Get to grips with building reliable, scalable, and maintainable database solutions for enterprises and production databases Key Features Implement PostgreSQL 13 features to perform end-to-end modern database management Design, manage, and build enterprise database solutions using a unique recipe-based approach Solve common and not-so-common challenges faced while working to achieve optimal database performance Book DescriptionPostgreSQL has become the most advanced open source database on the market. This book follows a step-by-step approach, guiding you effectively in deploying PostgreSQL in production environments. The book starts with an introduction to PostgreSQL and its architecture. You'll cover common and not-so-common challenges faced while designing and managing the database. Next, the book focuses on backup and recovery strategies to ensure your database is steady and achieves optimal performance. Throughout the book, you'll address key challenges such as maintaining reliability, data integrity, a fault-tolerant environment, a robust feature set, extensibility, consistency, and authentication. Moving ahead, you'll learn how to manage a PostgreSQL cluster and explore replication features for high availability. Later chapters will assist you in building a secure PostgreSQL server, along with covering recipes for encrypting data in motion and data at rest. Finally, you'll not only discover how to tune your database for optimal performance but also understand ways to monitor and manage maintenance activities, before learning how to perform PostgreSQL upgrades during downtime. By the end of this book, you'll be well-versed with the essential PostgreSQL 13 features to build enterprise relational databases. What you will learn Understand logical and physical backups in Postgres Demonstrate the different types of replication methods possible with PostgreSQL today Set up a high availability cluster that provides seamless automatic failover for applications Secure a PostgreSQL encryption through authentication, authorization, and auditing Analyze the live and historic activity of a PostgreSQL server Understand how to monitor critical services in Postgres 13 Manage maintenance activities and performance tuning of a PostgreSQL cluster Who this book is forThis PostgreSQL book is for database architects, database developers and administrators, or anyone who wants to become well-versed with PostgreSQL 13 features to plan, manage, and design efficient database solutions. Prior experience with the PostgreSQL database and SQL language is expected.

Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies Key Features Get up to speed with AutoML using OSS, Azure, AWS, GCP, or any platform of your choice Eliminate mundane tasks in data engineering and reduce human errors in machine learning models Find out how you can make machine learning accessible for all users to promote decentralized processes Book DescriptionEvery machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (AutoML) is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort. This book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and much more. You'll discover different ways of implementing these techniques in open source tools and then learn to use enterprise tools for implementing AutoML in three major cloud service providers: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform. As you progress, you'll explore the features of cloud AutoML platforms by building machine learning models using AutoML. The book will also show you how to develop accurate models by automating time-consuming and repetitive tasks in the machine learning development lifecycle. By the end of this machine learning book, you'll be able to build and deploy AutoML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks. What you will learn Explore AutoML fundamentals, underlying methods, and techniques Assess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an applied scenario Find out the difference between cloud and operations support systems (OSS) Implement AutoML in enterprise cloud to deploy ML models and pipelines Build explainable AutoML pipelines with transparency Understand automated feature engineering and time series forecasting Automate data science modeling tasks to implement ML solutions easily and focus on more complex problems Who this book is forCitizen data scientists, machine learning developers, artificial intelligence enthusiasts, or anyone looking to automatically build machine learning models using the features offered by open source tools, Microsoft Azure Machine Learning, AWS, and Google Cloud Platform will find this book useful. Beginner-level knowledge of building ML models is required to get the best out of this book. Prior experience in using Enterprise cloud is beneficial.

Unsere Umwelt wird mehr und mehr gepragt durch Automatische Datenverarbeitung. Weil die technische Seite dabei von der Elektronik beherrscht wird, verwendet man durch weg den Begriff Elektronische Datenverarbeitung (EDV). Es gibt eigentlich kaum noch einen Bereich im menschlichen Zusammenleben, der nicht mit EDV zumindest in Be ruhrung kame. Beinahe taglich begegnet uns EDV direkt oder indirekt - bewulSt und unbewulSt - beispielsweise in Form von Abrechnungen, Benachrichtigungen, beim Ein kauf, in der Verwaltung und vor allem im Berufsleben. Das ist der aktuelle Bezug fur das vorliegende Lehrbuch. Und nicht etwa modische Aspekte ver: anlassen zu der Aufforderung, Prinzipien und Einzelheiten der EDV zu studieren. Fur den angehenden Techniker und Ingenieur ergibt sich eine Notwendigkeit fur ein vertieftes Studium daraus, daIS man bei der Berufsausubung von ihm Verstandnis fur oder gar Detail kenntnisse uber EDV erwartet. Das wird besonders unabwendbar, wenn es sich um einen Absolventen mit elektrotechnischer Spl'/ialausbildung handelt. Der Bedeutung der EDV wird inzwischen mit einer Vielzahl von Abhandlungen und Lehr buchern gerecht. Das errichtete N iveau reicht dabei von einfachsten Darstellungen fur Jedermann bis zu wissenschaftlichen Arbeiten, die nur von Spezialisten lesbar sind."

Comprehensive recipes to give you valuable insights on Transformers, Reinforcement Learning, and more Key Features Deep Learning solutions from Kaggle Masters and Google Developer Experts Get to grips with the fundamentals including variables, matrices, and data sources Learn advanced techniques to make your algorithms faster and more accurate Book DescriptionThe independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. Dive into recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google's machine learning library, TensorFlow. This cookbook covers the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You'll discover real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and regression. Explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be used to solve computer vision and natural language processing (NLP) problems. With the help of this book, you will be proficient in using TensorFlow, understand deep learning from the basics, and be able to implement machine learning algorithms in real-world scenarios. What you will learn Take TensorFlow into production Implement and fine-tune Transformer models for various NLP tasks Apply reinforcement learning algorithms using the TF-Agents framework Understand linear regression techniques and use Estimators to train linear models Execute neural networks and improve predictions on tabular data Master convolutional neural networks and recurrent neural networks through practical recipes Who this book is forIf you are a data scientist or a machine learning engineer, and you want to skip detailed theoretical explanations in favor of building production-ready machine learning models using TensorFlow, this book is for you. Basic familiarity with Python, linear algebra, statistics, and machine learning is necessary to make the most out of this book.

Get to grips with building and productionizing end-to-end big data solutions in Azure and learn best practices for working with large datasets Key Features Integrate with Azure Synapse Analytics, Cosmos DB, and Azure HDInsight Kafka Cluster to scale and analyze your projects and build pipelines Use Databricks SQL to run ad hoc queries on your data lake and create dashboards Productionize a solution using CI/CD for deploying notebooks and Azure Databricks Service to various environments Book DescriptionAzure Databricks is a unified collaborative platform for performing scalable analytics in an interactive environment. The Azure Databricks Cookbook provides recipes to get hands-on with the analytics process, including ingesting data from various batch and streaming sources and building a modern data warehouse. The book starts by teaching you how to create an Azure Databricks instance within the Azure portal, Azure CLI, and ARM templates. You'll work through clusters in Databricks and explore recipes for ingesting data from sources, including files, databases, and streaming sources such as Apache Kafka and EventHub. The book will help you explore all the features supported by Azure Databricks for building powerful end-to-end data pipelines. You'll also find out how to build a modern data warehouse by using Delta tables and Azure Synapse Analytics. Later, you'll learn how to write ad hoc queries and extract meaningful insights from the data lake by creating visualizations and dashboards with Databricks SQL. Finally, you'll deploy and productionize a data pipeline as well as deploy notebooks and Azure Databricks service using continuous integration and continuous delivery (CI/CD). By the end of this Azure book, you'll be able to use Azure Databricks to streamline different processes involved in building data-driven apps. What you will learn Read and write data from and to various Azure resources and file formats Build a modern data warehouse with Delta Tables and Azure Synapse Analytics Explore jobs, stages, and tasks and see how Spark lazy evaluation works Handle concurrent transactions and learn performance optimization in Delta tables Learn Databricks SQL and create real-time dashboards in Databricks SQL Integrate Azure DevOps for version control, deploying, and productionizing solutions with CI/CD pipelines Discover how to use RBAC and ACLs to restrict data access Build end-to-end data processing pipeline for near real-time data analytics Who this book is forThis recipe-based book is for data scientists, data engineers, big data professionals, and machine learning engineers who want to perform data analytics on their applications. Prior experience of working with Apache Spark and Azure is necessary to get the most out of this book.

Das Arbeitsergebnis des Studienkreises Dr. Parli wurde zunachst in Form eines internen Arbeitsberichtes den Mitgliedern des Foerderervereins des Betriebswirtschaftlichen Instituts fur Organisation und Automation an der Universitat zu Koeln (BIFOA) zur Verfugung gestellt. Die sich daraus er- gebende Diskussion zeigte, dass die Probleme der Istaufnahme bei automati- sierter Datenverarbeitung (ADV) nach wie vor in Wissenschaft und Praxis von hoher Aktualitat sind, so dass mir nunmehr - nicht zuletzt aufgrund zahlreicher Anfragen aus der Wirtschaftspraxis - eine Publikation in der Instituts-Schriftenreihe sinnvoll erscheint. Damit werden die Ergebnisse einem groesseren Interessentenkreis zuganglich und koennen insbesondere mittleren und kleineren Unternehmungen und Einheiten der oeffentlichen Verwaltung, die aufgrund des vielfaltigen Angebots unterschiedlicher Computergroessen ebenfalls in den Kreis der Anwender von Anlagen der automatisierten Datenverarbeitung geruckt sind, als Orientierungshilfe dienen. In der vorliegenden Arbeit werden die Erfahrungen von Wirtschaftsprakti- kern aus Grossunternehmungen verschiedener Branchen sowie der oeffent- lichen Verwaltung systematisiert und auf ihre Allgemeingultigkeit unter- sucht. Es handelt sich um Erfahrungen, die aus Unternehmungen stammen, die sich aufgrund ihres Geschaftsumfanges schon fruhzeitig zum Einsatz von ADV-Anlagen entschliessen mussten und die teilweise - entsprechend den Stufen der technischen und organisatorischen Entwicklung - mit den Istaufnahmeproblemen unterschiedlichster Art konfrontiert wurden. Das Hauptanliegen der Schrift besteht nicht in einer rein theoretischen Durchdringung des Problemkreises Istaufnahme und Automatisierte Daten- verarbeitung, sondern in einer praxisbezogenen Aufbereitung und Systema- tisierung empirischen Wissens auf diesem Gebiet.

Learn how to gain insights from your data as well as machine learning and become a presentation pro who can create interactive dashboards Key Features Enhance your presentation skills by implementing engaging data storytelling and visualization techniques Learn the basics of machine learning and easily apply machine learning models to your data Improve productivity by automating your data processes Book DescriptionData Analytics Made Easy is an accessible beginner's guide for anyone working with data. The book interweaves four key elements: Data visualizations and storytelling - Tired of people not listening to you and ignoring your results? Don't worry; chapters 7 and 8 show you how to enhance your presentations and engage with your managers and co-workers. Learn to create focused content with a well-structured story behind it to captivate your audience. Automating your data workflows - Improve your productivity by automating your data analysis. This book introduces you to the open-source platform, KNIME Analytics Platform. You'll see how to use this no-code and free-to-use software to create a KNIME workflow of your data processes just by clicking and dragging components. Machine learning - Data Analytics Made Easy describes popular machine learning approaches in a simplified and visual way before implementing these machine learning models using KNIME. You'll not only be able to understand data scientists' machine learning models; you'll be able to challenge them and build your own. Creating interactive dashboards - Follow the book's simple methodology to create professional-looking dashboards using Microsoft Power BI, giving users the capability to slice and dice data and drill down into the results. What you will learn Understand the potential of data and its impact on your business Import, clean, transform, combine data feeds, and automate your processes Influence business decisions by learning to create engaging presentations Build real-world models to improve profitability, create customer segmentation, automate and improve data reporting, and more Create professional-looking and business-centric visuals and dashboards Open the lid on the black box of AI and learn about and implement supervised and unsupervised machine learning models Who this book is forThis book is for beginners who work with data and those who need to know how to interpret their business/customer data. The book also covers the high-level concepts of data workflows, machine learning, data storytelling, and visualizations, which are useful for managers. No previous math, statistics, or computer science knowledge is required.

1m Oktober 1968 trafen Klinikchefs mit Spezialisten aus dem Bereich der Hoch- schulen und der Computer-lndustrie in Reinhartshausen zusammen, urn innerhalb der raschen Entwicklung der sogenannten zweiten technischen Revolution den Trend der modernen Medizin aufzusptiren. Ais Diskussionsgrundlage dienten ausgewillllte Refe- rate. Ein tiberblick tiber den Verlauf dieser Tagung Ui.l3t es niitzlich erscheinen, die Thematik einem grol3eren Kreis zugiinglich zu machen. So haben wir uns entschlossen, die Manuskripte der Autoren zu einem Werk zusammenzuschliel3en. Die technischen Grundlagen der elektronischen Datenverarbeitung sollen dabei allerdings unbertick- sichtigt bleiben. Die Durchsicht der Beitrage mag den Eindruck erwecken, dal3 anscheinend bereits zurtickliegende Entwicklungsphasen mit phantasievollen Forderungen an die Zukunft inhomogen zusammengestellt seien. Aber es kommt uns darauf an, in der bestaunens- wert en Schnelligkeit, mit der sich eine elektronische Informationsverarbeitung - oder besser formuliert - die moderne Wissenschaft der Informatik vollzieht, den gegen- wartigen Zustand in der Medizin aufzuzeigen und in ihm an den Einzelheiten die Ten- denzen darzustellen, die sich bald aus den ursprtinglichen mechanischen Formen der Erfassung und Verarbeitung von Daten, bald aus dem Bild der Zukunft deutlicher ab- zeichnen. Wir hegen die Hoffnung, dal3 auf dieser Basis sich pragende Konzeptionen fUr die Gestaltung der Zukunft ergeben. Herrn Kollegen NORBERT EICHENSEHER danken wir fUr seine wertvolle Unter- stiltzung bei den Korrekturen und der Abfassung des Sachverzeichnisses.

Reinforce your understanding of data science and data analysis from a statistical perspective to extract meaningful insights from your data using Python programming Key Features Work your way through the entire data analysis pipeline with statistics concerns in mind to make reasonable decisions Understand how various data science algorithms function Build a solid foundation in statistics for data science and machine learning using Python-based examples Book DescriptionStatistics remain the backbone of modern analysis tasks, helping you to interpret the results produced by data science pipelines. This book is a detailed guide covering the math and various statistical methods required for undertaking data science tasks. The book starts by showing you how to preprocess data and inspect distributions and correlations from a statistical perspective. You'll then get to grips with the fundamentals of statistical analysis and apply its concepts to real-world datasets. As you advance, you'll find out how statistical concepts emerge from different stages of data science pipelines, understand the summary of datasets in the language of statistics, and use it to build a solid foundation for robust data products such as explanatory models and predictive models. Once you've uncovered the working mechanism of data science algorithms, you'll cover essential concepts for efficient data collection, cleaning, mining, visualization, and analysis. Finally, you'll implement statistical methods in key machine learning tasks such as classification, regression, tree-based methods, and ensemble learning. By the end of this Essential Statistics for Non-STEM Data Analysts book, you'll have learned how to build and present a self-contained, statistics-backed data product to meet your business goals. What you will learn Find out how to grab and load data into an analysis environment Perform descriptive analysis to extract meaningful summaries from data Discover probability, parameter estimation, hypothesis tests, and experiment design best practices Get to grips with resampling and bootstrapping in Python Delve into statistical tests with variance analysis, time series analysis, and A/B test examples Understand the statistics behind popular machine learning algorithms Answer questions on statistics for data scientist interviews Who this book is forThis book is an entry-level guide for data science enthusiasts, data analysts, and anyone starting out in the field of data science and looking to learn the essential statistical concepts with the help of simple explanations and examples. If you're a developer or student with a non-mathematical background, you'll find this book useful. Working knowledge of the Python programming language is required.

This report discusses the role computer-assisted personal interviewing (CAPI) can play in transforming survey data collection to allow better monitoring of the Sustainable Development Goals. The first part of this publication provides rigorous quantitative evidence on why CAPI is a better alternative to the traditional pen and paper interviewing method, particularly in the context of nationally representative surveys. The second part discusses the benefits of delivering CAPI training to statisticians using the popular massive online open course format. The final part provides a summary of existing CAPI platforms and offers some preliminary advice for NSOs to consider when selecting a CAPI platform for their institution. This is a Special Supplement to the Key Indicators for Asia and the Pacific 2019.

Als Stahl bezeichnet man heute alle Eisenlegierungen - mit Ausnahme der nicht schmiedbaren hochkohlenstoffhaltigen Gu sorten wie Grauguli, Hartguf und Ternperguf - ohne Riicksichr auf ihre Eigenschaften. Friiher wurde als wesentliches Merkmal des Stahles die Hartbarkeit angesehen. Es gibt aber eine ganze Reihe von Stahlen, die sich nicht harten lassen, die durch das Abschrecken aus hohen Temperaturen im Gegenteil sogar weicher, zaher werden. Edelstdble werden vielfach solche Stahle genannt, die au er mit Kohlenstoff auch noch mit anderen Grundstoffen, z. B. mit Chrom, Nickel, Wolfram, Vanadin usw. legiert sind. Diese Begriffsbestim- mung ist jedoch nicht erschopfend und auch anfechtbar, Denn man wird einen reinen Kohlenstoffstahl, der sorgfaltig erzeugt und auf dem ganzen Wege der Herstellung - vom Gu bis zum Versand - immer wieder gewissenhaft gepriift worden ist, zweifellos auch zu den Edelstahlen rechnen miissen. Andererseits enthalten manchmal Massenstahle - auch als unbeabsichtigte Verunreinigungen - ge- wisse Mengen von Legierungselementen. Das Richtige wird man treffen, wenn man die bei den grofsen Hiittenwerken in grofien Mengen erzeugten billigen Stahle als .Mas- senstahle bezeichnet, die von einem Edelstahlwerk mit Sorgfalt und unter scharfster Kontrolle hergestellten Stahle dagegen als Edelstahle. Die billigen Massenstahle werden meistens nach Festigkeit ver- kauft, die Edelstahle dagegen nach dem Verwendungszweck und unter einer Markenbezeichnung.

Get up to speed with the new features added to Microsoft SQL Server 2019 Analysis Services and create models to support your business Key Features Explore tips and tricks to design, develop, and optimize end-to-end data analytics solutions using Microsoft's technologies Learn tabular modeling and multi-dimensional cube design development using real-world examples Implement Analysis Services to help you make productive business decisions Book DescriptionSQL Server Analysis Services (SSAS) continues to be a leading enterprise-scale toolset, enabling customers to deliver data and analytics across large datasets with great performance. This book will help you understand MS SQL Server 2019's new features and improvements, especially when it comes to SSAS. First, you'll cover a quick overview of SQL Server 2019, learn how to choose the right analytical model to use, and understand their key differences. You'll then explore how to create a multi-dimensional model with SSAS and expand on that model with MDX. Next, you'll create and deploy a tabular model using Microsoft Visual Studio and Management Studio. You'll learn when and how to use both tabular and multi-dimensional model types, how to deploy and configure your servers to support them, and design principles that are relevant to each model. The book comes packed with tips and tricks to build measures, optimize your design, and interact with models using Excel and Power BI. All this will help you visualize data to gain useful insights and make better decisions. Finally, you'll discover practices and tools for securing and maintaining your models once they are deployed. By the end of this MS SQL Server book, you'll be able to choose the right model and build and deploy it to support the analytical needs of your business. What you will learn Determine the best analytical model using SSAS Cover the core aspects involved in MDX, including writing your first query Implement calculated tables and calculation groups (new in version 2019) in DAX Create and deploy tabular and multi-dimensional models on SQL 2019 Connect and create data visualizations using Excel and Power BI Implement row-level and other data security methods with tabular and multi-dimensional models Explore essential concepts and techniques to scale, manage, and optimize your SSAS solutions Who this book is forThis Microsoft SQL Server book is for BI professionals and data analysts who are looking for a practical guide to creating and maintaining tabular and multi-dimensional models using SQL Server 2019 Analysis Services. A basic working knowledge of BI solutions such as Power BI and database querying is required.

Leverage the Azure analytics platform's key analytics services to deliver unmatched intelligence for your data Key Features Learn to ingest, prepare, manage, and serve data for immediate business requirements Bring enterprise data warehousing and big data analytics together to gain insights from your data Develop end-to-end analytics solutions using Azure Synapse Book DescriptionAzure Synapse Analytics, which Microsoft describes as the next evolution of Azure SQL Data Warehouse, is a limitless analytics service that brings enterprise data warehousing and big data analytics together. With this book, you'll learn how to discover insights from your data effectively using this platform. The book starts with an overview of Azure Synapse Analytics, its architecture, and how it can be used to improve business intelligence and machine learning capabilities. Next, you'll go on to choose and set up the correct environment for your business problem. You'll also learn a variety of ways to ingest data from various sources and orchestrate the data using transformation techniques offered by Azure Synapse. Later, you'll explore how to handle both relational and non-relational data using the SQL language. As you progress, you'll perform real-time streaming and execute data analysis operations on your data using various languages, before going on to apply ML techniques to derive accurate and granular insights from data. Finally, you'll discover how to protect sensitive data in real time by using security and privacy features. By the end of this Azure book, you'll be able to build end-to-end analytics solutions while focusing on data prep, data management, data warehousing, and AI tasks. What you will learn Explore the necessary considerations for data ingestion and orchestration while building analytical pipelines Understand pipelines and activities in Synapse pipelines and use them to construct end-to-end data-driven workflows Query data using various coding languages on Azure Synapse Focus on Synapse SQL and Synapse Spark Manage and monitor resource utilization and query activity in Azure Synapse Connect Power BI workspaces with Azure Synapse and create or modify reports directly from Synapse Studio Create and manage IP firewall rules in Azure Synapse Who this book is forThis book is for data architects, data scientists, data engineers, and business analysts who are looking to get up and running with the Azure Synapse Analytics platform. Basic knowledge of data warehousing will be beneficial to help you understand the concepts covered in this book more effectively.