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Author: Rajesh Francis
Publisher: "O'Reilly Media, Inc."
ISBN: 1098135261
Category :
Languages : en
Pages : 523
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Book Description
Amazon Redshift powers analytic cloud data warehouses worldwide, from startups to some of the largest enterprise data warehouses available today. This practical guide thoroughly examines this managed service and demonstrates how you can use it to extract value from your data immediately, rather than go through the heavy lifting required to run a typical data warehouse. Analytic specialists Rajesh Francis, Rajiv Gupta, and Milind Oke detail Amazon Redshift's underlying mechanisms and options to help you explore out-of-the box automation. Whether you're a data engineer who wants to learn the art of the possible or a DBA looking to take advantage of machine learning-based auto-tuning, this book helps you get the most value from Amazon Redshift. By understanding Amazon Redshift features, you'll achieve excellent analytic performance at the best price, with the least effort. This book helps you: Build a cloud data strategy around Amazon Redshift as foundational data warehouse Get started with Amazon Redshift with simple-to-use data models and design best practices Understand how and when to use Redshift Serverless and Redshift provisioned clusters Take advantage of auto-tuning options inherent in Amazon Redshift and understand manual tuning options Transform your data platform for predictive analytics using Redshift ML and break silos using data sharing Learn best practices for security, monitoring, resilience, and disaster recovery Leverage Amazon Redshift integration with other AWS services to unlock additional value
Author: Rajesh Francis
Publisher: "O'Reilly Media, Inc."
ISBN: 1098135261
Category :
Languages : en
Pages : 523
Get Book Here
Book Description
Amazon Redshift powers analytic cloud data warehouses worldwide, from startups to some of the largest enterprise data warehouses available today. This practical guide thoroughly examines this managed service and demonstrates how you can use it to extract value from your data immediately, rather than go through the heavy lifting required to run a typical data warehouse. Analytic specialists Rajesh Francis, Rajiv Gupta, and Milind Oke detail Amazon Redshift's underlying mechanisms and options to help you explore out-of-the box automation. Whether you're a data engineer who wants to learn the art of the possible or a DBA looking to take advantage of machine learning-based auto-tuning, this book helps you get the most value from Amazon Redshift. By understanding Amazon Redshift features, you'll achieve excellent analytic performance at the best price, with the least effort. This book helps you: Build a cloud data strategy around Amazon Redshift as foundational data warehouse Get started with Amazon Redshift with simple-to-use data models and design best practices Understand how and when to use Redshift Serverless and Redshift provisioned clusters Take advantage of auto-tuning options inherent in Amazon Redshift and understand manual tuning options Transform your data platform for predictive analytics using Redshift ML and break silos using data sharing Learn best practices for security, monitoring, resilience, and disaster recovery Leverage Amazon Redshift integration with other AWS services to unlock additional value
Author: John Paul Mueller
Publisher: John Wiley & Sons
ISBN: 1119371848
Category : Computers
Languages : en
Pages : 384
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Book Description
Everything you need to get running with IaaS for Amazon Web Services Modern businesses rely on Infrastructure-as-a-Service (IaaS)—a setup in which someone else foots the bill to create application environments—and developers are expected to know how to write both platform-specific and IaaS-supported applications. If you're a developer who writes desktop and web applications but have little-to-no experience with cloud development, this book is an essential tool in getting started in the IaaS environment with Amazon Web Services. In Amazon Web Services For Developers For Dummies, you'll quickly and easily get up to speed on which language or platform will work best to meet a specific need, how to work with management consoles, ways you'll interact with services at the command line, how to create applications with the AWS API, and so much more. Assess development options to produce the kind of result that's actually needed Use the simplest approach to accomplish any given task Automate tasks using something as simple as the batch processing features offered by most platforms Create example applications using JavaScript, Python, and R Discover how to use the XML files that appear in the management console to fine tune your configuration Making sense of Amazon Web Services doesn't have to be as difficult as it seems—and this book shows you how.
Author: Joshua Arvin Lat
Publisher: Packt Publishing Ltd
ISBN: 1803231386
Category : Computers
Languages : en
Pages : 530
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Book Description
Work seamlessly with production-ready machine learning systems and pipelines on AWS by addressing key pain points encountered in the ML life cycle Key FeaturesGain practical knowledge of managing ML workloads on AWS using Amazon SageMaker, Amazon EKS, and moreUse container and serverless services to solve a variety of ML engineering requirementsDesign, build, and secure automated MLOps pipelines and workflows on AWSBook Description There is a growing need for professionals with experience in working on machine learning (ML) engineering requirements as well as those with knowledge of automating complex MLOps pipelines in the cloud. This book explores a variety of AWS services, such as Amazon Elastic Kubernetes Service, AWS Glue, AWS Lambda, Amazon Redshift, and AWS Lake Formation, which ML practitioners can leverage to meet various data engineering and ML engineering requirements in production. This machine learning book covers the essential concepts as well as step-by-step instructions that are designed to help you get a solid understanding of how to manage and secure ML workloads in the cloud. As you progress through the chapters, you'll discover how to use several container and serverless solutions when training and deploying TensorFlow and PyTorch deep learning models on AWS. You'll also delve into proven cost optimization techniques as well as data privacy and model privacy preservation strategies in detail as you explore best practices when using each AWS. By the end of this AWS book, you'll be able to build, scale, and secure your own ML systems and pipelines, which will give you the experience and confidence needed to architect custom solutions using a variety of AWS services for ML engineering requirements. What you will learnFind out how to train and deploy TensorFlow and PyTorch models on AWSUse containers and serverless services for ML engineering requirementsDiscover how to set up a serverless data warehouse and data lake on AWSBuild automated end-to-end MLOps pipelines using a variety of servicesUse AWS Glue DataBrew and SageMaker Data Wrangler for data engineeringExplore different solutions for deploying deep learning models on AWSApply cost optimization techniques to ML environments and systemsPreserve data privacy and model privacy using a variety of techniquesWho this book is for This book is for machine learning engineers, data scientists, and AWS cloud engineers interested in working on production data engineering, machine learning engineering, and MLOps requirements using a variety of AWS services such as Amazon EC2, Amazon Elastic Kubernetes Service (EKS), Amazon SageMaker, AWS Glue, Amazon Redshift, AWS Lake Formation, and AWS Lambda -- all you need is an AWS account to get started. Prior knowledge of AWS, machine learning, and the Python programming language will help you to grasp the concepts covered in this book more effectively.
Author: Shruti Worlikar
Publisher: Packt Publishing Ltd
ISBN: 1800561849
Category : Computers
Languages : en
Pages : 384
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Book Description
Discover how to build a cloud-based data warehouse at petabyte-scale that is burstable and built to scale for end-to-end analytical solutions Key FeaturesDiscover how to translate familiar data warehousing concepts into Redshift implementationUse impressive Redshift features to optimize development, productionizing, and operations processesFind out how to use advanced features such as concurrency scaling, Redshift Spectrum, and federated queriesBook Description Amazon Redshift is a fully managed, petabyte-scale AWS cloud data warehousing service. It enables you to build new data warehouse workloads on AWS and migrate on-premises traditional data warehousing platforms to Redshift. This book on Amazon Redshift starts by focusing on Redshift architecture, showing you how to perform database administration tasks on Redshift. You'll then learn how to optimize your data warehouse to quickly execute complex analytic queries against very large datasets. Because of the massive amount of data involved in data warehousing, designing your database for analytical processing lets you take full advantage of Redshift's columnar architecture and managed services. As you advance, you'll discover how to deploy fully automated and highly scalable extract, transform, and load (ETL) processes, which help minimize the operational efforts that you have to invest in managing regular ETL pipelines and ensure the timely and accurate refreshing of your data warehouse. Finally, you'll gain a clear understanding of Redshift use cases, data ingestion, data management, security, and scaling so that you can build a scalable data warehouse platform. By the end of this Redshift book, you'll be able to implement a Redshift-based data analytics solution and have understood the best practice solutions to commonly faced problems. What you will learnUse Amazon Redshift to build petabyte-scale data warehouses that are agile at scaleIntegrate your data warehousing solution with a data lake using purpose-built features and services on AWSBuild end-to-end analytical solutions from data sourcing to consumption with the help of useful recipesLeverage Redshift's comprehensive security capabilities to meet the most demanding business requirementsFocus on architectural insights and rationale when using analytical recipesDiscover best practices for working with big data to operate a fully managed solutionWho this book is for This book is for anyone involved in architecting, implementing, and optimizing an Amazon Redshift data warehouse, such as data warehouse developers, data analysts, database administrators, data engineers, and data scientists. Basic knowledge of data warehousing, database systems, and cloud concepts and familiarity with Redshift will be beneficial.
Author: Markus Klems
Publisher: Packt Publishing Ltd
ISBN: 1789340608
Category : Computers
Languages : en
Pages : 178
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Book Description
Discover techniques and tools for building serverless applications with AWS Lambda Key Features Learn to write, run, and deploy Lambda functions in the AWS cloud Make the most of AWS Lambda functions to build scalable and cost-efficient systems A practical guide to developing serverless services and applications in Node.js, Java, Python, and C# Book Description AWS Lambda is a part of AWS that lets you run your code without provisioning or managing servers. This enables you to deploy applications and backend services that operate with no upfront cost. This book gets you up to speed on how to build scalable systems and deploy serverless applications with AWS Lambda. The book starts with the fundamental concepts of AWS Lambda, and then teaches you how to combine your applications with other AWS services, such as AmazonAPI Gateway and DynamoDB. This book will also give a quick walk through on how to use the Serverless Framework to build larger applications that can structure code or autogenerate boilerplate code that can be used to get started quickly for increased productivity. Toward the end of the book, you will learn how to write, run, and test Lambda functions using Node.js, Java, Python, and C#. What you will learn Understand the fundamental concepts of AWS Lambda Get to grips with the Serverless Framework and how to create a serverless project Testing and debugging Lambda functions Create a stateful, serverless backend with DynamoDB Program AWS Lambda with Java, Python, and C# Program a lambda function with Node.js Who this book is for This book is primarily for IT architects and developers who want to build scalable systems and deploy serverless applications with AWS Lambda. No prior knowledge of AWS is necessary.
Author: Debu Panda
Publisher: Packt Publishing Ltd
ISBN: 1804619698
Category : Computers
Languages : en
Pages : 290
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Book Description
Supercharge and deploy Amazon Redshift Serverless, train and deploy machine learning models using Amazon Redshift ML, and run inference queries at scale Key Features Leverage supervised learning to build binary classification, multi-class classification, and regression models Learn to use unsupervised learning using the K-means clustering method Master the art of time series forecasting using Redshift ML Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionAmazon Redshift Serverless enables organizations to run petabyte-scale cloud data warehouses quickly and in a cost-effective way, enabling data science professionals to efficiently deploy cloud data warehouses and leverage easy-to-use tools to train models and run predictions. This practical guide will help developers and data professionals working with Amazon Redshift data warehouses to put their SQL knowledge to work for training and deploying machine learning models. The book begins by helping you to explore the inner workings of Redshift Serverless as well as the foundations of data analytics and types of data machine learning. With the help of step-by-step explanations of essential concepts and practical examples, you’ll then learn to build your own classification and regression models. As you advance, you’ll find out how to deploy various types of machine learning projects using familiar SQL code, before delving into Redshift ML. In the concluding chapters, you’ll discover best practices for implementing serverless architecture with Redshift. By the end of this book, you’ll be able to configure and deploy Amazon Redshift Serverless, train and deploy machine learning models using Amazon Redshift ML, and run inference queries at scale.What you will learn Utilize Redshift Serverless for data ingestion, data analysis, and machine learning Create supervised and unsupervised models and learn how to supply your own custom parameters Discover how to use time series forecasting in your data warehouse Create a SageMaker endpoint and use that to build a Redshift ML model for remote inference Find out how to operationalize machine learning in your data warehouse Use model explainability and calculate probabilities with Amazon Redshift ML Who this book is forData scientists and machine learning developers working with Amazon Redshift who want to explore its machine-learning capabilities will find this definitive guide helpful. A basic understanding of machine learning techniques and working knowledge of Amazon Redshift is needed to make the most of this book.
Author: Tristan Guillevin
Publisher: Packt Publishing Ltd
ISBN: 1838556044
Category : Computers
Languages : en
Pages : 261
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Book Description
Leverage the power of Tableau 2019.1’s new features to create impactful data visualization Key FeaturesGet up and running with the newly released features of Tableau 2019.1Create enterprise-grade dashboard and reports to communicate your insights effectivelyBegin your Tableau journey by understanding its core functionalitiesBook Description Tableau is one of the leading data visualization tools and is regularly updated with new functionalities and features. The latest release, Tableau 2019.1, promises new and advanced features related to visual analytics, reporting, dashboarding, and a host of other data visualization aspects. Getting Started with Tableau 2019.1 will get you up to speed with these additional functionalities. The book starts by highlighting the new functionalities of Tableau 2019.1, providing concrete examples of how to use them. However, if you're new to Tableau, don’t worry – you’ll be guided through the major aspects of Tableau with relevant examples. You'll learn how to connect to data, build a data source, visualize your data, build a dashboard, and even share data online. In the concluding chapters, you'll delve into advanced techniques such as creating a cross-database join and data blending. By the end of this book, you will be able to use Tableau effectively to create quick, cost-effective, and business-efficient Business Intelligence (BI) solutions. What you will learnDiscover new functionalities such as ‘Ask Data’, the new way to interact with your data using natural languageConnect tables and make transformations such as pivoting the field and splitting columnsBuild an efficient data source for analysisDesign insightful data visualization using different mark types and propertiesDevelop powerful dashboards and stories Share your work and interact with Tableau ServerUse Tableau to explore your data and find new insightsExplore Tableau's advanced features and gear up for upcoming challengesWho this book is for Existing Tableau users and BI professionals who want to get up to speed with what's new in Tableau 2019 will find this beginner-level book to be a very useful resource. Some experience of Tableau is assumed, however, the book also features introductory concepts, which even beginners can take advantage of.
Author: Michael Hsieh
Publisher: Packt Publishing Ltd
ISBN: 1801073481
Category : Computers
Languages : en
Pages : 327
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Book Description
Build production-grade machine learning models with Amazon SageMaker Studio, the first integrated development environment in the cloud, using real-life machine learning examples and code Key FeaturesUnderstand the ML lifecycle in the cloud and its development on Amazon SageMaker StudioLearn to apply SageMaker features in SageMaker Studio for ML use casesScale and operationalize the ML lifecycle effectively using SageMaker StudioBook Description Amazon SageMaker Studio is the first integrated development environment (IDE) for machine learning (ML) and is designed to integrate ML workflows: data preparation, feature engineering, statistical bias detection, automated machine learning (AutoML), training, hosting, ML explainability, monitoring, and MLOps in one environment. In this book, you'll start by exploring the features available in Amazon SageMaker Studio to analyze data, develop ML models, and productionize models to meet your goals. As you progress, you will learn how these features work together to address common challenges when building ML models in production. After that, you'll understand how to effectively scale and operationalize the ML life cycle using SageMaker Studio. By the end of this book, you'll have learned ML best practices regarding Amazon SageMaker Studio, as well as being able to improve productivity in the ML development life cycle and build and deploy models easily for your ML use cases. What you will learnExplore the ML development life cycle in the cloudUnderstand SageMaker Studio features and the user interfaceBuild a dataset with clicks and host a feature store for MLTrain ML models with ease and scaleCreate ML models and solutions with little codeHost ML models in the cloud with optimal cloud resourcesEnsure optimal model performance with model monitoringApply governance and operational excellence to ML projectsWho this book is for This book is for data scientists and machine learning engineers who are looking to become well-versed with Amazon SageMaker Studio and gain hands-on machine learning experience to handle every step in the ML lifecycle, including building data as well as training and hosting models. Although basic knowledge of machine learning and data science is necessary, no previous knowledge of SageMaker Studio and cloud experience is required.
Author: Yohan Wadia
Publisher: Packt Publishing Ltd
ISBN: 1788477170
Category : Computers
Languages : en
Pages : 349
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Book Description
Leverage this step-by-step guide to build a highly secure, fault-tolerant, and scalable Cloud environment Key Features Learn how to leverage various Amazon Web Services (AWS) components and services to build a secure, reliable, and robust environment to host your applications on. Delve into core AWS service offerings with hands-on tutorials, real-world use case scenarios, and best practices. A self-paced, systematic, and step-by-step guide to learning and implementing AWS in your own environment. Book Description Many businesses are moving from traditional data centers to AWS because of its reliability, vast service offerings, lower costs, and high rate of innovation. AWS can be used to accomplish a variety of both simple and tedious tasks. Whether you are a seasoned system admin or a rookie, this book will help you to learn all the skills you need to work with the AWS cloud. This book guides you through some of the most popular AWS services, such as EC2, Elastic Beanstalk, EFS, CloudTrail, Redshift, EMR, Data Pipeline, and IoT using a simple, real-world, application-hosting example. This book will also enhance your application delivery skills with the latest AWS services, such as CodeCommit, CodeDeploy, and CodePipeline, to provide continuous delivery and deployment, while also securing and monitoring your environment's workflow. Each chapter is designed to provide you with maximal information about each AWS service, coupled with easy to follow, hands-on steps, best practices, tips, and recommendations. By the end of the book, you will be able to create a highly secure, fault-tolerant, and scalable environment for your applications to run on. What you will learn Take an in-depth look at what's new with AWS, along with how to effectively manage and automate your EC2 infrastructure with AWS Systems Manager Deploy and scale your applications with ease using AWS Elastic Beanstalk and Amazon Elastic File System Secure and govern your environments using AWS CloudTrail, AWS Config, and AWS Shield Learn the DevOps way using a combination of AWS CodeCommit, AWS CodeDeploy, and AWS CodePipeline Run big data analytics and workloads using Amazon EMR and Amazon Redshift Learn to back up and safeguard your data using AWS Data Pipeline Get started with the Internet of Things using AWS IoT and AWS Greengrass Who this book is for This book is for those who want to learn and leverage the rich plethora of services provided by AWS. Although no prior experience with AWS is required, it is recommended that you have some hands-on experience of Linux, Web Services, and basic networking.
Author: Yohan Wadia
Publisher: Packt Publishing Ltd
ISBN: 1788831063
Category : Computers
Languages : en
Pages : 674
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Book Description
Work through exciting recipes to administer your AWS cloud Key FeaturesBuild secure environments using AWS components and servicesExplore core AWS features with real-world applications and best practicesDesign and build Lambda functions using real-world examplesBook Description With this Learning Path, you’ll explore techniques to easily manage applications on the AWS cloud. You’ll begin with an introduction to serverless computing, its advantages, and the fundamentals of AWS. The following chapters will guide you on how to manage multiple accounts by setting up consolidated billing, enhancing your application delivery skills, with the latest AWS services such as CodeCommit, CodeDeploy, and CodePipeline to provide continuous delivery and deployment, while also securing and monitoring your environment's workflow. It’ll also add to your understanding of the services AWS Lambda provides to developers. To refine your skills further, it demonstrates how to design, write, test, monitor, and troubleshoot Lambda functions. By the end of this Learning Path, you’ll be able to create a highly secure, fault-tolerant, and scalable environment for your applications. This Learning Path includes content from the following Packt products: AWS Administration: The Definitive Guide, Second Edition by Yohan WadiaAWS Administration Cookbook by Rowan Udell, Lucas ChanMastering AWS Lambda by Yohan Wadia, Udita GuptaWhat you will learnExplore the benefits of serverless computing and applicationsDeploy apps with AWS Elastic Beanstalk and Amazon Elastic File SystemSecure environments with AWS CloudTrail, AWSConfig, and AWS ShieldRun big data analytics with Amazon EMR and Amazon RedshiftBack up and safeguard data using AWS Data PipelineCreate monitoring and alerting dashboards using CloudWatchEffectively monitor and troubleshoot serverless applications with AWSDesign serverless apps via AWS Lambda, DynamoDB, and API GatewayWho this book is for This Learning Path is specifically designed for IT system and network administrators, AWS architects, and DevOps engineers who want to effectively implement AWS in their organization and easily manage daily activities. Familiarity with Linux, web services, cloud computing platforms, virtualization, networking, and other administration-related tasks will assist in understanding the concepts in the book. Prior hands-on experience with AWS core services such as EC2, IAM, S3, and programming languages, such as Node.Js, Java, and C#, will also prove beneficial.
