PostgreSQL 9 Administration Cookbook LITE: Configuration, Monitoring and Maintenance

Hannu Krosing, Simon Riggs, PostgreSQL

The Definitive Guide to Modernizing Applications on Google Cloud. The what, why, and how of application modernization on Google Cloud

Steve (Satish) Sangapu, Dheeraj Panyam, Jason Marston

Legacy applications, which comprise 75–80% of all enterprise applications, often end up being stuck in data centers. Modernizing these applications to make them cloud-native enables them to scale in a cloud environment without taking months or years to start seeing the benefits. This book will help software developers and solutions architects to modernize their applications on Google Cloud and transform them into cloud-native applications.This book helps you to build on your existing knowledge of enterprise application development and takes you on a journey through the six Rs: rehosting, replatforming, rearchitecting, repurchasing, retiring, and retaining. You'll learn how to modernize a legacy enterprise application on Google Cloud and build on existing assets and skills effectively. Taking an iterative and incremental approach to modernization, the book introduces the main services in Google Cloud in an easy-to-understand way that can be applied immediately to an application.By the end of this Google Cloud book, you'll have learned how to modernize a legacy enterprise application by exploring various interim architectures and tooling to develop a cloud-native microservices-based application.

Atlassian DevOps Toolchain Cookbook. Recipes for building, automating, and managing applications with Jira, Bitbucket Pipelines, and more

Robert Wen, Alex Ortiz, Edward Gaile, Rodney Nissen

Implementing DevOps practices and toolchains for automated testing and deployment can accelerate product development with minimal errors in the production environment. However, creating DevOps toolchains by integrating tools from various vendors presents challenges for both administrators and developers. Written by four well-known experts from the Atlassian community, this book addresses the complexities of DevOps toolchain creation and integration by leveraging Atlassian’s Open DevOps solution.Starting with a holistic overview of the DevOps and Atlassian Open DevOps solution, you’ll learn to integrate Jira with other tools. You’ll then find out how to create and integrate a CI/CD pipeline in Bitbucket for automated testing and deployment to Docker containers. With step-by-step guidance, you’ll connect Jira and Bitbucket with other tools, such as Snyk for security and SonarQube for testing, to form an extensive toolchain. You’ll also learn how Compass uses CheckOps for observability and how to use Confluence for documentation and reporting. Finally, you’ll leverage Opsgenie’s ChatOps functionality to enhance collaboration between developers and operations teams.By the end of this book, you’ll be able to establish your DevOps toolchain by integrating Atlassian tools to automate and optimize the software development lifecycle and beyond.

Puppet 2.7 Cookbook. Build reliable, scalable, secure, high-performance systems to fully utilize the power of cloud computing

John Arundel, Puppet Labs

A revolution is coming to IT operations. Configuration management tools can build servers in seconds, and automate your entire network. Tools like Puppet are essential to take full advantage of the power of cloud computing, and build reliable, scalable, secure, high-performance systems.The book takes you beyond the basics to explore the full power of Puppet, showing you in detail how to tackle a variety of real-world problems and applications. At every step it shows you exactly what commands you need to type, and includes full code samples for every recipe. The book takes the reader from a basic knowledge of Puppet to a complete and expert understanding of Puppet’s latest and most advanced features, community best practices, writing great manifests, scaling and performance, and how to extend Puppet by adding your own providers and resources.The book includes real examples from production systems and techniques that are in use in some of the world’s largest Puppet installations, including a distributed Puppet architecture and a high-performance Puppetmaster solution using Apache and Passenger.

Geospatial Data Analytics on AWS. Discover how to manage and analyze geospatial data in the cloud

Scott Bateman, Janahan Gnanachandran, Jeff DeMuth

Managing geospatial data and building location-based applications in the cloud can be a daunting task. This comprehensive guide helps you overcome this challenge by presenting the concept of working with geospatial data in the cloud in an easy-to-understand way, along with teaching you how to design and build data lake architecture in AWS for geospatial data.You’ll begin by exploring the use of AWS databases like Redshift and Aurora PostgreSQL for storing and analyzing geospatial data. Next, you’ll leverage services such as DynamoDB and Athena, which offer powerful built-in geospatial functions for indexing and querying geospatial data. The book is filled with practical examples to illustrate the benefits of managing geospatial data in the cloud. As you advance, you’ll discover how to analyze and visualize data using Python and R, and utilize QuickSight to share derived insights. The concluding chapters explore the integration of commonly used platforms like Open Data on AWS, OpenStreetMap, and ArcGIS with AWS to enable you to optimize efficiency and provide a supportive community for continuous learning.By the end of this book, you’ll have the necessary tools and expertise to build and manage your own geospatial data lake on AWS, along with the knowledge needed to tackle geospatial data management challenges and make the most of AWS services.

Python Machine Learning By Example. Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn - Third Edition

Yuxi (Hayden) Liu

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML).With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements.At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries.Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP.By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.

Learning Construct 2. Design and create your own engaging, extensible, and addictive game using Construct 2

Aryadi Subagio

Cognitive Computing with IBM Watson. Build smart applications using artificial intelligence as a service

Rob High, Tanmay Bakshi

Cognitive computing is rapidly becoming a part of every aspect of our lives through data science, machine learning (ML), and artificial intelligence (AI). It allows computing systems to learn and keep on improving as the amount of data in the system increases.This book introduces you to a whole new paradigm of computing – a paradigm that is totally different from the conventional computing of the Information Age. You will learn the concepts of ML, deep learning (DL), neural networks, and AI with the help of IBM Watson APIs. This book will help you build your own applications to understand, and solve problems, and analyze them as per your needs. You will explore various domains of cognitive computing, such as NLP, voice processing, computer vision, emotion analytics, and conversational systems.Equipped with the knowledge of machine learning concepts, how computers do their magic, and the applications of these concepts, you’ll be able to research and apply cognitive computing in your projects.