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Data structures (Computer science)

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A comprehensive guide to becoming a skilled Azure DevOps engineer Key Features Explore a step-by-step approach to designing and creating a successful DevOps environment Understand how to implement continuous integration and continuous deployment pipelines on Azure Integrate and implement security, compliance, containers, and databases in your DevOps strategies Book Description Implementing Azure DevOps Solutions helps DevOps engineers and administrators to leverage Azure DevOps Services to master practices such as continuous integration and continuous delivery (CI/CD), containerization, and zero downtime deployments. This book starts with the basics of continuous integration, continuous delivery, and automated deployments. You will then learn how to apply configuration management and Infrastructure as Code (IaC) along with managing databases in DevOps scenarios. Next, you will delve into fitting security and compliance with DevOps. As you advance, you will explore how to instrument applications, and gather metrics to understand application usage and user behavior. The latter part of this book will help you implement a container build strategy and manage Azure Kubernetes Services. Lastly, you will understand how to create your own Azure DevOps organization, along with covering quick tips and tricks to confidently apply effective DevOps practices. By the end of this book, you'll have gained the knowledge you need to ensure seamless application deployments and business continuity. What you will learn Get acquainted with Azure DevOps Services and DevOps practices Implement CI/CD processes Build and deploy a CI/CD pipeline with automated testing on Azure Integrate security and compliance in pipelines Understand and implement Azure Container Services Become well versed in closing the loop from production back to development Who this book is for This DevOps book is for software developers and operations specialists interested in implementing DevOps practices for the Azure cloud. Application developers and IT professionals with some experience in software development and development practices will also find this book useful. Some familiarity with Azure DevOps basics is an added advantage.

Data structures (Compter science)

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Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key Features Learn the basics of data science and explore its possibilities and limitations Manage data science projects and assemble teams effectively even in the most challenging situations Understand management principles and approaches for data science projects to streamline the innovation process Book Description Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learn Understand the underlying problems of building a strong data science pipeline Explore the different tools for building and deploying data science solutions Hire, grow, and sustain a data science team Manage data science projects through all stages, from prototype to production Learn how to use ModelOps to improve your data science pipelines Get up to speed with the model testing techniques used in both development and production stages Who this book is for This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book.

Data structures (Compter science)

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This book takes its reader on a journey through Apache Giraph, a popular distributed graph processing platform designed to bring the power of big data processing to graph data. Designed as a step-by-step self-study guide for everyone interested in large-scale graph processing, it describes the fundamental abstractions of the system, its programming models and various techniques for using the system to process graph data at scale, including the implementation of several popular and advanced graph analytics algorithms. The book is organized as follows: Chapter 1 starts by providing a general background of the big data phenomenon and a general introduction to the Apache Giraph system, its abstraction, programming model and design architecture. Next, chapter 2 focuses on Giraph as a platform and how to use it. Based on a sample job, even more advanced topics like monitoring the Giraph application lifecycle and different methods for monitoring Giraph jobs are explained.  Chapter 3 then provides an introduction to Giraph programming, introduces the basic Giraph graph model and explains how to write Giraph programs. In turn, Chapter 4 discusses in detail the implementation of some popular graph algorithms including PageRank, connected components, shortest paths and triangle closing. Chapter 5 focuses on advanced Giraph programming, discussing common Giraph algorithmic optimizations, tunable Giraph configurations that determine the system’s utilization of the underlying resources, and how to write a custom graph input and output format. Lastly, chapter 6 highlights two systems that have been introduced to tackle the challenge of large scale graph processing, GraphX and GraphLab, and explains the main commonalities and differences between these systems and Apache Giraph. This book serves as an essential reference guide for students, researchers and practitioners in the domain of large scale graph processing. It offers step-by-step guidance, with several code examples and the complete source code available in the related github repository. Students will find a comprehensive introduction to and hands-on practice with tackling large scale graph processing problems using the Apache Giraph system, while researchers will discover thorough coverage of the emerging and ongoing advancements in big graph processing systems.

Database management. --- Big data. --- Data structures (Computer science). --- Database Management. --- Big Data/Analytics. --- Data Structures. --- Data structures (Computer science)

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A major goal of the book is to continue a good tradition - to bring together reputable researchers from different countries in order to provide a comprehensive coverage of advanced and modern topics in scheduling not yet reflected by other books. The virtual consortium of the authors has been created by using electronic exchanges; it comprises 50 authors from 18 different countries who have submitted 23 contributions to this collective product. In this sense, the volume can be added to a bookshelf with similar collective publications in scheduling, started by Coffman (1976) and successfully continued by Chretienne et al. (1995), Gutin and Punnen (2002), and Leung (2004). This volume contains four major parts that cover the following directions: the state of the art in theory and algorithms for classical and non-standard scheduling problems; new exact optimization algorithms, approximation algorithms with performance guarantees, heuristics and metaheuristics; novel models and approaches to scheduling; and, last but least, several real-life applications and case studies.

Scheduling. --- Time management --- Algorithms & data structures