Tabla de Contenidos: “…-- Das digitale "Ökosystem" -- 1.1 Ein Ökosystem interdependenter digitaler Technologien -- Das Internet der Dinge -- Kabellose Netze der nächsten Generation: "5G" und darüber hinaus -- Cloud-Computing -- Big-Data-Analysen -- Künstliche Intelligenz -- Blockchain -- Rechenleistung -- Die Kombination verschiedener Technologien in einem digitalen Ökosystem vervielfacht ihr Potenzial -- Die Datenrevolution -- Daten als Rohstoff -- 1.2 Auf Videos von Privatanwendern entfällt fast die Hälfte des globalen IP-Verkehrs -- 1.2 Content Delivery Networks und lokales Daten-Caching -- 1.3 Versuch einer Klassifizierung verschiedener Datentypen -- Wertschöpfung durch Daten -- Wichtige datenbezogene Herausforderungen -- Wichtige Merkmale ("Vektoren") des digitalen Wandels und der neuen Geschäfts-modelle -- Größen-, Verbund- und Geschwindigkeitsvorteile -- 1.1 Vektoren des digitalen Wandels: Größen-, Verbund- und Geschwindigkeitsvorteile -- 1.4 Geschäftsmodelle auf der Basis digitaler Zahlungsinnovationen -- Eigentum, Vermögenswerte und wirtschaftlicher Wert -- 1.2 Vektoren des digitalen Wandels: Eigentum, Vermögenswerte und wirtschaftlicher Wert -- 1.5 Geschäftsmodelle, die Online- und Offline-Merkmale kombinieren -- Beziehungen, Märkte und Ökosysteme -- 1.3 Vektoren des digitalen Wandels: Beziehungen, Märkte und Ökosysteme -- 1.6 Geschäftsmodelle auf der Basis von Online-Plattformen -- Anmerkungen -- Literaturverzeichnis -- Kapitel 2 Den Zugang verbessern -- Den Zugang verbessern: Wo liegen die Prioritäten für die Politik?…”

Tabla de Contenidos: “…-- Do You Need to Use Data From Different Sources? -- Do You Have Big Data? -- Do You Have Little Data? -- In Summary ... -- References -- Structure your unstructured data first! …”

Tabla de Contenidos: “…-- A Branded World -- Understanding Consumers Is Good Business -- Consumers, Society, and Technology: A Moving Target -- Social Media: The Horizontal Revolution -- Artificial Intelligence and The Metaverse -- "Big Data" and Data Analytics -- Welcome to the Metaverse! …”

Tabla de Contenidos: “…Intro -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Understanding the Serverless Model -- Introducing serverless -- Thinking serverless -- Serverless and PaaS -- IaaS and On-Premises -- The main goals of serverless -- Scalability -- Availability -- Cost efficiency -- The pros and cons of serverless -- Pros -- Reduced operational costs -- Focus on business -- System security -- Continuous delivery -- Microservices friendly -- Cost model -- Cons -- Higher latency -- Constraints -- Hidden inefficiencies -- Vendor dependency -- Debugging difficulties -- Atomic deploys -- Uncertainties -- Use cases -- Static websites -- Lean websites -- Small e-commerce websites -- Temporary websites -- Triggered processing -- Chatbots -- IoT backends -- Scheduled events -- Big Data -- What you should avoid -- Summary -- Chapter 2: Getting Started with AWS -- Amazon Web Services -- Handling user accounts and security -- AWS IAM -- Creating users and groups -- Sign in with a non-root user account -- Amazon Cognito -- Managing AWS resources -- AWS Management Console -- AWS SDKs -- AWS CLI -- AWS CloudFormation -- Amazon CloudWatch -- Frontend services -- Amazon S3 -- Using S3 with the CLI -- Amazon Route 53 -- Amazon CloudFront -- AWS Certificate Manager -- Messaging and notifications -- Amazon SNS -- AWS IoT -- Backend services -- AWS Lambda -- Creating a Lambda function -- Amazon API Gateway -- Expose your Lambda function using API Gateway -- Database services -- Amazon SimpleDB -- Amazon DynamoDB -- The serverless architecture of our online store -- Estimating costs -- Assumptions -- Route 53 pricing -- S3 pricing -- CloudFront pricing -- Certificate Manager pricing -- IAM pricing -- Cognito pricing -- IoT pricing -- SNS pricing -- CloudWatch pricing…”

Tabla de Contenidos: “…Intro -- Preface -- Acknowledgments -- Part I Introduction and Strategic Landscape -- Big Data -- 1 The Business Analytics Revolution -- Information Technology and Business Analytics -- The Need for a Business Analytics Strategy -- The Complete Business Analytics Team -- Section 1.1 Best Statistical Practice = Meatball Surgery -- Bad News and Good News -- Section 1.2 The Shape of Things to Come-Chapter Summaries -- PART I The Strategic Landscape-Chapters 1 to 6 -- PART II Statistical QDR: Three Pillars for Best Statistical Practice-Chapters 7 to 9 -- PART III Data CSM: Three Building Blocks for Supporting Analytics-Chapters 10 to 12 -- Notes -- 2 Inside the Corporation -- Section 2.1 Analytics in the Traditional Hierarchical Management Offense -- Leadership and Analytics -- Specialization -- Delegating Decisions -- Incentives -- Section 2.2 Corporate Analytics Failures-Shakespearean Comedy of Statistical Errors -- The Financial Meltdown of 2007-2008: Failures in Analytics -- Fannie Mae: Next to the Bomb Blast -- The Great Pharmaceutical Sales-Force Arms Race by Tom "T. …”

Tabla de Contenidos: “…Message batch -- Kafka and the sendfile operation -- Compression -- Kafka streams -- Stream processing topology -- Notion of time in stream processing -- Samza's stream processing API -- The scheduler/executor component of the streaming architecture -- Processing concepts and tradeoffs -- Processing guarantees -- Micro-batch stream processing -- Windowing -- Types of windows -- Summary -- References -- Chapter 11: Let Us Store the Data -- The data explosion problem -- Relational Database Management Systems and Big data -- Introducing Hadoop, the Big Elephant -- Apache YARN -- Hadoop Distributed Filesystem -- HDFS architecture principles (and assumptions) -- High-level architecture of HDFS -- HDFS file formats -- HBase -- Understanding the basics of HBase -- HBase data model -- HBase architecture -- Horizontal scaling with automatic sharding of HBase tables -- HMaster, region assignment, and balancing -- Components of Apache HBase architecture -- Tips for improved performance from your HBase cluster -- Graph stores -- Background of the use case -- Scenario -- Solution discussion -- Bank fraud data model (as can be designed in a property graph data store such as Neo4J) -- Semantic graph -- Linked data -- Vocabularies -- Semantic Query Language -- Inference -- Stardog -- GraphQL queries -- Gremlin -- Virtual Graphs - a Unifying DAO -- Structured data -- CVS -- BITES - Unstructured/Semistructured document store -- Structured data extraction -- Text extraction -- Document queries -- Highly-available clusters -- Guarantees -- Scaling up -- Integration with SPARQL -- Data Formats -- Data integrity and validating constraints -- Strict parsing of RDF -- Integrity Constraint Validation -- Monitoring and operation -- Performance -- Summary -- Further reading -- Chapter 12: When Data Dissemination is as Important as Data Itself -- Data dissemination…”

“…From creating apps to building complex web sites to sorting big data, Python provides a way to get the work done. …”

Tabla de Contenidos: “…Rewiring Museum Information: Mobile and Cloud -- 1 Introduction -- 2 The Limit of Transmitting Information to Visitors in the Museum -- 3 Rewiring Various Information in the Museum: ImageFinder -- 4 The Possibility of Big Data: MAP (Minpaku Anthropological Phototheque) -- 5 Conclusion -- References -- Managing and Analyzing Museum Environmental Data -- 1 Introduction -- 2 Pest Monitoring Data Analysis -- 2.1 Daily Use -- 2.2 Advanced Use -- 3 Temperature/RH Monitoring Data Analysis -- 3.1 Daily Use -- 3.2 Advanced Use -- 4 Future Development -- 5 Conclusion -- References -- Part III: Balance Between Conservation and Access for Museums -- Exhibition, Conservation, and Documentation at the National Museum (Nay Pyi Taw) -- 1 Introduction -- 2 Exhibition at National Museum (Nay Pyi Taw) -- 2.1 Room B1 - Primate and Fossils Exhibition Room (Fig. 4) -- 2.2 Room B2 - Prehistoric Period exhibition room (Fig. 5) -- 2.3 Room B3- Art Gallery (Fig. 6) -- 2.4 Room B4- Performing Arts Exhibition Room (Fig. 7) -- 2.5 Room B5 - Protohistoric Period (Second Century BC to Tenth Century AD) Exhibition Room (Fig. 8) -- 2.6 Room B6 - Historic Period of Eleventh to Fourteenth Century AD Exhibition Room (Fig. 9) -- 2.7 Room C1 - Historic Period of Fourteenth to Seventeenth Century AD Exhibition Room -- 2.8 Room C2, C3 - Historic Period of Eighteenth to Nineteenth Century AD Exhibition Room -- 2.9 Room C4- Lion Throne Exhibition Room -- 2.10 Room C5- Colonial Period (1885-1948) and the Period After Gaining Independence (1948-1988) -- 2.11 Room D1-Myanmar Art and Crafts Exhibition Room -- 2.12 Room D2- ASEAN Exhibition Room -- 2.13 Room D3- Children Discovery Exhibition Room -- 2.14 Room D4- Special Exhibition Room -- 2.15 Room E1- Our Leaders Exhibition Room -- 2.16 Room E2- Presidents' Gifts Exhibition Room…”

Tabla de Contenidos: “…Management Decision Problems -- Improving Decision Making: Using a Database to Clarify Business Strategy -- Improving Decision Making: Using Web Tools To Configure And Price An Automobile -- Collaboration and Teamwork Project -- Business Problem-Solving Case Walmart Versus Amazon and the Future of Retail -- 4 Ethical and Social Issues in Information Systems -- Chapter-Opening Case: The Dark Side of Big Data -- 4-1 What ethical, social, and political issues are raised by information systems? …”

“…Who This Book Is For If you want to leverage the power of both Scala and Spark to make sense of Big Data, then this book is for you. If you are well versed with machine learning concepts and wants to expand your knowledge by delving into the practical implementation using the power of Scala, then this book is what you need! …”

Tabla de Contenidos: “…- Volker Penter und Kevin Pfaffner -- 9 Daten - Führt der Weg über Blockchain zu Big Data? - Alois G. Steidel -- 10 Die digitale Transformation - X-Health für den Patienten - Britta Böckmann -- 11 Persönliche Beratung im Gesundheitswesen - Risiken und Nutzen in der Kommunikation durch die Digitalisierung - Kristina Stenzel -- 12 Nutzerorientierung als Leitgedanke von technologischen Innovationen im Gesundheitswesen - Christoph Dockweiler -- 13 Qualität ist, was der virtuelle Kunde will - Fehlende Kundenorientierung als hausgemachtes Problem - Thomas Breisach -- XI Datenschutz und Compliance -- 1 Datenschutz - Ein Luxus für Gesunde? …”

Tabla de Contenidos: “…-- What is the differences between big data and BI -- What are Hadoop and MapReduce ? …”

“…An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. …”

Tabla de Contenidos: “…-- The public cloud landscape -- Amazon Web Services -- Microsoft Azure -- Google Cloud Platform -- Standing on the shoulders of giants -- A world-class global presence -- Choosing your own adventure -- Leading the way for big data -- The Open Cloud and innovation -- Dedication to customer success -- Bottom-up security -- In good company -- Summary -- Chapter 2: The Google Cloud Console -- Getting started - Google Cloud projects -- Architectural role of Google Cloud projects -- Creating a project -- Free trials on GCP -- The Google Cloud Console -- Understanding the Cloud Console dashboard -- The Google Cloud Shell -- Launching the Cloud Shell -- Supporting multiple sessions -- Features and integrations -- File management -- Web Preview -- The Cloud Shell Code Editor -- Opening in Cloud Shell -- Trying it out -- Installing additional tools -- Boost mode -- Repairing the Cloud Shell -- Other tools -- Mobile apps -- Developer tool integrations -- Summary -- Chapter 3: APIs, CLIs, IAM, and Billing -- Google Cloud APIs -- Managing APIs -- Google APIs Explorer -- Trying out the APIs Explorer -- The Google Cloud SDK -- Installing the Google Cloud SDK -- The gcloud command-line tool -- The basics of gcloud -- Command groups -- Root commands -- Global flags -- Initializing the Google Cloud SDK -- Authentication -- Managing your Google Cloud SDK -- Updating and rollbacks -- Alpha and beta channels -- Configurations in the Google Cloud SDK -- Modifying configuration properties -- Multiple configurations -- Other command-line tools -- bq -- gsutil -- kubectl -- Automating tasks with gcloud -- Modifying output and behavior -- Formatting attributes -- Formatting projections -- Filtering -- Google Cloud IAM -- How IAM works…”

Tabla de Contenidos: “…-- Cybersecurity Means Different Things to Different Folks -- Cybersecurity Is a Constantly Moving Target -- Technological changes -- Digital data -- The Internet -- Cryptocurrency -- Mobile workforces and ubiquitous access -- Smart devices -- Big data -- The COVID-19 pandemic -- Social shifts -- Economic model shifts -- Political shifts -- Data collection -- Election interference -- Hacktivism -- Greater freedom -- Sanctions -- New balances of power -- Looking at the Risks Cybersecurity Mitigates -- The goal of cybersecurity: The CIA Triad -- From a human perspective -- Chapter 2 Getting to Know Common Cyberattacks -- Attacks That Inflict Damage -- Denial-of-service (DoS) attacks -- Distributed denial-of-service (DDoS) attacks -- Botnets and zombies -- Data destruction attacks -- Is That Really You? …”

Tabla de Contenidos: “…Chapter 28 The Value of Data: Objective 5.1: Explain the value of data and information -- Data and Information -- Data -- Information -- Data-Driven Business Decisions -- Data Monetization -- Big Data -- Analytics -- Descriptive Analytics -- Predictive Analytics -- Prescriptive Analytics -- Securing Data -- Chapter 29 Database Concepts: Objective 5.2: Explain database concepts and the purpose of a database -- Databases -- Databases vs. …”

Tabla de Contenidos: “…-- JVM adapts to market changes -- Java Class Library -- Ecosystem -- Scenario 1 - Using a JVM application server -- Scenario 2 - Using a general high-level web application framework -- Scenario 3 - Using a microservice framework -- Popular use cases -- Web applications -- Big data -- IoT -- JVM concepts -- Virtual machine -- The JIT compiler -- Primitive datatypes -- Classes -- Reference types -- References and null references -- Garbage collector -- Example -- Backward compatibility -- Build tools -- Java editions -- Java SE -- Java EE -- Example - Java Persistence API as implemented by two application servers -- Java ME -- Other languages on JVM -- Why choose a language other than Java? …”

Tabla de Contenidos: “…NetBean profiler for analyzing Java applications -- Thread state -- CPU Performance -- Memory Usage -- Conclusion -- Chapter 22: Testing Mobile Applications - Key Challenges and Considerations -- User expectations -- Key challenges encountered in testing mobile applications -- User experience -- Contextual appropriateness -- Varied mobile user interfaces -- Device diversity and availability -- Device based testing approach -- Automated testing of layouts -- Test automation challenges with non-standardized tools -- Reduced time to market -- Recommendations to enhance mobile applications usability -- Conclusion -- Chapter 23: Testing Analytics Applications - What Has Changed in SMAC World -- Understanding your customers, gathering data and analyzing it -- Testing the data -- Testing the BI/BA applications -- How is testing done differently for big data/Hadoop applications? -- Conclusion -- Chapter 24: Migrating Applications to Cloud Environments - Key Testing Considerations -- Need for validating applications for cloud migration -- Key testing focus areas in cloud migration of applications -- Key challenges in validation during cloud migration of applications -- Application migration validation - a holistic approach -- Conclusion -- Chapter 25: How Should a Tester Adapt to Cloud - Call for Change of Mindset among Testers -- What must testers prepare for when testing applications on cloud -- Testing the invisible -- Understanding the distance -- Breaking the communication barriers -- Securing the application -- Replicating the platform -- Using the right tools -- Conclusion -- Chapter 26: On-Demand Performance Testing on Self-Service Environments -- On-demand performance testing environments - key challenges and solutions -- Need for a cloud platform to build end-to-end performance testing…”

“…This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning. …”

“…Simplify real-time data processing by leveraging the power of Apache Kafka 1.0 About This Book Use Kafka 1.0 features such as Confluent platforms and Kafka streams to build efficient streaming data applications to handle and process your data Integrate Kafka with other Big Data tools such as Apache Hadoop, Apache Spark, and more Hands-on recipes to help you design, operate, maintain, and secure your Apache Kafka cluster with ease Who This Book Is For This book is for developers and Kafka administrators who are looking for quick, practical solutions to problems encountered while operating, managing or monitoring Apache Kafka. …”