Azure Data Factory by example : practical implementation for data engineers / / Richard Swinbank |
Autore | Swinbank Richard |
Edizione | [1st ed. 2021.] |
Pubbl/distr/stampa | [Place of publication not identified] : , : Apress, , [2021] |
Descrizione fisica | 1 online resource (XXII, 335 p. 148 illus.) |
Disciplina | 658.40380285574 |
Soggetto topico | Data warehousing |
ISBN | 1-4842-7029-0 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | 1. Creating an Azure Data Factory Instance -- 2. Your First Pipeline -- 3. The Copy Data Activity -- 4. Expressions -- 5. Parameters -- 6. Controlling Flow -- 7. Data Flows -- 8. Integration Runtimes -- 9. Power Query in ADF -- 10. Publishing to ADF -- 11. Triggers -- 12. Monitoring. |
Record Nr. | UNINA-9910484859203321 |
Swinbank Richard
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[Place of publication not identified] : , : Apress, , [2021] | ||
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Lo trovi qui: Univ. Federico II | ||
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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 |
Shiyal Bhadresh
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[Place of publication not identified] : , : Apress, , [2021] | ||
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Lo trovi qui: Univ. Federico II | ||
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BigQuery for data warehousing : managed data analysis in the Google cloud / / Mark Mucchetti |
Autore | Mucchetti Mark |
Edizione | [1st ed. 2020.] |
Pubbl/distr/stampa | [Place of publication not identified] : , : Apress, , [2020] |
Descrizione fisica | 1 online resource (539 pages) |
Disciplina | 658.40380285574 |
Soggetto topico | Data warehousing |
ISBN | 1-4842-6186-0 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Part I. Building a Warehouse -- 1. Settling into BigQuery -- 2. Starting Your Warehouse Project -- 3. All My Data -- 4. Managing BigQuery Costs -- Part II. Filling the Warehouse -- 5. Loading Data Into the Warehouse -- 6. Streaming Data Into the Warehouse -- 7. Dataflow -- Part III. Using the Warehouse -- 8. Care and Feeding of Your Warehouse -- 9. Querying the Warehouse -- 10. Scheduling Jobs -- 11. Serverless Functions with GCP -- 12. Cloud Logging -- Part IV. Maintaining the Warehouse -- 13. Advanced BigQuery -- 14. Data Governance -- 15. Adapting to Long-Term Change -- Part V. Reporting On and Visualizing Your Data -- 16. Reporting -- 17. Dashboards and Visualization -- 18. Google Data Studio -- Part VI. Enhancing Your Data's Potential -- 19. BigQuery ML -- 20. Jupyter Notebooks and Public Datasets -- 21. Conclusion -- 22. Appendix A: Cloud Shell and Cloud SDK -- 23. Appendix B: Sample Project Charter. |
Record Nr. | UNINA-9910427044503321 |
Mucchetti Mark
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[Place of publication not identified] : , : Apress, , [2020] | ||
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Lo trovi qui: Univ. Federico II | ||
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Building a scalable data warehouse with data vault 2.0 / Daniel Linstedt, Michael Olschimke |
Autore | LINSTENDT, Daniel |
Pubbl/distr/stampa | Amsterdam, : Morgan Kaufmann, 2016 |
Descrizione fisica | Testo elettronico (PDF) (661 p.) |
Disciplina | 658.40380285574 |
Soggetto topico | Data warehouse |
Formato | Risorse elettroniche ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNISA-996459053103316 |
LINSTENDT, Daniel
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Amsterdam, : Morgan Kaufmann, 2016 | ||
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Lo trovi qui: Univ. di Salerno | ||
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Data warehouse design solutions / Christopher Adamson, Michael Venerable |
Autore | ADAMSON, Christopher |
Pubbl/distr/stampa | New York : J. Wiley & Sons, c1998 |
Descrizione fisica | XX, 523 p. ; 24 cm + cd-rom |
Disciplina | 658.40380285574 |
Altri autori (Persone) | VENERABLE, Michael |
Soggetto topico | Aziende - Servizi di informazione - Automazione |
ISBN | 0-471-25195-X |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNISA-990000377760203316 |
ADAMSON, Christopher
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New York : J. Wiley & Sons, c1998 | ||
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Lo trovi qui: Univ. di Salerno | ||
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Data warehouse systems : design and implementation / / Alejandro Vaisman and Esteban Zimányi |
Autore | Vaisman Alejandro |
Edizione | [Second edition.] |
Pubbl/distr/stampa | Berlin, Germany : , : Springer, , [2022] |
Descrizione fisica | 1 online resource (713 pages) |
Disciplina | 658.40380285574 |
Collana | Data-centric systems and applications |
Soggetto topico |
Data warehousing
Management information systems Database management |
ISBN | 3-662-65167-X |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Intro -- Foreword to the Second Edition -- Foreword to the First Edition -- Preface -- Objective of the Book -- Organization of the Book and Teaching Paths -- Acknowledgments -- About the Authors -- Contents -- Part I Fundamental Concepts -- Chapter 1 Introduction -- 1.1 An Overview of Data Warehousing -- 1.2 Emerging Data Warehousing Technologies -- 1.3 Review Questions -- Chapter 2 Database Concepts -- 2.1 Database Design -- 2.2 The Northwind Case Study -- 2.3 Conceptual Database Design -- 2.4 Logical Database Design -- 2.4.1 The Relational Model -- 2.4.2 Normalization -- 2.4.3 Relational Query Languages -- 2.5 Physical Database Design -- 2.6 Summary -- 2.7 Bibliographic Notes -- 2.8 Review Questions -- 2.9 Exercises -- Chapter 3 Data Warehouse Concepts -- 3.1 Multidimensional Model -- 3.1.1 Hierarchies -- 3.1.2 Measures -- 3.2 OLAP Operations -- 3.3 Data Warehouses -- 3.4 Data Warehouse Architecture -- 3.4.1 Back-End Tier -- 3.4.2 Data Warehouse Tier -- 3.4.3 OLAP Tier -- 3.4.4 Front-End Tier -- 3.4.5 Variations of the Architecture -- 3.5 Overview of Microsoft SQL Server BI Tools -- 3.6 Summary -- 3.7 Bibliographic Notes -- 3.8 Review Questions -- 3.9 Exercises -- Chapter 4 Conceptual Data Warehouse Design -- 4.1 Conceptual Modeling of Data Warehouses -- 4.2 Hierarchies -- 4.2.1 Balanced Hierarchies -- 4.2.2 Unbalanced Hierarchies -- 4.2.3 Generalized Hierarchies -- 4.2.4 Alternative Hierarchies -- 4.2.5 Parallel Hierarchies -- 4.2.6 Nonstrict Hierarchies -- 4.3 Advanced Modeling Aspects -- 4.3.1 Facts with Multiple Granularities -- 4.3.2 Many-to-Many Dimensions -- 4.3.3 Links between Facts -- 4.4 Querying the Northwind Cube Using the OLAP Operations -- 4.5 Summary -- 4.6 Bibliographic Notes -- 4.7 Review Questions -- 4.8 Exercises -- Chapter 5 Logical Data Warehouse Design -- 5.1 Logical Modeling of Data Warehouses.
5.2 Relational Data Warehouse Design -- 5.3 Relational Representation of Data Warehouses -- 5.4 Time Dimension -- 5.5 Logical Representation of Hierarchies -- 5.5.1 Balanced Hierarchies -- 5.5.2 Unbalanced Hierarchies -- 5.5.3 Generalized Hierarchies -- 5.5.4 Alternative Hierarchies -- 5.5.5 Parallel Hierarchies -- 5.5.6 Nonstrict Hierarchies -- 5.6 Advanced Modeling Aspects -- 5.6.1 Facts with Multiple Granularities -- 5.6.2 Many-to-Many Dimensions -- 5.6.3 Links between Facts -- 5.7 Slowly Changing Dimensions -- 5.8 Performing OLAP Queries with SQL -- 5.9 Defining the Northwind Data Warehouse in Analysis Services -- 5.9.1 Multidimensional Model -- 5.9.2 Tabular Model -- 5.10 Summary -- 5.11 Bibliographic Notes -- 5.12 Review Questions -- 5.13 Exercises -- Chapter 6 Data Analysis in Data Warehouses -- 6.1 Introduction to MDX -- 6.1.1 Tuples and Sets -- 6.1.2 Basic Queries -- 6.1.3 Slicing -- 6.1.4 Navigation -- 6.1.5 Cross Join -- 6.1.6 Subqueries -- 6.1.7 Calculated Members and Named Sets -- 6.1.8 Relative Navigation -- 6.1.9 Time-Related Calculations -- 6.1.10 Filtering -- 6.1.11 Sorting -- 6.1.12 Top and Bottom Analysis -- 6.1.13 Aggregation Functions -- 6.2 Introduction to DAX -- 6.2.1 Expressions -- 6.2.2 Evaluation Context -- 6.2.3 Queries -- 6.2.4 Filtering -- 6.2.5 Hierarchy Handling -- 6.2.6 Time-Related Calculations -- 6.2.7 Top and Bottom Analysis -- 6.2.8 Table Operations -- 6.3 Key Performance Indicators -- 6.3.1 Classification of Key Performance Indicators -- 6.3.2 Defining Key Performance Indicators -- 6.4 Dashboards -- 6.4.1 Types of Dashboards -- 6.4.2 Guidelines for Dashboard Design -- 6.5 Summary -- 6.6 Bibliographic Notes -- 6.7 Review Questions -- Chapter 7 Data Analysis in the Northwind Data Warehouse -- 7.1 Querying the Multidimensional Model in MDX -- 7.2 Querying the Tabular Model in DAX. 7.3 Querying the Relational Data Warehouse in SQL -- 7.4 Comparison of MDX, DAX, and SQL -- 7.5 KPIs for the Northwind Case Study -- 7.5.1 KPIs in Analysis Services Multidimensional -- 7.5.2 KPIs in Analysis Services Tabular -- 7.6 Dashboards for the Northwind Case Study -- 7.6.1 Dashboards in Reporting Services -- 7.6.2 Dashboards in Power BI -- 7.7 Summary -- 7.8 Review Questions -- 7.9 Exercises -- Part II Implementation and Deployment -- Chapter 8 Physical Data Warehouse Design -- 8.1 Physical Modeling of Data Warehouses -- 8.2 Materialized Views -- 8.2.1 Algorithms Using Full Information -- 8.2.2 Algorithms Using Partial Information -- 8.3 Data Cube Maintenance -- 8.4 Computation of a Data Cube -- 8.4.1 PipeSort Algorithm -- 8.4.2 Cube Size Estimation -- 8.4.3 Partial Computation of a Data Cube -- 8.5 Indexes for Data Warehouses -- 8.5.1 Bitmap Indexes -- 8.5.2 Bitmap Compression -- 8.5.3 Join Indexes -- 8.6 Evaluation of Star Queries -- 8.7 Partitioning -- 8.8 Parallel Processing -- 8.9 Physical Design in SQL Server and Analysis Services -- 8.9.1 Indexed Views -- 8.9.2 Partition-Aligned Indexed Views -- 8.9.3 Column-Store Indexes -- 8.9.4 Partitions in Analysis Services -- 8.10 Query Performance in Analysis Services -- 8.11 Summary -- 8.12 Bibliographic Notes -- 8.13 Review Questions -- 8.14 Exercises -- Chapter 9 Extraction, Transformation, and Loading -- 9.1 Business Process Modeling Notation -- 9.2 Conceptual ETL Design Using BPMN -- 9.3 Conceptual Design of the Northwind ETL Process -- 9.4 SQL Server Integration Services -- 9.5 The Northwind ETL Process in Integration Services -- 9.6 Implementing ETL Processes in SQL -- 9.7 Summary -- 9.8 Bibliographic Notes -- 9.9 Review Questions -- 9.10 Exercises -- Chapter 10 A Method for Data Warehouse Design -- 10.1 Approaches to Data Warehouse Design -- 10.2 General Overview of the Method. 10.3 Requirements Specification -- 10.3.1 Business-Driven Requirements Specification -- 10.3.2 Data-driven Requirements Specification -- 10.3.3 Business/Data-driven Requirements Specification -- 10.4 Conceptual Design -- 10.4.1 Business-Driven Conceptual Design -- 10.4.2 Data-driven Conceptual Design -- 10.4.3 Business/Data-driven Conceptual Design -- 10.5 Logical Design -- 10.5.1 Logical Schemas -- 10.5.2 ETL Processes -- 10.6 Physical Design -- 10.7 Characterization of the Various Approaches -- 10.7.1 Business-Driven Approach -- 10.7.2 Data-driven Approach -- 10.7.3 Business/Data-driven Approach -- 10.8 Summary -- 10.9 Bibliographic Notes -- 10.10 Review Questions -- 10.11 Exercises -- Part III Advanced Topics -- Chapter 11 Temporal and Multiversion Data Warehouses -- 11.1 Manipulating Temporal Information in SQL -- 11.2 Conceptual Design of Temporal Data Warehouses -- 11.2.1 Time Data Types -- 11.2.2 Synchronization Relationships -- 11.2.3 A Conceptual Model for Temporal Data Warehouses -- 11.2.4 Temporal Hierarchies -- 11.2.5 Temporal Facts -- 11.3 Logical Design of Temporal Data Warehouses -- 11.4 Implementation Considerations -- 11.4.1 Period Encoding -- 11.4.2 Tables for Temporal Roll-Up -- 11.4.3 Integrity Constraints -- 11.4.4 Measure Aggregation -- 11.4.5 Temporal Measures -- 11.5 Querying the Temporal Northwind Data Warehouse in SQL -- 11.6 Temporal Data Warehouses versus Slowly Changing Dimensions -- 11.7 Conceptual Design of Multiversion Data Warehouses -- 11.8 Logical Design of Multiversion Data Warehouses -- 11.9 Querying the Multiversion Northwind Data Warehouse in SQL -- 11.10 Summary -- 11.11 Bibliographic Notes -- 11.12 Review Questions -- 11.13 Exercises -- Chapter 12 Spatial and Mobility Data Warehouses -- 12.1 Conceptual Design of Spatial Data Warehouses -- 12.1.1 Spatial Data Types -- 12.1.2 Topological relationships. 12.1.3 Continuous Fields -- 12.1.4 A Conceptual Model of Spatial Data Warehouses -- 12.2 Implementation Considerations for Spatial Data -- 12.2.1 Spatial Reference Systems -- 12.2.2 Vector Model -- 12.2.3 Raster Model -- 12.3 Logical Design of Spatial Data Warehouses -- 12.4 Topological Constraints -- 12.5 Querying the GeoNorthwind Data Warehouse in SQL -- 12.6 Mobility Data Analysis -- 12.7 Temporal Types -- 12.8 Temporal Types in MobilityDB -- 12.9 Mobility Data Warehouses -- 12.10 Querying the Northwind Mobility Data Warehouse in SQL -- 12.11 Summary -- 12.12 Bibliographic Notes -- 12.13 Review Questions -- 12.14 Exercises -- Chapter 13 Graph Data Warehouses -- 13.1 Graph Data Models -- 13.2 Property Graph Database Systems -- 13.2.1 Neo4j -- 13.2.2 Introduction to Cypher -- 13.2.3 Querying the Northwind Cube with Cypher -- 13.3 OLAP on Hypergraphs -- 13.3.1 Operations on Hypergraphs -- 13.3.2 OLAP on Trajectory Graphs -- 13.4 Graph Processing Frameworks -- 13.4.1 Gremlin -- 13.4.2 JanusGraph -- 13.5 Bibliographic Notes -- 13.6 Review Questions -- 13.7 Exercises -- Chapter 14 Semantic Web Data Warehouses -- 14.1 Semantic Web -- 14.1.1 Introduction to RDF and RDFS -- 14.1.2 RDF Serializations -- 14.1.3 RDF Representation of Relational Data -- 14.2 Introduction to SPARQL -- 14.2.1 SPARQL Basics -- 14.2.2 SPARQL Semantics -- 14.3 RDF Representation of Multidimensional Data -- 14.4 Representation of the Northwind Cube in QB4OLAP -- 14.5 Querying the Northwind Cube in SPARQL -- 14.6 Summary -- 14.7 Bibliographic Notes -- 14.8 Review Questions -- 14.9 Exercises -- Chapter 15 Recent Developments in Big Data Warehouses -- 15.1 Data Warehousing in the Age of Big Data -- 15.2 Distributed Processing Frameworks -- 15.2.1 Hadoop -- 15.2.2 Hive -- 15.2.3 Spark -- 15.2.4 Comparison of Hadoop and Spark -- 15.2.5 Kylin -- 15.3 Distributed Database Systems. 15.3.1 MySQL Cluster. |
Record Nr. | UNISA-996483163203316 |
Vaisman Alejandro
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Berlin, Germany : , : Springer, , [2022] | ||
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Lo trovi qui: Univ. di Salerno | ||
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Data warehouse systems : design and implementation / / Alejandro Vaisman and Esteban Zimányi |
Autore | Vaisman Alejandro |
Edizione | [Second edition.] |
Pubbl/distr/stampa | Berlin, Germany : , : Springer, , [2022] |
Descrizione fisica | 1 online resource (713 pages) |
Disciplina | 658.40380285574 |
Collana | Data-centric systems and applications |
Soggetto topico |
Data warehousing
Management information systems Database management |
ISBN | 3-662-65167-X |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Intro -- Foreword to the Second Edition -- Foreword to the First Edition -- Preface -- Objective of the Book -- Organization of the Book and Teaching Paths -- Acknowledgments -- About the Authors -- Contents -- Part I Fundamental Concepts -- Chapter 1 Introduction -- 1.1 An Overview of Data Warehousing -- 1.2 Emerging Data Warehousing Technologies -- 1.3 Review Questions -- Chapter 2 Database Concepts -- 2.1 Database Design -- 2.2 The Northwind Case Study -- 2.3 Conceptual Database Design -- 2.4 Logical Database Design -- 2.4.1 The Relational Model -- 2.4.2 Normalization -- 2.4.3 Relational Query Languages -- 2.5 Physical Database Design -- 2.6 Summary -- 2.7 Bibliographic Notes -- 2.8 Review Questions -- 2.9 Exercises -- Chapter 3 Data Warehouse Concepts -- 3.1 Multidimensional Model -- 3.1.1 Hierarchies -- 3.1.2 Measures -- 3.2 OLAP Operations -- 3.3 Data Warehouses -- 3.4 Data Warehouse Architecture -- 3.4.1 Back-End Tier -- 3.4.2 Data Warehouse Tier -- 3.4.3 OLAP Tier -- 3.4.4 Front-End Tier -- 3.4.5 Variations of the Architecture -- 3.5 Overview of Microsoft SQL Server BI Tools -- 3.6 Summary -- 3.7 Bibliographic Notes -- 3.8 Review Questions -- 3.9 Exercises -- Chapter 4 Conceptual Data Warehouse Design -- 4.1 Conceptual Modeling of Data Warehouses -- 4.2 Hierarchies -- 4.2.1 Balanced Hierarchies -- 4.2.2 Unbalanced Hierarchies -- 4.2.3 Generalized Hierarchies -- 4.2.4 Alternative Hierarchies -- 4.2.5 Parallel Hierarchies -- 4.2.6 Nonstrict Hierarchies -- 4.3 Advanced Modeling Aspects -- 4.3.1 Facts with Multiple Granularities -- 4.3.2 Many-to-Many Dimensions -- 4.3.3 Links between Facts -- 4.4 Querying the Northwind Cube Using the OLAP Operations -- 4.5 Summary -- 4.6 Bibliographic Notes -- 4.7 Review Questions -- 4.8 Exercises -- Chapter 5 Logical Data Warehouse Design -- 5.1 Logical Modeling of Data Warehouses.
5.2 Relational Data Warehouse Design -- 5.3 Relational Representation of Data Warehouses -- 5.4 Time Dimension -- 5.5 Logical Representation of Hierarchies -- 5.5.1 Balanced Hierarchies -- 5.5.2 Unbalanced Hierarchies -- 5.5.3 Generalized Hierarchies -- 5.5.4 Alternative Hierarchies -- 5.5.5 Parallel Hierarchies -- 5.5.6 Nonstrict Hierarchies -- 5.6 Advanced Modeling Aspects -- 5.6.1 Facts with Multiple Granularities -- 5.6.2 Many-to-Many Dimensions -- 5.6.3 Links between Facts -- 5.7 Slowly Changing Dimensions -- 5.8 Performing OLAP Queries with SQL -- 5.9 Defining the Northwind Data Warehouse in Analysis Services -- 5.9.1 Multidimensional Model -- 5.9.2 Tabular Model -- 5.10 Summary -- 5.11 Bibliographic Notes -- 5.12 Review Questions -- 5.13 Exercises -- Chapter 6 Data Analysis in Data Warehouses -- 6.1 Introduction to MDX -- 6.1.1 Tuples and Sets -- 6.1.2 Basic Queries -- 6.1.3 Slicing -- 6.1.4 Navigation -- 6.1.5 Cross Join -- 6.1.6 Subqueries -- 6.1.7 Calculated Members and Named Sets -- 6.1.8 Relative Navigation -- 6.1.9 Time-Related Calculations -- 6.1.10 Filtering -- 6.1.11 Sorting -- 6.1.12 Top and Bottom Analysis -- 6.1.13 Aggregation Functions -- 6.2 Introduction to DAX -- 6.2.1 Expressions -- 6.2.2 Evaluation Context -- 6.2.3 Queries -- 6.2.4 Filtering -- 6.2.5 Hierarchy Handling -- 6.2.6 Time-Related Calculations -- 6.2.7 Top and Bottom Analysis -- 6.2.8 Table Operations -- 6.3 Key Performance Indicators -- 6.3.1 Classification of Key Performance Indicators -- 6.3.2 Defining Key Performance Indicators -- 6.4 Dashboards -- 6.4.1 Types of Dashboards -- 6.4.2 Guidelines for Dashboard Design -- 6.5 Summary -- 6.6 Bibliographic Notes -- 6.7 Review Questions -- Chapter 7 Data Analysis in the Northwind Data Warehouse -- 7.1 Querying the Multidimensional Model in MDX -- 7.2 Querying the Tabular Model in DAX. 7.3 Querying the Relational Data Warehouse in SQL -- 7.4 Comparison of MDX, DAX, and SQL -- 7.5 KPIs for the Northwind Case Study -- 7.5.1 KPIs in Analysis Services Multidimensional -- 7.5.2 KPIs in Analysis Services Tabular -- 7.6 Dashboards for the Northwind Case Study -- 7.6.1 Dashboards in Reporting Services -- 7.6.2 Dashboards in Power BI -- 7.7 Summary -- 7.8 Review Questions -- 7.9 Exercises -- Part II Implementation and Deployment -- Chapter 8 Physical Data Warehouse Design -- 8.1 Physical Modeling of Data Warehouses -- 8.2 Materialized Views -- 8.2.1 Algorithms Using Full Information -- 8.2.2 Algorithms Using Partial Information -- 8.3 Data Cube Maintenance -- 8.4 Computation of a Data Cube -- 8.4.1 PipeSort Algorithm -- 8.4.2 Cube Size Estimation -- 8.4.3 Partial Computation of a Data Cube -- 8.5 Indexes for Data Warehouses -- 8.5.1 Bitmap Indexes -- 8.5.2 Bitmap Compression -- 8.5.3 Join Indexes -- 8.6 Evaluation of Star Queries -- 8.7 Partitioning -- 8.8 Parallel Processing -- 8.9 Physical Design in SQL Server and Analysis Services -- 8.9.1 Indexed Views -- 8.9.2 Partition-Aligned Indexed Views -- 8.9.3 Column-Store Indexes -- 8.9.4 Partitions in Analysis Services -- 8.10 Query Performance in Analysis Services -- 8.11 Summary -- 8.12 Bibliographic Notes -- 8.13 Review Questions -- 8.14 Exercises -- Chapter 9 Extraction, Transformation, and Loading -- 9.1 Business Process Modeling Notation -- 9.2 Conceptual ETL Design Using BPMN -- 9.3 Conceptual Design of the Northwind ETL Process -- 9.4 SQL Server Integration Services -- 9.5 The Northwind ETL Process in Integration Services -- 9.6 Implementing ETL Processes in SQL -- 9.7 Summary -- 9.8 Bibliographic Notes -- 9.9 Review Questions -- 9.10 Exercises -- Chapter 10 A Method for Data Warehouse Design -- 10.1 Approaches to Data Warehouse Design -- 10.2 General Overview of the Method. 10.3 Requirements Specification -- 10.3.1 Business-Driven Requirements Specification -- 10.3.2 Data-driven Requirements Specification -- 10.3.3 Business/Data-driven Requirements Specification -- 10.4 Conceptual Design -- 10.4.1 Business-Driven Conceptual Design -- 10.4.2 Data-driven Conceptual Design -- 10.4.3 Business/Data-driven Conceptual Design -- 10.5 Logical Design -- 10.5.1 Logical Schemas -- 10.5.2 ETL Processes -- 10.6 Physical Design -- 10.7 Characterization of the Various Approaches -- 10.7.1 Business-Driven Approach -- 10.7.2 Data-driven Approach -- 10.7.3 Business/Data-driven Approach -- 10.8 Summary -- 10.9 Bibliographic Notes -- 10.10 Review Questions -- 10.11 Exercises -- Part III Advanced Topics -- Chapter 11 Temporal and Multiversion Data Warehouses -- 11.1 Manipulating Temporal Information in SQL -- 11.2 Conceptual Design of Temporal Data Warehouses -- 11.2.1 Time Data Types -- 11.2.2 Synchronization Relationships -- 11.2.3 A Conceptual Model for Temporal Data Warehouses -- 11.2.4 Temporal Hierarchies -- 11.2.5 Temporal Facts -- 11.3 Logical Design of Temporal Data Warehouses -- 11.4 Implementation Considerations -- 11.4.1 Period Encoding -- 11.4.2 Tables for Temporal Roll-Up -- 11.4.3 Integrity Constraints -- 11.4.4 Measure Aggregation -- 11.4.5 Temporal Measures -- 11.5 Querying the Temporal Northwind Data Warehouse in SQL -- 11.6 Temporal Data Warehouses versus Slowly Changing Dimensions -- 11.7 Conceptual Design of Multiversion Data Warehouses -- 11.8 Logical Design of Multiversion Data Warehouses -- 11.9 Querying the Multiversion Northwind Data Warehouse in SQL -- 11.10 Summary -- 11.11 Bibliographic Notes -- 11.12 Review Questions -- 11.13 Exercises -- Chapter 12 Spatial and Mobility Data Warehouses -- 12.1 Conceptual Design of Spatial Data Warehouses -- 12.1.1 Spatial Data Types -- 12.1.2 Topological relationships. 12.1.3 Continuous Fields -- 12.1.4 A Conceptual Model of Spatial Data Warehouses -- 12.2 Implementation Considerations for Spatial Data -- 12.2.1 Spatial Reference Systems -- 12.2.2 Vector Model -- 12.2.3 Raster Model -- 12.3 Logical Design of Spatial Data Warehouses -- 12.4 Topological Constraints -- 12.5 Querying the GeoNorthwind Data Warehouse in SQL -- 12.6 Mobility Data Analysis -- 12.7 Temporal Types -- 12.8 Temporal Types in MobilityDB -- 12.9 Mobility Data Warehouses -- 12.10 Querying the Northwind Mobility Data Warehouse in SQL -- 12.11 Summary -- 12.12 Bibliographic Notes -- 12.13 Review Questions -- 12.14 Exercises -- Chapter 13 Graph Data Warehouses -- 13.1 Graph Data Models -- 13.2 Property Graph Database Systems -- 13.2.1 Neo4j -- 13.2.2 Introduction to Cypher -- 13.2.3 Querying the Northwind Cube with Cypher -- 13.3 OLAP on Hypergraphs -- 13.3.1 Operations on Hypergraphs -- 13.3.2 OLAP on Trajectory Graphs -- 13.4 Graph Processing Frameworks -- 13.4.1 Gremlin -- 13.4.2 JanusGraph -- 13.5 Bibliographic Notes -- 13.6 Review Questions -- 13.7 Exercises -- Chapter 14 Semantic Web Data Warehouses -- 14.1 Semantic Web -- 14.1.1 Introduction to RDF and RDFS -- 14.1.2 RDF Serializations -- 14.1.3 RDF Representation of Relational Data -- 14.2 Introduction to SPARQL -- 14.2.1 SPARQL Basics -- 14.2.2 SPARQL Semantics -- 14.3 RDF Representation of Multidimensional Data -- 14.4 Representation of the Northwind Cube in QB4OLAP -- 14.5 Querying the Northwind Cube in SPARQL -- 14.6 Summary -- 14.7 Bibliographic Notes -- 14.8 Review Questions -- 14.9 Exercises -- Chapter 15 Recent Developments in Big Data Warehouses -- 15.1 Data Warehousing in the Age of Big Data -- 15.2 Distributed Processing Frameworks -- 15.2.1 Hadoop -- 15.2.2 Hive -- 15.2.3 Spark -- 15.2.4 Comparison of Hadoop and Spark -- 15.2.5 Kylin -- 15.3 Distributed Database Systems. 15.3.1 MySQL Cluster. |
Record Nr. | UNINA-9910584481803321 |
Vaisman Alejandro
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Berlin, Germany : , : Springer, , [2022] | ||
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Lo trovi qui: Univ. Federico II | ||
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Data Warehouse Systems : Design and Implementation / / by Alejandro Vaisman, Esteban Zimányi |
Autore | Vaisman Alejandro |
Edizione | [1st ed. 2014.] |
Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014 |
Descrizione fisica | 1 online resource (XVI, 625 p. 133 illus.) |
Disciplina | 658.40380285574 |
Collana | Data-Centric Systems and Applications |
Soggetto topico |
Database management
Information storage and retrieval Management information systems Application software Database Management Information Storage and Retrieval Business IT Infrastructure Computer Appl. in Administrative Data Processing |
ISBN | 3-642-54655-2 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Part I Fundamental Concepts -- 1 Introduction -- 2 Database Concepts -- 3 Data Warehouse Concepts -- 4 Conceptual Data Warehouse Design -- 5 Logical Data Warehouse Design -- 6 Querying Data Warehouses -- Part II Implementation and Deployment -- 7 Physical Data Warehouse Design -- 8 Extraction, Transformation and Loading -- 9 Data Analytics: Exploiting the Data Warehouse -- 10 A Method for Data Warehouse Design -- Part III Advanced Topics -- 11 Spatial Data Warehouses -- 12 Trajectory Data Warehouses -- 13 New Data Warehouse Technologies -- 14 Data Warehouses and the Semantic Web -- 15 Conclusion. |
Record Nr. | UNINA-9910298988003321 |
Vaisman Alejandro
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Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014 | ||
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Lo trovi qui: Univ. Federico II | ||
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The data warehouse toolkit [[electronic resource] ] : the definitive guide to dimensional modeling / / Ralph Kimball, Margy Ross |
Autore | Kimball Ralph |
Edizione | [3rd ed.] |
Pubbl/distr/stampa | Indianapolis, Ind., : Wiley, c2013 |
Descrizione fisica | 1 online resource (601 p.) |
Disciplina | 658.40380285574 |
Altri autori (Persone) | RossMargy |
Soggetto topico |
Data warehousing
Business enterprises - Data processing |
Soggetto genere / forma | Electronic books. |
ISBN |
1-118-73228-6
1-118-53077-2 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Cover; Title Page; Copyright; Contents; 1 Data Warehousing, Business Intelligence, and Dimensional Modeling Primer; Different Worlds of Data Capture and Data Analysis; Goals of Data Warehousing and Business Intelligence; Publishing Metaphor for DW/BI Managers; Dimensional Modeling Introduction; Star Schemas Versus OLAP Cubes; Fact Tables for Measurements; Dimension Tables for Descriptive Context; Facts and Dimensions Joined in a Star Schema; Kimball's DW/BI Architecture; Operational Source Systems; Extract, Transformation, and Load System; Presentation Area to Support Business Intelligence
Business Intelligence ApplicationsRestaurant Metaphor for the Kimball Architecture; Alternative DW/BI Architectures; Independent Data Mart Architecture; Hub-and-Spoke Corporate Information Factory Inmon Architecture; Hybrid Hub-and-Spoke and Kimball Architecture; Dimensional Modeling Myths; Myth 1: Dimensional Models are Only for Summary Data; Myth 2: Dimensional Models are Departmental, Not Enterprise; Myth 3: Dimensional Models are Not Scalable; Myth 4: Dimensional Models are Only for Predictable Usage; Myth 5: Dimensional Models Can't Be Integrated; More Reasons to Think Dimensionally Agile ConsiderationsSummary; 2 Kimball Dimensional Modeling Techniques Overview; Fundamental Concepts; Gather Business Requirements and Data Realities; Collaborative Dimensional Modeling Workshops; Four-Step Dimensional Design Process; Business Processes; Grain; Dimensions for Descriptive Context; Facts for Measurements; Star Schemas and OLAP Cubes; Graceful Extensions to Dimensional Models; Basic Fact Table Techniques; Fact Table Structure; Additive, Semi-Additive, Non-Additive Facts; Nulls in Fact Tables; Conformed Facts; Transaction Fact Tables; Periodic Snapshot Fact Tables Accumulating Snapshot Fact TablesFactless Fact Tables; Aggregate Fact Tables or OLAP Cubes; Consolidated Fact Tables; Basic Dimension Table Techniques; Dimension Table Structure; Dimension Surrogate Keys; Natural, Durable, and Supernatural Keys; Drilling Down; Degenerate Dimensions; Denormalized Flattened Dimensions; Multiple Hierarchies in Dimensions; Flags and Indicators as Textual Attributes; Null Attributes in Dimensions; Calendar Date Dimensions; Role-Playing Dimensions; Junk Dimensions; Snowflaked Dimensions; Outrigger Dimensions; Integration via Conformed Dimensions Conformed DimensionsShrunken Dimensions; Drilling Across; Value Chain; Enterprise Data Warehouse Bus Architecture; Enterprise Data Warehouse Bus Matrix; Detailed Implementation Bus Matrix; Opportunity/Stakeholder Matrix; Dealing with Slowly Changing Dimension Attributes; Type 0: Retain Original; Type 1: Overwrite; Type 2: Add New Row; Type 3: Add New Attribute; Type 4: Add Mini-Dimension; Type 5: Add Mini-Dimension and Type 1 Outrigger; Type 6: Add Type 1 Attributes to Type 2 Dimension; Type 7: Dual Type 1 and Type 2 Dimensions; Dealing with Dimension Hierarchies Fixed Depth Positional Hierarchies |
Record Nr. | UNINA-9910452802803321 |
Kimball Ralph
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Indianapolis, Ind., : Wiley, c2013 | ||
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Lo trovi qui: Univ. Federico II | ||
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The data warehouse toolkit : the definitive guide to dimensional modeling / / Ralph Kimball, Margy Ross |
Autore | Kimball Ralph |
Edizione | [Third edition] |
Pubbl/distr/stampa | Indianapolis, Ind. : , : Wiley, , [2013] |
Descrizione fisica | 1 online resource (601 p.) |
Disciplina | 658.40380285574 |
Altri autori (Persone) | RossMargy |
Soggetto topico |
Gestor de dades
Empreses - Informàtica Data warehousing Business enterprises - Data processing |
ISBN |
1-118-73228-6
1-118-53077-2 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Cover; Title Page; Copyright; Contents; 1 Data Warehousing, Business Intelligence, and Dimensional Modeling Primer; Different Worlds of Data Capture and Data Analysis; Goals of Data Warehousing and Business Intelligence; Publishing Metaphor for DW/BI Managers; Dimensional Modeling Introduction; Star Schemas Versus OLAP Cubes; Fact Tables for Measurements; Dimension Tables for Descriptive Context; Facts and Dimensions Joined in a Star Schema; Kimball's DW/BI Architecture; Operational Source Systems; Extract, Transformation, and Load System; Presentation Area to Support Business Intelligence
Business Intelligence ApplicationsRestaurant Metaphor for the Kimball Architecture; Alternative DW/BI Architectures; Independent Data Mart Architecture; Hub-and-Spoke Corporate Information Factory Inmon Architecture; Hybrid Hub-and-Spoke and Kimball Architecture; Dimensional Modeling Myths; Myth 1: Dimensional Models are Only for Summary Data; Myth 2: Dimensional Models are Departmental, Not Enterprise; Myth 3: Dimensional Models are Not Scalable; Myth 4: Dimensional Models are Only for Predictable Usage; Myth 5: Dimensional Models Can't Be Integrated; More Reasons to Think Dimensionally Agile ConsiderationsSummary; 2 Kimball Dimensional Modeling Techniques Overview; Fundamental Concepts; Gather Business Requirements and Data Realities; Collaborative Dimensional Modeling Workshops; Four-Step Dimensional Design Process; Business Processes; Grain; Dimensions for Descriptive Context; Facts for Measurements; Star Schemas and OLAP Cubes; Graceful Extensions to Dimensional Models; Basic Fact Table Techniques; Fact Table Structure; Additive, Semi-Additive, Non-Additive Facts; Nulls in Fact Tables; Conformed Facts; Transaction Fact Tables; Periodic Snapshot Fact Tables Accumulating Snapshot Fact TablesFactless Fact Tables; Aggregate Fact Tables or OLAP Cubes; Consolidated Fact Tables; Basic Dimension Table Techniques; Dimension Table Structure; Dimension Surrogate Keys; Natural, Durable, and Supernatural Keys; Drilling Down; Degenerate Dimensions; Denormalized Flattened Dimensions; Multiple Hierarchies in Dimensions; Flags and Indicators as Textual Attributes; Null Attributes in Dimensions; Calendar Date Dimensions; Role-Playing Dimensions; Junk Dimensions; Snowflaked Dimensions; Outrigger Dimensions; Integration via Conformed Dimensions Conformed DimensionsShrunken Dimensions; Drilling Across; Value Chain; Enterprise Data Warehouse Bus Architecture; Enterprise Data Warehouse Bus Matrix; Detailed Implementation Bus Matrix; Opportunity/Stakeholder Matrix; Dealing with Slowly Changing Dimension Attributes; Type 0: Retain Original; Type 1: Overwrite; Type 2: Add New Row; Type 3: Add New Attribute; Type 4: Add Mini-Dimension; Type 5: Add Mini-Dimension and Type 1 Outrigger; Type 6: Add Type 1 Attributes to Type 2 Dimension; Type 7: Dual Type 1 and Type 2 Dimensions; Dealing with Dimension Hierarchies Fixed Depth Positional Hierarchies |
Record Nr. | UNINA-9910779708303321 |
Kimball Ralph
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Indianapolis, Ind. : , : Wiley, , [2013] | ||
![]() | ||
Lo trovi qui: Univ. Federico II | ||
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