Vaibhav Kohli,Santosh Kumar Konduri,Russ McKendrick
Publisher: Packt Publishing Ltd
ISBN: 1786463016
Category: Computers
Page: 820
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Become an expert in the innovative containerization tool to unlock new opportunities in the way you use and deploy software About This Book Harness the power of Docker to create a robust and resilient environment in which you can generate portable, composable, scalable, and stable application containers Learn the art of container networking with elevated efficiency using Docker Better manage Docker containers using expert techniques and methods Explore the ways to keep your Docker environment secure Deploy your applications easily Who This Book Is For Whether you are a developer or a sysadmin, or anything in between, this course will give you the guidance you need to use Docker to build, test, and deploy your applications and make them easier, even enjoyable. What You Will Learn Learn how to install Docker across all the platforms along with a few troubleshooting techniques Build, push, and publish images on Docker Hub Orchestrate multiple containers with Docker Compose Test and debug applications inside a Docker container Get to know the basics of networking and see how Docker networking works Discover the tools built into Docker to gain an insight into your container's performance Take advantage of the various SaaS offerings from third parties to move monitoring away from your local infrastructure and into the cloud Familiarize yourself with third-party tools such as Traffic Authorization, Summon, sVirt, and SELinux to secure your Docker environment Integrate Docker with a wide range of cloud and configuration tools to fully realize its potential In Detail So hot off the presses, the latest buzz that has been on the tip of everyone's tongues and the topic of almost any conversation that includes containers these days is Docker! Docker has been a game-changer when it comes to virtualization. With this course, you will go from just being the person in the office who hears that buzz to the one who is tooting it around every day. This course will be a smooth journey covering Docker from scratch to finish! The first module will help you get familiarized with the fundamentals of Docker. The second module will show you how to create, deploy, and manage a virtual network for connecting containers spanning single or multiple hosts. In the third module, you'll get to grips with monitoring your Docker apps and containers - this will show you how monitoring containers and keeping a keen eye on the working of applications helps improve the overall performance of the applications that run on Docker. The purpose of our fourth module, Securing Docker, is to provide techniques and enhance your skills to secure Docker containers easily and efficiently. Finally, you'll see how to deploy Docker in production and three interesting GUI applications: Shipyard, Panamax, and Tutum. Style and approach Covering best practices to make sure you're confident with the basics, such as building, managing, and storing containers, before diving deeper into Docker security, you'll find everything you need to help you extend and integrate Docker in new and innovative ways.