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Architecting a Modern Data Warehouse for Large Enterprises : Build Multi-Cloud Modern Distributed Data Warehouses with Azure and AWS / / Anjani Kumar, Abhishek Mishra, and Sanjeev Kumar
| Architecting a Modern Data Warehouse for Large Enterprises : Build Multi-Cloud Modern Distributed Data Warehouses with Azure and AWS / / Anjani Kumar, Abhishek Mishra, and Sanjeev Kumar |
| Autore | Kumar Anjani |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | New York, NY : , : Apress Media LLC, , [2024] |
| Descrizione fisica | 1 online resource (XV, 368 p. 146 illus.) |
| Disciplina | 005.7 |
| Soggetto topico |
Data warehousing
Microsoft Azure (Computing platform) Big data Business enterprises - Data processing |
| ISBN | 979-88-6880-029-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Introduction -- Chapter 2: Modern Data Warehouses -- Chapter 3: Data Lake, Lake House, and Delta Lake -- Chapter 4: Data Mesh -- Chapter 5: Data Orchestration Techniques -- Chapter 6: Data Democratization, Governance, and Security -- Chapter 7: Business Intelligence. |
| Record Nr. | UNINA-9910799251003321 |
Kumar Anjani
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| New York, NY : , : Apress Media LLC, , [2024] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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The art of site reliability engineering (SRE) with Azure : building and deploying applications that endure / / Unai Huete Beloki
| The art of site reliability engineering (SRE) with Azure : building and deploying applications that endure / / Unai Huete Beloki |
| Autore | Beloki Unai Huete |
| Pubbl/distr/stampa | Berkeley, California : , : Apress, , [2022] |
| Descrizione fisica | 1 online resource (289 pages) |
| Disciplina | 629.8 |
| Soggetto topico |
Reliability (Engineering)
Microsoft Azure (Computing platform) Engineering - Management Cyberinfrastructure - Management |
| ISBN | 1-4842-8704-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: The foundation of Site Reliability Engineering -- Chapter 2: Service Level Management Definitions and Acronyms -- Chapter 3: Azure Well-Architected Framework (WAF) -- Chapter 4: Architecting Resilient Solutions in Azure -- Chapter 5: Automation to Enable SRE with GitHub Actions/Azure DevOps/ Azure Automation -- Chapter 6: Monitoring as the Key to Knowledge -- Chapter 7: Efficiently Handle Incident Response and Blameless Post-Mortems -- Chapter 8: Azure Chaos Studio (Preview) and Azure Load Testing (Preview). |
| Record Nr. | UNINA-9910595047903321 |
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Beloki Unai Huete
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| Berkeley, California : , : Apress, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Azure Arc Systems Management : Governance and Administration of Multi-Cloud and Hybrid IT Estates
| Azure Arc Systems Management : Governance and Administration of Multi-Cloud and Hybrid IT Estates |
| Autore | Maxwell Ramona |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress L. P., , 2024 |
| Descrizione fisica | 1 online resource (298 pages) |
| Disciplina | 004.67/82 |
| Soggetto topico |
Microsoft Azure (Computing platform)
Cloud computing |
| ISBN | 9781484294802 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910855394703321 |
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Maxwell Ramona
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| Berkeley, CA : , : Apress L. P., , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Azure Arc-enabled data services revealed : deploying Azure Data services on any infrastructure / / Ben Weissman and Anthony E. Nocentino
| Azure Arc-enabled data services revealed : deploying Azure Data services on any infrastructure / / Ben Weissman and Anthony E. Nocentino |
| Autore | Weissman Ben |
| Edizione | [2nd edition.] |
| Pubbl/distr/stampa | [Place of publication not identified] : , : Apress, , [2022] |
| Descrizione fisica | 1 online resource (184 pages) |
| Disciplina | 004.6782 |
| Soggetto topico |
Microsoft Azure (Computing platform)
Cloud computing |
| ISBN | 1-4842-8085-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. A Kubernetes Primer 2. Azure Arc-Enabled Data Services 3. Getting Ready for Deployment 4. Installing Kubernetes 5. Deploying a Data Controller in Indirect Mode 6. Deploying a Data Controller in Direct Mode 7. Deploying an Azure Arc-Enabled SQL Managed Instance 8. Deploying Azure Arc-Enabled PostgreSQL Hyperscale 9. Monitoring and Management |
| Record Nr. | UNINA-9910548181203321 |
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Weissman Ben
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| [Place of publication not identified] : , : Apress, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Azure Arc-Enabled Kubernetes and servers : extending hyperscale cloud management to your datacenter / / Steve Buchanan, John Joyner
| Azure Arc-Enabled Kubernetes and servers : extending hyperscale cloud management to your datacenter / / Steve Buchanan, John Joyner |
| Autore | Buchanan Steve |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2022 |
| Descrizione fisica | 1 online resource (310 pages) : illustrations (black and white, and colour) |
| Disciplina | 004.6782 |
| Soggetto topico |
Microsoft Azure (Computing platform)
Cloud Computing |
| ISBN | 1-4842-7768-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Azure Arc as an Extension of the Azure Control Plane -- Chapter 2: Azure Resource Manager Insights -- Chapter 3: Azure Management Insights -- Chapter 4: Azure Arc Servers: Getting Started -- Chapter 5: Azure Arc Servers: Using at Scale -- Chapter 6: Hybrid Server Monitoring Solution -- Chapter 7: Regulatory and Security Compliance for Azure Arc Servers -- Chapter 8: GitOps Insights -- Chapter 9: Azure Arc Enabled Kubernetes: Getting Started. |
| Record Nr. | UNINA-9910522981503321 |
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Buchanan Steve
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| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Azure cloud security for absolute beginners : enabling cloud infrastructure security with multi-level security options / / Pushpa Herath
| Azure cloud security for absolute beginners : enabling cloud infrastructure security with multi-level security options / / Pushpa Herath |
| Autore | Herath Pushpa |
| Pubbl/distr/stampa | [Berkeley, CA] : , : Apress, , [2022] |
| Descrizione fisica | 1 online resource (242 pages) : illustrations (colour) |
| Disciplina | 004.6782 |
| Soggetto topico |
Cloud computing - Security measures
Microsoft Azure (Computing platform) |
| ISBN | 1-4842-7860-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Understanding the Importance of Data/Application Security -- Chapter 2: Overview of Basic Azure Security Components -- Chapter 3: Introduction to Azure Active Directory -- Chapter 4: Working with Azure Key Vault -- Chapter 5: Ensure Azure Application Security -- Chapter 6: Ensure Data Security with Azure Storages -- Chapter 7: Ensure Security using Azure Virtual Networks -- Chapter 8: Azure Virtual Machine Security -- Chapter 9: Securing resources with Azure Firewall -- Chapter 10: App Service Environments. |
| Record Nr. | UNINA-9910522980703321 |
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Herath Pushpa
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| [Berkeley, CA] : , : Apress, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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The Azure Data Lakehouse Toolkit : Building and Scaling Data Lakehouses on Azure with Delta Lake, Apache Spark, Databricks, Synapse Analytics, and Snowflake / / by Ron L'Esteve
| The Azure Data Lakehouse Toolkit : Building and Scaling Data Lakehouses on Azure with Delta Lake, Apache Spark, Databricks, Synapse Analytics, and Snowflake / / by Ron L'Esteve |
| Autore | L'Esteve Ron |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2022 |
| Descrizione fisica | 1 online resource (467 pages) |
| Disciplina | 004.6782 |
| Soggetto topico |
Microsoft Azure (Computing platform)
Cloud computing Electronic data processing Databases |
| ISBN | 1-4842-8233-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I: Getting Started -- Chapter 1: The Data Lakehouse Paradigm -- Part II: Data Platforms -- Chapter 2: Snowflake -- Chapter 3: Databricks -- Chapter 4: Synapse Analytics -- Part III: Apache Spark ELT -- Chapter 5: Pipelines and Jobs -- Chapter 6: Notebook Code -- Part IV: Delta Lake.-Chapter 7: Schema Evolution -- Chapter 8: Change Feed -- Chapter 9: Clones -- Chapter 10: Live Tables -- Chapter 11: Sharing -- Part V: Optimizing Performance -- Chapter 12: Dynamic Partition Pruning for Querying Star Schemas -- Chapter 13: Z-Ordering & Data Skipping -- Chapter 14: Adaptive Query Execution -- Chapter 15: Bloom Filter Index -- Chapter 16: Hyperspace -- Part VI: Advanced Capabilities -- Chapter 17: Auto Loader -- Chapter 18: Python Wheels -- Chapter 19: Security & Controls. |
| Record Nr. | UNINA-9910584482303321 |
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L'Esteve Ron
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| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Azure Security For Critical Workloads : Implementing Modern Security Controls for Authentication, Authorization and Auditing / / by Sagar Lad
| Azure Security For Critical Workloads : Implementing Modern Security Controls for Authentication, Authorization and Auditing / / by Sagar Lad |
| Autore | Lad Sagar |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2023 |
| Descrizione fisica | 1 online resource (221 pages) |
| Disciplina | 411 |
| Soggetto topico |
Microsoft Azure (Computing platform)
Computer security Computer networks - Security measures Cloud computing |
| ISBN | 1-4842-8936-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Introduction: Dimensions of cloud security -- Chapter 2: Identity and Access Management with Azure Active Directory -- Chapter 3: Network Security Patterns -- Chapter 4: Infrastructure Security Patterns -- Chapter 5: Application and Data Security Patterns -- Chapter 6: Security Processes -- Chapter 7: Automated Security Monitoring -- Chapter 8: Creating a Security Culture. |
| Record Nr. | UNINA-9910634041003321 |
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Lad Sagar
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| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Azure SQL Hyperscale Revealed : High-Performance Scalable Solutions for Critical Data Workloads / / Zoran Barać and Daniel Scott-Raynsford
| Azure SQL Hyperscale Revealed : High-Performance Scalable Solutions for Critical Data Workloads / / Zoran Barać and Daniel Scott-Raynsford |
| Autore | Barać Zoran |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | New York, NY : , : Apress, , [2023] |
| Descrizione fisica | 1 online resource (475 pages) |
| Disciplina | 005.7565 |
| Soggetto topico |
Database management
Microsoft Azure (Computing platform) SQL (Computer program language) |
| ISBN | 1-4842-9225-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Part I. Architecture -- 1. The Journey to Hyperscale Architecture in Azure SQL -- 2. Azure SQL Hyperscale Architecture: Concepts and Foundations -- Part II. Planning and Deployment -- 3. Planning an Azure SQL DB Hyperscale Environment -- 4. Deploying a Highly Available Hyperscale Database into a Virtual Network -- 5. Administering a Hyperscale Database in a Virtual Network in the Azure Portal -- 6. Configuring Transparent Data Encryption to Bring Your Own Key -- 7. Enabling Geo-replication for Disaster Recovery -- 8. Configuring Security Features and Enabling Diagnostic and Audit Logs -- 9. Deploying Azure SQL DB Hyperscale using PowerShell -- 10. Deploying Azure SQL DB Hyperscale using Bash and Azure CLI -- 11. Deploying Azure SQL DB Hyperscale using Azure Bicep -- 12. Testing Hyperscale Database Performance Against Other Azure SQL Deployment Options -- Part III. Operation and Management -- 13. Monitoring and Scaling -- 14. Backup, Restore and Disaster Recovery -- 15. Security and Updating -- 16. Managing Costs -- Part IV. Migration -- 17. Determining whether Hyperscale is Appropriate -- 18. Migrating to Hyperscale -- 19. Reverse Migrating Away from Hyperscale -- Conclusion. |
| Record Nr. | UNINA-9910683346503321 |
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Barać Zoran
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| New York, NY : , : Apress, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Beginning Azure synapse analytics : transition from data warehouse to data lakehouse / / Bhadresh Shiyal
| Beginning Azure synapse analytics : transition from data warehouse to data lakehouse / / Bhadresh Shiyal |
| Autore | Shiyal Bhadresh |
| Pubbl/distr/stampa | [Place of publication not identified] : , : Apress, , [2021] |
| Descrizione fisica | 1 online resource (263 pages) |
| Disciplina | 658.40380285574 |
| Soggetto topico |
Data warehousing - Management
Microsoft Azure (Computing platform) |
| ISBN | 1-4842-7061-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Core Data and Analytics Concepts -- Core Data Concepts -- What Is Data? -- Structured Data -- Semi-structured Data -- Unstructured Data -- Data Processing Methods -- Batch Data Processing -- Streaming or Real-Time Data Processing -- Relational Data and Its Characteristics -- Non-Relational Data and Its Characteristics -- Core Data Analytics Concepts -- What Is Data Analytics? -- Data Ingestion -- Data Exploration -- Data Processing -- ETL -- ELT -- ELT / ETL Tools -- Data Visualization -- Data Analytics Categories -- Descriptive Analytics -- Diagnostic Analytics -- Predictive Analytics -- Prescriptive Analytics -- Cognitive Analytics -- Summary -- Chapter 2: Modern Data Warehouses and Data Lakehouses -- What Is a Data Warehouse? -- Core Data Warehouse Concepts -- Data Model -- Model Types -- Schema Types -- Metadata -- Why Do We Need a Data Warehouse? -- Efficient Decision-Making -- Separation of Concerns -- Single Version of the Truth -- Data Restructuring -- Self-Service BI -- Historical Data -- Security -- Data Quality -- Data Mining -- More Revenues -- What Is a Modern Data Warehouse? -- Difference Between Traditional & -- Modern Data Warehouses -- Cloud vs. On-Premises -- Separation of Compute and Storage Resources -- Cost -- Scalability -- ETL vs. ELT -- Disaster Recovery -- Overall Architecture -- Data Lakehouse -- What Is a Data Lake? -- What Is Delta Lake? -- What Is Apache Spark? -- What Is a Data Lakehouse? -- Characteristics of a Data Lakehouse -- Various Data Types -- AI -- Decoupled Compute and Storage Resources -- Open Source Storage Format -- Data Analytics and BI Tools -- ACID Properties -- Differences Between a Data Warehouse and a Data Lakehouse -- Architecture -- Access to Raw Data.
Open Source vs. Proprietary -- Workloads -- Query Engines -- Data Processing -- Real-Time Data -- Examples of Data Lakehouses -- Azure Synapse Analytics -- Databricks -- Benefits of Data Lakehouse -- Support for All Types of Data -- Time to Market -- More Cost Effective -- AI -- Reduction in ETL/ELT Jobs -- Usage of Open Source Tools and Technologies -- Efficient and Easy Data Governance -- Drawbacks of Data Lakehouse -- Monolithic Architecture -- Technical Infancy -- Migration Cost -- Lack of Many Products/Options -- Scarcity of Skilled Technical Resources -- Summary -- Chapter 3: Introduction to Azure Synapse Analytics -- What Is Azure Synapse Analytics? -- Azure Synapse Analytics vs. Azure SQL Data Warehouse -- Why Should You Learn Azure Synapse Analytics? -- Main Features of Azure Synapse Analytics -- Unified Data Analytics Experience -- Powerful Data Insights -- Unlimited Scale -- Security, Privacy, and Compliance -- HTAP -- Key Service Capabilities of Azure Synapse Analytics -- Data Lake Exploration -- Multiple Language Support -- Deeply Integrated Apache Spark -- Serverless Synapse SQL Pool -- Hybrid Data Integration -- Power BI Integration -- AI Integration -- Enterprise Data Warehousing -- Seamless Streaming Analytics -- Workload Management -- Advanced Security -- Summary -- Chapter 4: Architecture and Its Main Components -- High-Level Architecture -- Main Components of Architecture -- Synapse SQL -- Compute Layer -- Dedicated Synapse SQL Pool -- Serverless Synapse SQL Pool -- Storage Layer -- Synapse Spark or Apache Spark -- Synapse Pipelines -- Synapse Studio -- Synapse Link -- Summary -- Chapter 5: Synapse SQL -- Synapse SQL Architecture Components -- Massively Parallel Processing Engine -- Distributed Query Processing Engine -- Control Node -- Compute Nodes -- Data Movement Service -- Distribution -- Hash Distribution. Round-Robin Distribution -- Replication-based Distribution -- Azure Storage -- Dedicated or Provisioned Synapse SQL Pool -- Serverless or On-Demand Synapse SQL Pool -- Synapse SQL Feature Comparison -- Database Object Types -- Query Language -- Security -- Tools -- Storage Options -- Data Formats -- Resource Consumption Model for Synapse SQL -- Synapse SQL Best Practices -- Best Practices for Serverless Synapse SQL Pool -- Best Practices for Dedicated Synapse SQL Pool -- How-To's -- Create a Dedicated Synapse SQL Pool -- Create a Serverless or On-Demand Synapse SQL Pool -- Load Data Using COPY Statement in Dedicated Synapse SQL Pool -- Ingest Data into Azure Data Lake Storage Gen2 -- Summary -- Chapter 6: Synapse Spark -- What Is Apache Spark? -- What Is Synapse Spark in Azure Synapse Analytics? -- Synapse Spark Features & -- Capabilities -- Speed -- Faster Start Time -- Ease of Creation -- Ease of Use -- Security -- Automatic Scalability -- Separation of Concerns -- Multiple Language Support -- Integration with IDEs -- Pre-loaded Libraries -- REST APIs -- Delta Lake and Its Importance in Synapse Spark -- Synapse Spark Job Optimization -- Data Format -- Memory Management -- Data Serialization -- Data Caching -- Data Abstraction -- Join and Shuffle Optimization -- Bucketing -- Hyperspace Indexing -- Synapse Spark Machine Learning -- Data Preparation and Exploration -- Build Machine Learning Models -- Train Machine Learning Models -- Model Deployment and Scoring -- How-To's -- How to Create a Synapse Spark Pool -- How to Create and Submit Apache Spark Job Definition in Synapse Studio Using Python -- How to Monitor Synapse Spark Pools Using Synapse Studio -- Summary -- Chapter 7: Synapse Pipelines -- Overview of Azure Data Factory -- Overview of Synapse Pipelines -- Activities -- Pipelines -- Linked Services -- Dataset -- Integration Runtimes (IR). Azure Integration Runtime (Azure IR) -- Self-Hosted Integration Runtimes (SHIR) -- Azure SSIS Integration Runtimes (Azure SSIS IR) -- Control Flow -- Parameters -- Data Flow -- Data Movement Activities -- Category: Azure -- Category: Database -- Category: NoSQL -- Category: File -- Category: Generic -- Category: Services and Applications -- Data Transformation Activities -- Control Flow Activities -- Copy Pipeline Example -- Transformation Pipeline Example -- Pipeline Triggers -- Summary -- Chapter 8: Synapse Workspace and Studio -- What Is a Synapse Analytics Workspace? -- Synapse Analytics Workspace Components and Features -- Azure Data Lake Storage Gen2 Account and File System -- Serverless Synapse SQL Pool -- Shared Metadata Management -- Code Artifacts -- What Is Synapse Studio? -- Main Features of Synapse Studio -- Home Hub -- Data Hub -- Develop Hub -- Integrate Hub -- Monitor Hub -- Integration -- Activities -- Manage Hub -- Analytics Pools -- External Connections -- Integration -- Security -- Synapse Studio Capabilities -- Data Preparation -- Data Management -- Data Exploration -- Data Warehousing -- Data Visualization -- Machine Learning -- Power BI in Synapse Studio -- How-To's -- How to Create or Provision a New Azure Synapse Analytics Workspace Using Azure Portal -- How to Launch Azure Synapse Studio -- How to Link Power BI with Azure Synapse Studio -- Summary -- Chapter 9: Synapse Link -- OLTP vs. OLAP -- What Is HTAP? -- Benefits of HTAP -- No-ETL Analytics -- Instant Insights -- Reduced Data Duplication -- Simplified Technical Architecture -- What Is Azure Synapse Link? -- Azure Cosmos DB -- Azure Cosmos DB Analytical Store -- Columnar Storage -- Decoupling of Operational Store -- Automatic Data Synchronization -- SQL API and MongoDB API -- Analytical TTL -- Automatic Schema Updates -- Cost-Effective Archiving -- Scalability. When to Use Azure Synapse Link for Cosmos DB -- Azure Synapse Link Limitations -- Azure Synapse Link Use Cases -- Industrial IOT -- Predictive Maintenance Pipeline -- Operational Reporting -- Real-Time Applications -- Real-Time Personalization for E-Commerce Users -- How-To's -- How to Enable Azure Synapse Link for Azure Cosmos DB -- How to Create an Azure Cosmos DB Container with Analytical Store Using Azure Portal -- How to Connect to Azure Synapse Link for Azure Cosmos DB Using Azure Portal -- Summary -- Chapter 10: Azure Synapse Analytics Use Cases and Reference Architecture -- Where Should You Use Azure Synapse Analytics? -- Large Volume of Data -- Disparate Sources of Data -- Data Transformation -- Batch or Streaming Data -- Where Should You Not Use Azure Synapse Analytics? -- Use Cases for Azure Synapse Analytics -- Financial Services -- Manufacturing -- Retail -- Healthcare -- Reference Architectures for Azure Synapse Analytics -- Modern Data Warehouse Architecture -- Real-Time Analytics on Big Data Architecture -- Summary -- Index. |
| Record Nr. | UNINA-9910485588003321 |
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Shiyal Bhadresh
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| [Place of publication not identified] : , : Apress, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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