Das anhaltende Interesse an der Theorie monetArer Integration ist einerseits dem europAischen EinigungsprozeA zu verdanken, andererseits der InstabilitAt des WeltwAhrungssystems seit dem Zusammenbruch der Bretton-Woods-Vereinbarung. Die vorherrschende Theorie des optimalen WAhrungsraumes hat sich jedoch angesichts neuerer Entwicklungen in der Theorie der Wirtschaftspolitik sowie in der Theorie des Wechselkurses als zu eng und methodisch fragwA1/4rdig erwiesen. Das Buch gibt einen umfassenden, auch fA1/4r AngehArige anderer sozialwissenschaftlichen Disziplinen gut lesbaren Aoeberblick A1/4ber den Stand der Forschung zur monetAren Integration im allgemeinen und zur europAischen WAhrungsintegration im besonderen. Es gibt darA1/4ber hinaus Anregungen fA1/4r weiterfA1/4hrende Untersuchungen, z.B. zur Rolle der Arbeitsmarktverfassungen oder der Fiskal- und der Sozialpolitik.

Learn data architecture essentials and prepare for the Salesforce Certified Data Architect exam with the help of tips and mock test questions Key Features * Leverage data modelling, Salesforce database design, and techniques for effective data design * Learn master data management, Salesforce data management, and how to include considerations * Get to grips with large data volumes, performance tuning, and poor performance mitigation techniques Book Description The Salesforce Data Architect is a prerequisite exam for the Application Architect half of the Salesforce Certified Technical Architect credential. This book offers a complete, up-to-date coverage of the Salesforce Data Architect exam so you can take it with confidence. The book is written in a clear, succinct way with self-assessment and practice exam questions, covering all topics necessary to help you pass the exam with ease. You'll understand the theory around Salesforce data modeling, database design, master data management (MDM), Salesforce data management (SDM), and data governance. Additionally, performance considerations associated with large data volumes will be covered. You'll also get to grips with data migration and understand the supporting theory needed to achieve Salesforce Data Architect certification. By the end of this Salesforce book, you'll have covered everything you need to pass the Salesforce Data Architect certification exam and have a handy, on-the-job desktop reference guide to re-visit the concepts. What you will learn * Understand the topics relevant to passing the Data Architect exam * Explore specialist areas such as large data volumes * Test your knowledge with the help of exam-like questions * Pick up useful tips and tricks that can be referred to time and again * Understand the reasons underlying the way Salesforce data management works * Discover the techniques that are available for loading massive amounts of data Who This Book Is For This book is for both aspiring Salesforce data architects and those already familiar with Salesforce data architecture who want to pass the exam and have a reference guide to revisit the material as part of their day-to-day job. Working knowledge of the Salesforce platform is assumed, alongside a clear understanding of Salesforce architectural concepts.

Build an end-to-end business solution in the cognitive automation lifecycle and explore UiPath Document Understanding, UiPath AI Center, and Druid Key Features Explore out-of-the-box (OOTB) AI Models in UiPath Learn how to deploy, manage, and continuously improve machine learning models using UiPath AI Center Deploy UiPath-integrated chatbots and master UiPath Document Understanding Book DescriptionArtificial intelligence (AI) enables enterprises to optimize business processes that are probabilistic, highly variable, and require cognitive abilities with unstructured data. Many believe there is a steep learning curve with AI, however, the goal of our book is to lower the barrier to using AI. This practical guide to AI with UiPath will help RPA developers and tech-savvy business users learn how to incorporate cognitive abilities into business process optimization. With the hands-on approach of this book, you'll quickly be on your way to implementing cognitive automation to solve everyday business problems. Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book will help you understand the power of AI and give you an overview of the relevant out-of-the-box models. You'll learn about cognitive AI in the context of RPA, the basics of machine learning, and how to apply cognitive automation within the development lifecycle. You'll then put your skills to test by building three use cases with UiPath Document Understanding, UiPath AI Center, and Druid. By the end of this AI book, you'll be able to build UiPath automations with the cognitive capabilities of intelligent document processing, machine learning, and chatbots, while understanding the development lifecycle. What you will learn Discover how to bridge the gap between RPA and cognitive automation Understand how to configure, deploy, and maintain ML models in UiPath Explore OOTB models to manage documents, chats, emails, and more Prepare test data and test cases for user acceptance testing (UAT) Build a UiPath automation to act upon Druid responses Find out how to connect custom models to RPA Who this book is forAI Engineers and RPA developers who want to upskill and deploy out-of-the-box models using UiPath's AI capabilities will find this guide useful. A basic understanding of robotic process automation and machine learning will be beneficial but not mandatory to get started with this UiPath book.

Do you enjoy completing puzzles? Perhaps one of the most challenging (yet rewarding) puzzles is delivering a successful data warehouse suitable for data mining and analytics. The Analytical Puzzle describes an unbiased, practical, and comprehensive approach to building a data warehouse which will lead to an increased level of business intelligence within your organisation. New technologies continuously impact this approach and therefore this book explains how to leverage big data, cloud computing, data warehouse appliances, data mining, predictive analytics, data visualisation and mobile devices. This book describes an unbiased, practical, and comprehensive approach to building a data warehouse which will lead to an increased level of business intelligence within your organisation. New technologies continuously impact this approach and therefore this book explains how to leverage big data, cloud computing, data warehouse appliances, data mining, predictive analytics, data visualisation and mobile devices.

Understand the fundamentals of Kubernetes deployment on Azure with a learn-by-doing approach Key Features Get to grips with the fundamentals of containers and Kubernetes Deploy containerized applications using the Kubernetes platform Learn how you can scale your workloads and secure your application running in Azure Kubernetes Service Book DescriptionContainers and Kubernetes containers facilitate cloud deployments and application development by enabling efficient versioning with improved security and portability. With updated chapters on role-based access control, pod identity, storing secrets, and network security in AKS, this third edition begins by introducing you to containers, Kubernetes, and Azure Kubernetes Service (AKS), and guides you through deploying an AKS cluster in different ways. You will then delve into the specifics of Kubernetes by deploying a sample guestbook application on AKS and installing complex Kubernetes apps using Helm. With the help of real-world examples, you'll also get to grips with scaling your applications and clusters. As you advance, you'll learn how to overcome common challenges in AKS and secure your applications with HTTPS. You will also learn how to secure your clusters and applications in a dedicated section on security. In the final section, you'll learn about advanced integrations, which give you the ability to create Azure databases and run serverless functions on AKS as well as the ability to integrate AKS with a continuous integration and continuous delivery (CI/CD) pipeline using GitHub Actions. By the end of this Kubernetes book, you will be proficient in deploying containerized workloads on Microsoft Azure with minimal management overhead. What you will learn Plan, configure, and run containerized applications in production. Use Docker to build applications in containers and deploy them on Kubernetes. Monitor the AKS cluster and the application. Monitor your infrastructure and applications in Kubernetes using Azure Monitor. Secure your cluster and applications using Azure-native security tools. Connect an app to the Azure database. Store your container images securely with Azure Container Registry. Install complex Kubernetes applications using Helm. Integrate Kubernetes with multiple Azure PaaS services, such as databases, Azure Security Center, and Functions. Use GitHub Actions to perform continuous integration and continuous delivery to your cluster. Who this book is forIf you are an aspiring DevOps professional, system administrator, developer, or site reliability engineer interested in learning how to get the most out of containers and Kubernetes, then this book is for you.

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 Features Discover how to translate familiar data warehousing concepts into Redshift implementation Use impressive Redshift features to optimize development, productionizing, and operations processes Find out how to use advanced features such as concurrency scaling, Redshift Spectrum, and federated queries Book DescriptionAmazon 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 learn Use Amazon Redshift to build petabyte-scale data warehouses that are agile at scale Integrate your data warehousing solution with a data lake using purpose-built features and services on AWS Build end-to-end analytical solutions from data sourcing to consumption with the help of useful recipes Leverage Redshift's comprehensive security capabilities to meet the most demanding business requirements Focus on architectural insights and rationale when using analytical recipes Discover best practices for working with big data to operate a fully managed solution Who this book is forThis 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.

Quickly build and deploy massive data pipelines and improve productivity using Azure Databricks Key Features Get to grips with the distributed training and deployment of machine learning and deep learning models Learn how ETLs are integrated with Azure Data Factory and Delta Lake Explore deep learning and machine learning models in a distributed computing infrastructure Book DescriptionMicrosoft Azure Databricks helps you to harness the power of distributed computing and apply it to create robust data pipelines, along with training and deploying machine learning and deep learning models. Databricks' advanced features enable developers to process, transform, and explore data. Distributed Data Systems with Azure Databricks will help you to put your knowledge of Databricks to work to create big data pipelines. The book provides a hands-on approach to implementing Azure Databricks and its associated methodologies that will make you productive in no time. Complete with detailed explanations of essential concepts, practical examples, and self-assessment questions, you'll begin with a quick introduction to Databricks core functionalities, before performing distributed model training and inference using TensorFlow and Spark MLlib. As you advance, you'll explore MLflow Model Serving on Azure Databricks and implement distributed training pipelines using HorovodRunner in Databricks. Finally, you'll discover how to transform, use, and obtain insights from massive amounts of data to train predictive models and create entire fully working data pipelines. By the end of this MS Azure book, you'll have gained a solid understanding of how to work with Databricks to create and manage an entire big data pipeline. What you will learn Create ETLs for big data in Azure Databricks Train, manage, and deploy machine learning and deep learning models Integrate Databricks with Azure Data Factory for extract, transform, load (ETL) pipeline creation Discover how to use Horovod for distributed deep learning Find out how to use Delta Engine to query and process data from Delta Lake Understand how to use Data Factory in combination with Databricks Use Structured Streaming in a production-like environment Who this book is forThis book is for software engineers, machine learning engineers, data scientists, and data engineers who are new to Azure Databricks and want to build high-quality data pipelines without worrying about infrastructure. Knowledge of Azure Databricks basics is required to learn the concepts covered in this book more effectively. A basic understanding of machine learning concepts and beginner-level Python programming knowledge is also recommended.

Build and design multiple types of applications that are cross-language, platform, and cost-effective by understanding core Azure principles and foundational concepts Key Features Get familiar with the different design patterns available in Microsoft Azure Develop Azure cloud architecture and a pipeline management system Get to know the security best practices for your Azure deployment Book DescriptionThanks to its support for high availability, scalability, security, performance, and disaster recovery, Azure has been widely adopted to create and deploy different types of application with ease. Updated for the latest developments, this third edition of Azure for Architects helps you get to grips with the core concepts of designing serverless architecture, including containers, Kubernetes deployments, and big data solutions. You'll learn how to architect solutions such as serverless functions, you'll discover deployment patterns for containers and Kubernetes, and you'll explore large-scale big data processing using Spark and Databricks. As you advance, you'll implement DevOps using Azure DevOps, work with intelligent solutions using Azure Cognitive Services, and integrate security, high availability, and scalability into each solution. Finally, you'll delve into Azure security concepts such as OAuth, OpenConnect, and managed identities. By the end of this book, you'll have gained the confidence to design intelligent Azure solutions based on containers and serverless functions. What you will learn Understand the components of the Azure cloud platform Use cloud design patterns Use enterprise security guidelines for your Azure deployment Design and implement serverless and integration solutions Build efficient data solutions on Azure Understand container services on Azure Who this book is forIf you are a cloud architect, DevOps engineer, or a developer looking to learn about the key architectural aspects of the Azure cloud platform, this book is for you. A basic understanding of the Azure cloud platform will help you grasp the concepts covered in this book more effectively.

Learn Azure's cloud capabilities with the help of this introductory guide to employing Azure for your cloud infrastructure needs. Key Features Get a clear overview of Azure's capabilities and benefits, and learn how to get started efficiently Develop the ability to opt for cloud architecture and design that best fits your organization Leverage Azure opportunities for cost savings and optimization Book DescriptionMicrosoft Azure is a powerful cloud computing platform that offers a multitude of services and capabilities for organizations of any size moving to a cloud strategy. Azure Strategy and Implementation Guide Third Edition encapsulates the entire spectrum of measures involved in Azure deployment that includes understanding Azure fundamentals, choosing a suitable cloud architecture, building on design principles, becoming familiar with Azure DevOps, and learning best practices for optimization and management. The book begins by introducing you to the Azure cloud platform and demonstrating the substantial scope of digital transformation and innovation that can be achieved by leveraging Azure's capabilities. The guide further acquaints you with practical insights on application modernization, Azure Infrastructure as a Service (IaaS) deployment, infrastructure management, key application architectures, best practices of Azure DevOps, and Azure automation. By the end of this book, you will be proficient in driving Azure operations right from the planning and cloud migration stage to cost management and troubleshooting. What you will learn Deploy and run Azure infrastructure services Carry out detailed planning for migrating applications to the cloud with Azure Move underlying code class structure into a serverless model Use a gateway to isolate your services and applications Define roles and responsibilities in DevOps Implement release & deployment coordination and automation Who this book is forAzure Strategy and Implementation Guide Third Edition is designed to benefit Azure architects, cloud solution architects, Azure developers, Azure administrators, and anyone who wants to develop an expertise in operating and administering the Azure cloud. A basic familiarity with operating systems and databases will help you grasp the concepts covered in this book.

Learn how to combine SQL Server's analytics with Azure's flexibility and hybrid connectivity to achieve industry-leading performance and manageability for your cloud database. Key Features Understand platform availability for SQL Server in Azure Explore the benefits and deployment choices offered by SQL IaaS Get to grips with deploying SQL Server on the Linux development ecosystem Book DescriptionDeploying SQL Server on Azure virtual machines allows you to work on full versions of SQL Server in the cloud without having to maintain on-premises hardware. The book begins by introducing you to the SQL portfolio in Azure and takes you through SQL Server IaaS scenarios, before explaining the factors that you need to consider while choosing an OS for SQL Server in Azure VMs. As you progress through the book, you'll explore different VM options and deployment choices for IaaS and understand platform availability, migration tools, and best practices in Azure. In later chapters, you'll learn how to configure storage to achieve optimized performance. Finally, you'll get to grips with the concept of Azure Hybrid Benefit and find out how you can use it to maximize the value of your existing on-premises SQL Server. By the end of this book, you'll be proficient in administering SQL Server on Microsoft Azure and leveraging the tools required for its deployment. What you will learn Choose an operating system for SQL Server in Azure VMs Use the Azure Management Portal to facilitate the deployment process Verify connectivity and network latency in cloud Configure storage for optimal performance and connectivity Explore various disaster recovery options for SQL Server in Azure Optimize SQL Server on Linux Discover how to back up databases to a URL Who this book is forSQL Server on Azure VMs is for you if you are a developer, data enthusiast, or anyone who wants to migrate SQL Server databases to Azure virtual machines. Basic familiarity with SQL Server and managed identities for Azure resources will be a plus.

Learn to extract actionable insights from your big data in real time using a range of Microsoft Azure features Key Features Updated with the latest features and new additions to Microsoft Azure Master the fundamentals of cloud analytics using Azure Learn to use Azure Synapse Analytics (formerly known as Azure SQL Data Warehouse) to derive real-time customer insights Book DescriptionCloud Analytics with Microsoft Azure serves as a comprehensive guide for big data analysis and processing using a range of Microsoft Azure features. This book covers everything you need to build your own data warehouse and learn numerous techniques to gain useful insights by analyzing big data The book begins by introducing you to the power of data with big data analytics, the Internet of Things (IoT), machine learning, artificial intelligence, and DataOps. You will learn about cloud-scale analytics and the services Microsoft Azure offers to empower businesses to discover insights. You will also be introduced to the new features and functionalities added to the modern data warehouse. Finally, you will look at two real-world business use cases to demonstrate high-level solutions using Microsoft Azure. The aim of these use cases will be to illustrate how real-time data can be analyzed in Azure to derive meaningful insights and make business decisions. You will learn to build an end-to-end analytics pipeline on the cloud with machine learning and deep learning concepts. By the end of this book, you will be proficient in analyzing large amounts of data with Azure and using it effectively to benefit your organization. What you will learn Explore the concepts of modern data warehouses and data pipelines Discover unique design considerations while applying a cloud analytics solution Design an end-to-end analytics pipeline on the cloud Differentiate between structured, semi-structured, and unstructured data Choose a cloud-based service for your data analytics solutions Use Azure services to ingest, store, and analyze data of any scale Who this book is forThis book is designed to benefit software engineers, Azure developers, cloud consultants, and anyone who is keen to learn the process of deriving business insights from huge amounts of data using Azure. Though not necessary, a basic understanding of data analytics concepts such as data streaming, data types, the machine learning life cycle, and Docker containers will help you get the most out of the book.

Solve real-world data problems and create data-driven workflows for easy data movement and processing at scale with Azure Data Factory Key Features Learn how to load and transform data from various sources, both on-premises and on cloud Use Azure Data Factory's visual environment to build and manage hybrid ETL pipelines Discover how to prepare, transform, process, and enrich data to generate key insights Book DescriptionAzure Data Factory (ADF) is a modern data integration tool available on Microsoft Azure. This Azure Data Factory Cookbook helps you get up and running by showing you how to create and execute your first job in ADF. You'll learn how to branch and chain activities, create custom activities, and schedule pipelines. This book will help you to discover the benefits of cloud data warehousing, Azure Synapse Analytics, and Azure Data Lake Gen2 Storage, which are frequently used for big data analytics. With practical recipes, you'll learn how to actively engage with analytical tools from Azure Data Services and leverage your on-premise infrastructure with cloud-native tools to get relevant business insights. As you advance, you'll be able to integrate the most commonly used Azure Services into ADF and understand how Azure services can be useful in designing ETL pipelines. The book will take you through the common errors that you may encounter while working with ADF and show you how to use the Azure portal to monitor pipelines. You'll also understand error messages and resolve problems in connectors and data flows with the debugging capabilities of ADF. By the end of this book, you'll be able to use ADF as the main ETL and orchestration tool for your data warehouse or data platform projects. What you will learn Create an orchestration and transformation job in ADF Develop, execute, and monitor data flows using Azure Synapse Create big data pipelines using Azure Data Lake and ADF Build a machine learning app with Apache Spark and ADF Migrate on-premises SSIS jobs to ADF Integrate ADF with commonly used Azure services such as Azure ML, Azure Logic Apps, and Azure Functions Run big data compute jobs within HDInsight and Azure Databricks Copy data from AWS S3 and Google Cloud Storage to Azure Storage using ADF's built-in connectors Who this book is forThis book is for ETL developers, data warehouse and ETL architects, software professionals, and anyone who wants to learn about the common and not-so-common challenges faced while developing traditional and hybrid ETL solutions using Microsoft's Azure Data Factory. You'll also find this book useful if you are looking for recipes to improve or enhance your existing ETL pipelines. Basic knowledge of data warehousing is expected.

Develop modern solutions with Snowflake's unique architecture and integration capabilities; process bulk and real-time data into a data lake; and leverage time travel, cloning, and data-sharing features to optimize data operations Key Features Build and scale modern data solutions using the all-in-one Snowflake platform Perform advanced cloud analytics for implementing big data and data science solutions Make quicker and better-informed business decisions by uncovering key insights from your data Book DescriptionSnowflake is a unique cloud-based data warehousing platform built from scratch to perform data management on the cloud. This book introduces you to Snowflake's unique architecture, which places it at the forefront of cloud data warehouses. You'll explore the compute model available with Snowflake, and find out how Snowflake allows extensive scaling through the virtual warehouses. You will then learn how to configure a virtual warehouse for optimizing cost and performance. Moving on, you'll get to grips with the data ecosystem and discover how Snowflake integrates with other technologies for staging and loading data. As you progress through the chapters, you will leverage Snowflake's capabilities to process a series of SQL statements using tasks to build data pipelines and find out how you can create modern data solutions and pipelines designed to provide high performance and scalability. You will also get to grips with creating role hierarchies, adding custom roles, and setting default roles for users before covering advanced topics such as data sharing, cloning, and performance optimization. By the end of this Snowflake book, you will be well-versed in Snowflake's architecture for building modern analytical solutions and understand best practices for solving commonly faced problems using practical recipes. What you will learn Get to grips with data warehousing techniques aligned with Snowflake's cloud architecture Broaden your skills as a data warehouse designer to cover the Snowflake ecosystem Transfer skills from on-premise data warehousing to the Snowflake cloud analytics platform Optimize performance and costs associated with a Snowflake solution Stage data on object stores and load it into Snowflake Secure data and share it efficiently for access Manage transactions and extend Snowflake using stored procedures Extend cloud data applications using Spark Connector Who this book is forThis book is for data warehouse developers, data analysts, database administrators, and anyone involved in designing, implementing, and optimizing a Snowflake data warehouse. Knowledge of data warehousing and database and cloud concepts will be useful. Basic familiarity with Snowflake is beneficial, but not necessary.

A comprehensive guide to understanding key techniques for architecture and hardware planning, monitoring, replication, backups, and decoupling Key Features Newly updated edition, covering the latest PostgreSQL 12 features with hands-on industry-driven recipes Create a PostgreSQL cluster that stays online even when disaster strikes Learn how to avoid costly downtime and data loss that can ruin your business Book DescriptionDatabases are nothing without the data they store. In the event of an outage or technical catastrophe, immediate recovery is essential. This updated edition ensures that you will learn the important concepts related to node architecture design, as well as techniques such as using repmgr for failover automation. From cluster layout and hardware selection to software stacks and horizontal scalability, this PostgreSQL cookbook will help you build a PostgreSQL cluster that will survive crashes, resist data corruption, and grow smoothly with customer demand. You'll start by understanding how to plan a PostgreSQL database architecture that is resistant to outages and scalable, as it is the scaffolding on which everything rests. With the bedrock established, you'll cover the topics that PostgreSQL database administrators need to know to manage a highly available cluster. This includes configuration, troubleshooting, monitoring and alerting, backups through proxies, failover automation, and other considerations that are essential for a healthy PostgreSQL cluster. Later, you'll learn to use multi-master replication to maximize server availability. Later chapters will guide you through managing major version upgrades without downtime. By the end of this book, you'll have learned how to build an efficient and adaptive PostgreSQL 12 database cluster. What you will learn Understand how to protect data with PostgreSQL replication tools Focus on hardware planning to ensure that your database runs efficiently Reduce database resource contention with connection pooling Monitor and visualize cluster activity with Nagios and the TIG (Telegraf, InfluxDB, Grafana) stack Construct a robust software stack that can detect and avert outages Use multi-master to achieve an enduring PostgreSQL cluster Who this book is forThis book is for Postgres administrators and developers who are looking to build and maintain a highly reliable PostgreSQL cluster. Although knowledge of the new features of PostgreSQL 12 is not required, a basic understanding of PostgreSQL administration is expected.