Debu Panda – författare

SummaryBuilding on the bestselling first edition, EJB 3 in Action, Second Edition tackles EJB 3.2 head-on, through numerous code samples, real-life scenarios, and illustrations. This book is a fast-paced tutorial for Java EE 6 business component development using EJB 3.2, JPA 2, and CDI. Besides covering the basics of EJB 3.2, this book includes in-depth EJB 3.2 internal implementation details, best practices, design patterns, and performance tuning tips.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the BookThe EJB 3 framework provides a standard way to capture business logic in manageable server-side modules, making it easier to write, maintain, and extend Java EE applications. EJB 3.2 provides more enhancements and intelligent defaults and integrates more fully with other Java technologies, such as CDI, to make development even easier.EJB 3 in Action, Second Edition is a fast-paced tutorial for Java EE business component developers using EJB 3.2, JPA, and CDI. It tackles EJB head-on through numerous code samples, real-life scenarios, and illustrations. Beyond the basics, this book includes internal implementation details, best practices, design patterns, performance tuning tips, and various means of access including Web Services, REST Services, and WebSockets.Readers need to know Java. No prior experience with EJB or Java EE is assumed.What''s InsideFully revised for EJB 3.2POJO persistence with JPA 2.1Dependency injection and bean management with CDI 1.1Interactive application with WebSocket 1.0About the AuthorsDebu Panda, Reza Rahman, Ryan Cuprak, and Michael Remijan are seasoned Java architects, developers, authors, and community leaders. Debu and Reza coauthored the first edition of EJB 3 in Action.Table of Contents

PART 1 OVERVIEW OF THE EJB LANDSCAPE

What''s what in EJB 3A first taste of EJB

PART 2 WORKING WITH EJB COMPONENTS

Building business logic with session beansMessaging and developing MDBsEJB runtime context, dependency injection,and crosscutting logic Transactions and securityScheduling and timersExposing EJBs as web services

PART 3 USING EJB WITH JPA AND CDI

JPA entitiesManaging entitiesJPQLUsing CDI with EJB 3

PART 4 PUTTING EJB INTO ACTION

Packaging EJB 3 applicationsUsing WebSockets with EJB 3Testing and EJB

Supercharge and deploy Amazon Redshift Serverless, train and deploy machine learning models using Amazon Redshift ML, and run inference queries at scaleKey FeaturesLeverage supervised learning to build binary classification, multi-class classification, and regression modelsLearn to use unsupervised learning using the K-means clustering methodMaster the art of time series forecasting using Redshift MLPurchase of the print or Kindle book includes a free PDF eBookBook 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 learnUtilize Redshift Serverless for data ingestion, data analysis, and machine learningCreate supervised and unsupervised models and learn how to supply your own custom parametersDiscover how to use time series forecasting in your data warehouseCreate a SageMaker endpoint and use that to build a Redshift ML model for remote inferenceFind out how to operationalize machine learning in your data warehouseUse model explainability and calculate probabilities with Amazon Redshift MLWho 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.

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 modelsLearn to use unsupervised learning using the K-means clustering methodMaster the art of time series forecasting using Redshift MLPurchase of the print or Kindle book includes a free PDF eBook

Book Description

Amazon 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 learningCreate supervised and unsupervised models and learn how to supply your own custom parametersDiscover how to use time series forecasting in your data warehouseCreate a SageMaker endpoint and use that to build a Redshift ML model for remote inferenceFind out how to operationalize machine learning in your data warehouseUse model explainability and calculate probabilities with Amazon Redshift ML

Who this book is for

Data 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.