Author: Stefan Bauer
Publisher: Packt Publishing Ltd
Release Date: 2013-06-14
Genre: Business & Economics
Getting Started With Amazon Redshift is a step-by-step, practical guide to the world of Redshift. Learn to load, manage, and query data on Redshift.This book is for CIOs, enterprise architects, developers, and anyone else who needs to get familiar with RedShift. The CIO will gain an understanding of what their technical staff is working on; the technical implementation personnel will get an in-depth view of the technology, and what it will take to implement their own solutions.
Learn to leverage Amazon's powerful platform for your predictive analytics needs About This Book Create great machine learning models that combine the power of algorithms with interactive tools without worrying about the underlying complexity Learn the What's next? of machine learning—machine learning on the cloud—with this unique guide Create web services that allow you to perform affordable and fast machine learning on the cloud Who This Book Is For This book is intended for data scientists and managers of predictive analytics projects; it will teach beginner- to advanced-level machine learning practitioners how to leverage Amazon Machine Learning and complement their existing Data Science toolbox. No substantive prior knowledge of Machine Learning, Data Science, statistics, or coding is required. What You Will Learn Learn how to use the Amazon Machine Learning service from scratch for predictive analytics Gain hands-on experience of key Data Science concepts Solve classic regression and classification problems Run projects programmatically via the command line and the Python SDK Leverage the Amazon Web Service ecosystem to access extended data sources Implement streaming and advanced projects In Detail Predictive analytics is a complex domain requiring coding skills, an understanding of the mathematical concepts underpinning machine learning algorithms, and the ability to create compelling data visualizations. Following AWS simplifying Machine learning, this book will help you bring predictive analytics projects to fruition in three easy steps: data preparation, model tuning, and model selection. This book will introduce you to the Amazon Machine Learning platform and will implement core data science concepts such as classification, regression, regularization, overfitting, model selection, and evaluation. Furthermore, you will learn to leverage the Amazon Web Service (AWS) ecosystem for extended access to data sources, implement realtime predictions, and run Amazon Machine Learning projects via the command line and the Python SDK. Towards the end of the book, you will also learn how to apply these services to other problems, such as text mining, and to more complex datasets. Style and approach This book will include use cases you can relate to. In a very practical manner, you will explore the various capabilities of Amazon Machine Learning services, allowing you to implementing them in your environment with consummate ease.
This book is a practical guide to developing, administering, and managing applications and infrastructures with AWS. With this, you'll be able to create, design, and manage an entire application life cycle on AWS by using the AWS SDKs, APIs, and the AWS Management Console. You'll start with the basics of the AWS development platform and look into creating stable and scalable infrastructures using EC2, EBS, and Elastic Load Balancers. You'll then deep-dive into designing and developing your own web app and learn about the alarm mechanism, disaster recovery plan, and connecting AWS services through REST-based APIs. Following this, you'll get to grips with CloudFormation, auto scaling, bootstrap AWS EC2 instances, automation and deployment with Chef, and develop your knowledge of big data and Apache Hadoop on AWS Cloud. At the end, you'll have learned about AWS billing, cost-control architecture designs, AWS Security features and troubleshooting methods, and developed AWS-centric applications based on an underlying AWS infrastructure.
Cloud Computing is a "daily spoken" and most commonly used terminology in every forum. Every conversation with a CIO has a reference to cloud computing. The objective of this book is to simplify cloud computing, explain what is cloud computing’s impact on Enterprise IT and how business should be prepared to leverage the benefits of cloud in the right way. THIS BOOK WILL BE YOUR KNOWLEDGE GATEWAY TO CLOUD COMPUTING AND NEXT GENERATION INFORMATION TECHNOLOGY MANAGEMENT. Besides core cloud computing concepts and process you will also be presented with latest technologies and tools available today to onboard your assets to cloud and manage cloud better. A cloud computing professional who has worked with several cloud providers and organizations of varied sizes writes this book so expect real life examples, techniques, process and working models for every scenario in strategizing, migrating and managing IT infrastructure in the cloud. The book is carefully structured to gradually take the readers through the basics of cloud computing concepts, terminologies, implementation and management techniques through traditional IT management so that readers can easily connect ends. Several transformational, working models and best practices are discussed throughout the book. If you are looking for a book on cloud computing, #thecloudbook is the right book for you. If you have already purchased any books on cloud computing, read #thecloudbook and then go through the other books, you will understand the other books better. #thecloudbook is a must for every IT professional.
Author: Segall, Richard S.
Publisher: IGI Global
Release Date: 2018-01-05
The digital age has presented an exponential growth in the amount of data available to individuals looking to draw conclusions based on given or collected information across industries. Challenges associated with the analysis, security, sharing, storage, and visualization of large and complex data sets continue to plague data scientists and analysts alike as traditional data processing applications struggle to adequately manage big data. The Handbook of Research on Big Data Storage and Visualization Techniques is a critical scholarly resource that explores big data analytics and technologies and their role in developing a broad understanding of issues pertaining to the use of big data in multidisciplinary fields. Featuring coverage on a broad range of topics, such as architecture patterns, programing systems, and computational energy, this publication is geared towards professionals, researchers, and students seeking current research and application topics on the subject.
If you have interest in DynamoDB and want to know what DynamoDB is all about and become proficient in using it, this is the book for you. If you are an intermediate user who wishes to enhance your knowledge of DynamoDB, this book is aimed at you. Basic familiarity with programming, NoSQL, and cloud computing concepts would be helpful.
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.