The Modern Data Warehouse in Azure: Building with Speed and Agility on Microsoft's Cloud Platform
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berkeley, CA
Apress L. P.
2020
|
| Schlagworte: | |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (297 pages) |
| ISBN: | 9781484258231 |
Internformat
MARC
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| 245 | 1 | 0 | |a The Modern Data Warehouse in Azure |b Building with Speed and Agility on Microsoft's Cloud Platform |
| 264 | 1 | |a Berkeley, CA |b Apress L. P. |c 2020 | |
| 264 | 4 | |c ©2020 | |
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| 500 | |a Description based on publisher supplied metadata and other sources | ||
| 505 | 8 | |a Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: The Rise of the Modern Data Warehouse -- Getting Started -- Multi-region Support -- Resource Groups and Tagging -- Azure Security -- Tools of the Trade -- Glossary of Terms -- Naming Conventions -- Chapter 2: The SQL Engine -- The Four Vs -- Azure Synapse Analytics -- Understanding Distributions -- The First Problem -- ROUND ROBIN Distribution -- HASH Distribution -- The Distribution Column -- How to Check if You Have the Right Column -- REPLICATED Distribution -- Resource Management -- Resource Classes -- Static Resource Classes -- Dynamic Resource Classes -- Pausing and Resuming the Warehouse -- Workload Management -- PolyBase -- Azure SQL Database -- The Cloud-Based OLTP Engine -- The Benefits of Azure SQL Database -- Improved Concurrency -- Trickle-Fed Data Warehouses -- Managing Slowly Changing Dimensions -- Intelligent Query Processing and Tuning -- Automatic Tuning -- Adaptive Query Processing -- Batch Mode Memory Grant Feedback -- Adaptive Joins -- Interleaved Execution -- Hyperscale -- The Hyperscale Architecture -- Accelerated Disaster Recovery -- Azure SQL Deployment Options -- Azure SQL Database Managed Instances -- Azure SQL Database Elastic Pools -- Azure SQL Database V-Core Tiers -- Azure Synapse Analytics vs. Azure SQL Database -- The Right Type of Data -- The Size of the Data -- The Frequency of the Data -- The Availability of the Data -- The Integration of Data -- Chapter 3: The Integration Engine -- Introduction to Azure Data Factory -- The Data Factory Building Blocks -- Linked Services -- Integration Runtimes -- Self-Hosted Integration Runtime -- Azure SSIS Integration Runtime -- Triggers -- Datasets -- Pipelines and Activities -- Activity Types -- External Compute Activities -- Internal Activities | |
| 505 | 8 | |a Iteration and Conditional Activities -- Web Activities -- Output Constraints -- Implementing Azure Data Factory -- Security in Azure Data Factory -- Using the Managed Service Identity -- Source Control of Azure Data Factory -- Templates -- Solution Structure -- Getting Started with Azure Data Factory -- Create Linked Services -- Creating Datasets -- Creating Pipelines -- Debugging Your Pipelines -- Monitoring Your Pipelines -- Parameter-Driven Pipelines -- Getting Started with Parameters -- Using the Lookup Activity -- Getting Started with the Lookup Activity -- Additional Azure Data Factory Elements -- Additional Invocation Methods -- Mapping Data Flows -- Multiple Inputs and Outputs -- Schema Modifier -- Row Modifier -- Execute Mapping Data Flows -- Azure Data Factory Processing Patterns -- Linear Pipelines -- Parent-Child Processing -- Iterative Parent-Child Processing -- Dynamic Column Mappings -- Partitioning Datasets -- Chapter 4: The Ingestion Architecture -- Layers of Curation -- The Raw Layer -- The Clean Layer -- The Transformed Layer -- Understanding Ingestion Architecture -- Batch Ingestion -- The Risks and Opportunities of Batch Ingestion -- The ETL Window -- The ETL Anti-window -- Failure Investigation and Troubleshooting -- The Batch Ingestion Tools -- Batch Ingestion for Azure Synapse Analytics -- Create External Table As Select (CETAS) -- Event Ingestion -- The Risks and Opportunities of Event-Based Ingestion -- Implementing Event Ingestion -- Decoupled Processing -- Listening for Events -- Queuing Events -- Event Ingestion for Azure Synapse Analytics -- Event Ingestion for Azure SQL Database -- Stream Ingestion -- The Risks and Opportunities of Stream Ingestion -- Implementing Stream Ingestion -- Stream Ingestion with Azure Event Hub's and Stream Analytics Jobs -- Stream Ingestion for Azure Blob Storage | |
| 505 | 8 | |a Stream Ingestion for Azure SQL Database -- The Lambda Architecture -- Blending Streams and Batches -- The Serving Layer -- Assessing the Approach -- Chapter 5: The Role of the Data Lake -- The Modern Enterprise and Its Data Lake -- Azure Data Lake Technology -- Azure Data Lake Gen 1 -- Azure Blob Storage -- Azure Data Lake Gen 2 -- Planning the Enterprise Data Lake -- Storing Raw Data -- Storing Cleaned Data -- Storing Transformed Data -- Facilitating Experimentation -- Implementing the Enterprise Data Lake -- Security Configuration in Azure Data Lake -- Applying Security in Azure Data Lake Gen 2 -- Implementing a Raw Directory -- Partitioning -- Choosing a File Format -- Implementing a Clean Directory -- Cleaning Within a Database -- Cleaning Within a Data Lake -- Cleaning Within Azure Data Factory -- Implementing a Transformed Directory -- Example Polyglot Architectures -- Example One -- Example Two -- Example Three -- Example Four -- Chapter 6: The Role of the Data Contract -- What Is a Data Contract? -- Working with Data Contracts -- Designing Data Contracts -- Generating Data Contracts -- Validating Data Contacts -- Storing Data Contracts -- Modifying Data Contracts -- Integrating Data Contracts -- Fetching Metadata -- Fetching Orchestration Metadata -- Utilizing Orchestration Metadata -- Fetching Entity Metadata -- Utilizing Entity Metadata -- Code Generation -- Getting Started with Code Generation -- Harmonizing Schema Evolution -- Chapter 7: Logging, Auditing, and Resilience -- Logging the Data Movement Process -- Basic Logging Requirements -- Where to Store Your Logs -- Events to Be Logged -- Extended Logging Capabilities -- Aggregating Your Logs -- Auditing the Data Movement Process -- Basic Auditing Requirements -- Auditing Data Volumes -- Auditing Processing Times -- Storing High Watermarks | |
| 505 | 8 | |a Incorporating Resilience into the Data Movement Process -- Basic Resiliency -- Using Metadata for Troubleshooting -- Creating Alerts Using Azure Data Factory Alert Rules -- Creating Custom Alerts from Azure Data Factory -- Extending Resiliency -- Utilizing Data Factory Fault Tolerance -- Checking File Structure Using Data Factory -- Creating Alerts from Skipped Rows -- Monitoring the Data Movement Process -- Chapter 8: Using Scripting and Automation -- The Power of PowerShell -- Commonly Used Scripts -- Code Generation -- Invoke Data Factory Pipeline -- Recurse Data Lake Structures -- Chapter 9: Beyond the Modern Data Warehouse -- Microsoft Power BI -- Working with Power BI -- Building a Power BI Report -- Publish Report to Power BI Service -- Azure Analysis Services -- The Basics of Azure Analysis Services -- Analysis Services as a Semantic Layer -- Analysis Services Security Model -- The Vertipaq Engine -- Creating an Analysis Services Project -- Create Analysis Objects -- Create a Calculated Column -- Create a Measure -- Create a KPI -- Create a Hierarchy -- Create a Perspective -- Creating Roles (RBAC) -- Deploy Analysis Services to Azure -- Processing an Azure Analysis Services Model -- Azure Cosmos DB -- The Cosmos DB Architecture -- Horizontal Partitioning -- Resource Units -- Consistency -- Write Data to Azure Cosmos DB -- Index | |
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| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a How, Matt |t The Modern Data Warehouse in Azure |d Berkeley, CA : Apress L. P.,c2020 |z 9781484258224 |
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Datensatz im Suchindex
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| author | How, Matt |
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| contents | Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: The Rise of the Modern Data Warehouse -- Getting Started -- Multi-region Support -- Resource Groups and Tagging -- Azure Security -- Tools of the Trade -- Glossary of Terms -- Naming Conventions -- Chapter 2: The SQL Engine -- The Four Vs -- Azure Synapse Analytics -- Understanding Distributions -- The First Problem -- ROUND ROBIN Distribution -- HASH Distribution -- The Distribution Column -- How to Check if You Have the Right Column -- REPLICATED Distribution -- Resource Management -- Resource Classes -- Static Resource Classes -- Dynamic Resource Classes -- Pausing and Resuming the Warehouse -- Workload Management -- PolyBase -- Azure SQL Database -- The Cloud-Based OLTP Engine -- The Benefits of Azure SQL Database -- Improved Concurrency -- Trickle-Fed Data Warehouses -- Managing Slowly Changing Dimensions -- Intelligent Query Processing and Tuning -- Automatic Tuning -- Adaptive Query Processing -- Batch Mode Memory Grant Feedback -- Adaptive Joins -- Interleaved Execution -- Hyperscale -- The Hyperscale Architecture -- Accelerated Disaster Recovery -- Azure SQL Deployment Options -- Azure SQL Database Managed Instances -- Azure SQL Database Elastic Pools -- Azure SQL Database V-Core Tiers -- Azure Synapse Analytics vs. Azure SQL Database -- The Right Type of Data -- The Size of the Data -- The Frequency of the Data -- The Availability of the Data -- The Integration of Data -- Chapter 3: The Integration Engine -- Introduction to Azure Data Factory -- The Data Factory Building Blocks -- Linked Services -- Integration Runtimes -- Self-Hosted Integration Runtime -- Azure SSIS Integration Runtime -- Triggers -- Datasets -- Pipelines and Activities -- Activity Types -- External Compute Activities -- Internal Activities Iteration and Conditional Activities -- Web Activities -- Output Constraints -- Implementing Azure Data Factory -- Security in Azure Data Factory -- Using the Managed Service Identity -- Source Control of Azure Data Factory -- Templates -- Solution Structure -- Getting Started with Azure Data Factory -- Create Linked Services -- Creating Datasets -- Creating Pipelines -- Debugging Your Pipelines -- Monitoring Your Pipelines -- Parameter-Driven Pipelines -- Getting Started with Parameters -- Using the Lookup Activity -- Getting Started with the Lookup Activity -- Additional Azure Data Factory Elements -- Additional Invocation Methods -- Mapping Data Flows -- Multiple Inputs and Outputs -- Schema Modifier -- Row Modifier -- Execute Mapping Data Flows -- Azure Data Factory Processing Patterns -- Linear Pipelines -- Parent-Child Processing -- Iterative Parent-Child Processing -- Dynamic Column Mappings -- Partitioning Datasets -- Chapter 4: The Ingestion Architecture -- Layers of Curation -- The Raw Layer -- The Clean Layer -- The Transformed Layer -- Understanding Ingestion Architecture -- Batch Ingestion -- The Risks and Opportunities of Batch Ingestion -- The ETL Window -- The ETL Anti-window -- Failure Investigation and Troubleshooting -- The Batch Ingestion Tools -- Batch Ingestion for Azure Synapse Analytics -- Create External Table As Select (CETAS) -- Event Ingestion -- The Risks and Opportunities of Event-Based Ingestion -- Implementing Event Ingestion -- Decoupled Processing -- Listening for Events -- Queuing Events -- Event Ingestion for Azure Synapse Analytics -- Event Ingestion for Azure SQL Database -- Stream Ingestion -- The Risks and Opportunities of Stream Ingestion -- Implementing Stream Ingestion -- Stream Ingestion with Azure Event Hub's and Stream Analytics Jobs -- Stream Ingestion for Azure Blob Storage Stream Ingestion for Azure SQL Database -- The Lambda Architecture -- Blending Streams and Batches -- The Serving Layer -- Assessing the Approach -- Chapter 5: The Role of the Data Lake -- The Modern Enterprise and Its Data Lake -- Azure Data Lake Technology -- Azure Data Lake Gen 1 -- Azure Blob Storage -- Azure Data Lake Gen 2 -- Planning the Enterprise Data Lake -- Storing Raw Data -- Storing Cleaned Data -- Storing Transformed Data -- Facilitating Experimentation -- Implementing the Enterprise Data Lake -- Security Configuration in Azure Data Lake -- Applying Security in Azure Data Lake Gen 2 -- Implementing a Raw Directory -- Partitioning -- Choosing a File Format -- Implementing a Clean Directory -- Cleaning Within a Database -- Cleaning Within a Data Lake -- Cleaning Within Azure Data Factory -- Implementing a Transformed Directory -- Example Polyglot Architectures -- Example One -- Example Two -- Example Three -- Example Four -- Chapter 6: The Role of the Data Contract -- What Is a Data Contract? -- Working with Data Contracts -- Designing Data Contracts -- Generating Data Contracts -- Validating Data Contacts -- Storing Data Contracts -- Modifying Data Contracts -- Integrating Data Contracts -- Fetching Metadata -- Fetching Orchestration Metadata -- Utilizing Orchestration Metadata -- Fetching Entity Metadata -- Utilizing Entity Metadata -- Code Generation -- Getting Started with Code Generation -- Harmonizing Schema Evolution -- Chapter 7: Logging, Auditing, and Resilience -- Logging the Data Movement Process -- Basic Logging Requirements -- Where to Store Your Logs -- Events to Be Logged -- Extended Logging Capabilities -- Aggregating Your Logs -- Auditing the Data Movement Process -- Basic Auditing Requirements -- Auditing Data Volumes -- Auditing Processing Times -- Storing High Watermarks Incorporating Resilience into the Data Movement Process -- Basic Resiliency -- Using Metadata for Troubleshooting -- Creating Alerts Using Azure Data Factory Alert Rules -- Creating Custom Alerts from Azure Data Factory -- Extending Resiliency -- Utilizing Data Factory Fault Tolerance -- Checking File Structure Using Data Factory -- Creating Alerts from Skipped Rows -- Monitoring the Data Movement Process -- Chapter 8: Using Scripting and Automation -- The Power of PowerShell -- Commonly Used Scripts -- Code Generation -- Invoke Data Factory Pipeline -- Recurse Data Lake Structures -- Chapter 9: Beyond the Modern Data Warehouse -- Microsoft Power BI -- Working with Power BI -- Building a Power BI Report -- Publish Report to Power BI Service -- Azure Analysis Services -- The Basics of Azure Analysis Services -- Analysis Services as a Semantic Layer -- Analysis Services Security Model -- The Vertipaq Engine -- Creating an Analysis Services Project -- Create Analysis Objects -- Create a Calculated Column -- Create a Measure -- Create a KPI -- Create a Hierarchy -- Create a Perspective -- Creating Roles (RBAC) -- Deploy Analysis Services to Azure -- Processing an Azure Analysis Services Model -- Azure Cosmos DB -- The Cosmos DB Architecture -- Horizontal Partitioning -- Resource Units -- Consistency -- Write Data to Azure Cosmos DB -- Index |
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| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
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| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| format | Electronic eBook |
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| id | DE-604.BV048222865 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:50:37Z |
| indexdate | 2024-07-10T09:32:27Z |
| institution | BVB |
| isbn | 9781484258231 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033603598 |
| oclc_num | 1159171322 |
| open_access_boolean | |
| physical | 1 Online-Ressource (297 pages) |
| psigel | ZDB-30-PQE |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Apress L. P. |
| record_format | marc |
| spelling | How, Matt Verfasser aut The Modern Data Warehouse in Azure Building with Speed and Agility on Microsoft's Cloud Platform Berkeley, CA Apress L. P. 2020 ©2020 1 Online-Ressource (297 pages) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: The Rise of the Modern Data Warehouse -- Getting Started -- Multi-region Support -- Resource Groups and Tagging -- Azure Security -- Tools of the Trade -- Glossary of Terms -- Naming Conventions -- Chapter 2: The SQL Engine -- The Four Vs -- Azure Synapse Analytics -- Understanding Distributions -- The First Problem -- ROUND ROBIN Distribution -- HASH Distribution -- The Distribution Column -- How to Check if You Have the Right Column -- REPLICATED Distribution -- Resource Management -- Resource Classes -- Static Resource Classes -- Dynamic Resource Classes -- Pausing and Resuming the Warehouse -- Workload Management -- PolyBase -- Azure SQL Database -- The Cloud-Based OLTP Engine -- The Benefits of Azure SQL Database -- Improved Concurrency -- Trickle-Fed Data Warehouses -- Managing Slowly Changing Dimensions -- Intelligent Query Processing and Tuning -- Automatic Tuning -- Adaptive Query Processing -- Batch Mode Memory Grant Feedback -- Adaptive Joins -- Interleaved Execution -- Hyperscale -- The Hyperscale Architecture -- Accelerated Disaster Recovery -- Azure SQL Deployment Options -- Azure SQL Database Managed Instances -- Azure SQL Database Elastic Pools -- Azure SQL Database V-Core Tiers -- Azure Synapse Analytics vs. Azure SQL Database -- The Right Type of Data -- The Size of the Data -- The Frequency of the Data -- The Availability of the Data -- The Integration of Data -- Chapter 3: The Integration Engine -- Introduction to Azure Data Factory -- The Data Factory Building Blocks -- Linked Services -- Integration Runtimes -- Self-Hosted Integration Runtime -- Azure SSIS Integration Runtime -- Triggers -- Datasets -- Pipelines and Activities -- Activity Types -- External Compute Activities -- Internal Activities Iteration and Conditional Activities -- Web Activities -- Output Constraints -- Implementing Azure Data Factory -- Security in Azure Data Factory -- Using the Managed Service Identity -- Source Control of Azure Data Factory -- Templates -- Solution Structure -- Getting Started with Azure Data Factory -- Create Linked Services -- Creating Datasets -- Creating Pipelines -- Debugging Your Pipelines -- Monitoring Your Pipelines -- Parameter-Driven Pipelines -- Getting Started with Parameters -- Using the Lookup Activity -- Getting Started with the Lookup Activity -- Additional Azure Data Factory Elements -- Additional Invocation Methods -- Mapping Data Flows -- Multiple Inputs and Outputs -- Schema Modifier -- Row Modifier -- Execute Mapping Data Flows -- Azure Data Factory Processing Patterns -- Linear Pipelines -- Parent-Child Processing -- Iterative Parent-Child Processing -- Dynamic Column Mappings -- Partitioning Datasets -- Chapter 4: The Ingestion Architecture -- Layers of Curation -- The Raw Layer -- The Clean Layer -- The Transformed Layer -- Understanding Ingestion Architecture -- Batch Ingestion -- The Risks and Opportunities of Batch Ingestion -- The ETL Window -- The ETL Anti-window -- Failure Investigation and Troubleshooting -- The Batch Ingestion Tools -- Batch Ingestion for Azure Synapse Analytics -- Create External Table As Select (CETAS) -- Event Ingestion -- The Risks and Opportunities of Event-Based Ingestion -- Implementing Event Ingestion -- Decoupled Processing -- Listening for Events -- Queuing Events -- Event Ingestion for Azure Synapse Analytics -- Event Ingestion for Azure SQL Database -- Stream Ingestion -- The Risks and Opportunities of Stream Ingestion -- Implementing Stream Ingestion -- Stream Ingestion with Azure Event Hub's and Stream Analytics Jobs -- Stream Ingestion for Azure Blob Storage Stream Ingestion for Azure SQL Database -- The Lambda Architecture -- Blending Streams and Batches -- The Serving Layer -- Assessing the Approach -- Chapter 5: The Role of the Data Lake -- The Modern Enterprise and Its Data Lake -- Azure Data Lake Technology -- Azure Data Lake Gen 1 -- Azure Blob Storage -- Azure Data Lake Gen 2 -- Planning the Enterprise Data Lake -- Storing Raw Data -- Storing Cleaned Data -- Storing Transformed Data -- Facilitating Experimentation -- Implementing the Enterprise Data Lake -- Security Configuration in Azure Data Lake -- Applying Security in Azure Data Lake Gen 2 -- Implementing a Raw Directory -- Partitioning -- Choosing a File Format -- Implementing a Clean Directory -- Cleaning Within a Database -- Cleaning Within a Data Lake -- Cleaning Within Azure Data Factory -- Implementing a Transformed Directory -- Example Polyglot Architectures -- Example One -- Example Two -- Example Three -- Example Four -- Chapter 6: The Role of the Data Contract -- What Is a Data Contract? -- Working with Data Contracts -- Designing Data Contracts -- Generating Data Contracts -- Validating Data Contacts -- Storing Data Contracts -- Modifying Data Contracts -- Integrating Data Contracts -- Fetching Metadata -- Fetching Orchestration Metadata -- Utilizing Orchestration Metadata -- Fetching Entity Metadata -- Utilizing Entity Metadata -- Code Generation -- Getting Started with Code Generation -- Harmonizing Schema Evolution -- Chapter 7: Logging, Auditing, and Resilience -- Logging the Data Movement Process -- Basic Logging Requirements -- Where to Store Your Logs -- Events to Be Logged -- Extended Logging Capabilities -- Aggregating Your Logs -- Auditing the Data Movement Process -- Basic Auditing Requirements -- Auditing Data Volumes -- Auditing Processing Times -- Storing High Watermarks Incorporating Resilience into the Data Movement Process -- Basic Resiliency -- Using Metadata for Troubleshooting -- Creating Alerts Using Azure Data Factory Alert Rules -- Creating Custom Alerts from Azure Data Factory -- Extending Resiliency -- Utilizing Data Factory Fault Tolerance -- Checking File Structure Using Data Factory -- Creating Alerts from Skipped Rows -- Monitoring the Data Movement Process -- Chapter 8: Using Scripting and Automation -- The Power of PowerShell -- Commonly Used Scripts -- Code Generation -- Invoke Data Factory Pipeline -- Recurse Data Lake Structures -- Chapter 9: Beyond the Modern Data Warehouse -- Microsoft Power BI -- Working with Power BI -- Building a Power BI Report -- Publish Report to Power BI Service -- Azure Analysis Services -- The Basics of Azure Analysis Services -- Analysis Services as a Semantic Layer -- Analysis Services Security Model -- The Vertipaq Engine -- Creating an Analysis Services Project -- Create Analysis Objects -- Create a Calculated Column -- Create a Measure -- Create a KPI -- Create a Hierarchy -- Create a Perspective -- Creating Roles (RBAC) -- Deploy Analysis Services to Azure -- Processing an Azure Analysis Services Model -- Azure Cosmos DB -- The Cosmos DB Architecture -- Horizontal Partitioning -- Resource Units -- Consistency -- Write Data to Azure Cosmos DB -- Index Data warehousing Erscheint auch als Druck-Ausgabe How, Matt The Modern Data Warehouse in Azure Berkeley, CA : Apress L. P.,c2020 9781484258224 |
| spellingShingle | How, Matt The Modern Data Warehouse in Azure Building with Speed and Agility on Microsoft's Cloud Platform Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: The Rise of the Modern Data Warehouse -- Getting Started -- Multi-region Support -- Resource Groups and Tagging -- Azure Security -- Tools of the Trade -- Glossary of Terms -- Naming Conventions -- Chapter 2: The SQL Engine -- The Four Vs -- Azure Synapse Analytics -- Understanding Distributions -- The First Problem -- ROUND ROBIN Distribution -- HASH Distribution -- The Distribution Column -- How to Check if You Have the Right Column -- REPLICATED Distribution -- Resource Management -- Resource Classes -- Static Resource Classes -- Dynamic Resource Classes -- Pausing and Resuming the Warehouse -- Workload Management -- PolyBase -- Azure SQL Database -- The Cloud-Based OLTP Engine -- The Benefits of Azure SQL Database -- Improved Concurrency -- Trickle-Fed Data Warehouses -- Managing Slowly Changing Dimensions -- Intelligent Query Processing and Tuning -- Automatic Tuning -- Adaptive Query Processing -- Batch Mode Memory Grant Feedback -- Adaptive Joins -- Interleaved Execution -- Hyperscale -- The Hyperscale Architecture -- Accelerated Disaster Recovery -- Azure SQL Deployment Options -- Azure SQL Database Managed Instances -- Azure SQL Database Elastic Pools -- Azure SQL Database V-Core Tiers -- Azure Synapse Analytics vs. Azure SQL Database -- The Right Type of Data -- The Size of the Data -- The Frequency of the Data -- The Availability of the Data -- The Integration of Data -- Chapter 3: The Integration Engine -- Introduction to Azure Data Factory -- The Data Factory Building Blocks -- Linked Services -- Integration Runtimes -- Self-Hosted Integration Runtime -- Azure SSIS Integration Runtime -- Triggers -- Datasets -- Pipelines and Activities -- Activity Types -- External Compute Activities -- Internal Activities Iteration and Conditional Activities -- Web Activities -- Output Constraints -- Implementing Azure Data Factory -- Security in Azure Data Factory -- Using the Managed Service Identity -- Source Control of Azure Data Factory -- Templates -- Solution Structure -- Getting Started with Azure Data Factory -- Create Linked Services -- Creating Datasets -- Creating Pipelines -- Debugging Your Pipelines -- Monitoring Your Pipelines -- Parameter-Driven Pipelines -- Getting Started with Parameters -- Using the Lookup Activity -- Getting Started with the Lookup Activity -- Additional Azure Data Factory Elements -- Additional Invocation Methods -- Mapping Data Flows -- Multiple Inputs and Outputs -- Schema Modifier -- Row Modifier -- Execute Mapping Data Flows -- Azure Data Factory Processing Patterns -- Linear Pipelines -- Parent-Child Processing -- Iterative Parent-Child Processing -- Dynamic Column Mappings -- Partitioning Datasets -- Chapter 4: The Ingestion Architecture -- Layers of Curation -- The Raw Layer -- The Clean Layer -- The Transformed Layer -- Understanding Ingestion Architecture -- Batch Ingestion -- The Risks and Opportunities of Batch Ingestion -- The ETL Window -- The ETL Anti-window -- Failure Investigation and Troubleshooting -- The Batch Ingestion Tools -- Batch Ingestion for Azure Synapse Analytics -- Create External Table As Select (CETAS) -- Event Ingestion -- The Risks and Opportunities of Event-Based Ingestion -- Implementing Event Ingestion -- Decoupled Processing -- Listening for Events -- Queuing Events -- Event Ingestion for Azure Synapse Analytics -- Event Ingestion for Azure SQL Database -- Stream Ingestion -- The Risks and Opportunities of Stream Ingestion -- Implementing Stream Ingestion -- Stream Ingestion with Azure Event Hub's and Stream Analytics Jobs -- Stream Ingestion for Azure Blob Storage Stream Ingestion for Azure SQL Database -- The Lambda Architecture -- Blending Streams and Batches -- The Serving Layer -- Assessing the Approach -- Chapter 5: The Role of the Data Lake -- The Modern Enterprise and Its Data Lake -- Azure Data Lake Technology -- Azure Data Lake Gen 1 -- Azure Blob Storage -- Azure Data Lake Gen 2 -- Planning the Enterprise Data Lake -- Storing Raw Data -- Storing Cleaned Data -- Storing Transformed Data -- Facilitating Experimentation -- Implementing the Enterprise Data Lake -- Security Configuration in Azure Data Lake -- Applying Security in Azure Data Lake Gen 2 -- Implementing a Raw Directory -- Partitioning -- Choosing a File Format -- Implementing a Clean Directory -- Cleaning Within a Database -- Cleaning Within a Data Lake -- Cleaning Within Azure Data Factory -- Implementing a Transformed Directory -- Example Polyglot Architectures -- Example One -- Example Two -- Example Three -- Example Four -- Chapter 6: The Role of the Data Contract -- What Is a Data Contract? -- Working with Data Contracts -- Designing Data Contracts -- Generating Data Contracts -- Validating Data Contacts -- Storing Data Contracts -- Modifying Data Contracts -- Integrating Data Contracts -- Fetching Metadata -- Fetching Orchestration Metadata -- Utilizing Orchestration Metadata -- Fetching Entity Metadata -- Utilizing Entity Metadata -- Code Generation -- Getting Started with Code Generation -- Harmonizing Schema Evolution -- Chapter 7: Logging, Auditing, and Resilience -- Logging the Data Movement Process -- Basic Logging Requirements -- Where to Store Your Logs -- Events to Be Logged -- Extended Logging Capabilities -- Aggregating Your Logs -- Auditing the Data Movement Process -- Basic Auditing Requirements -- Auditing Data Volumes -- Auditing Processing Times -- Storing High Watermarks Incorporating Resilience into the Data Movement Process -- Basic Resiliency -- Using Metadata for Troubleshooting -- Creating Alerts Using Azure Data Factory Alert Rules -- Creating Custom Alerts from Azure Data Factory -- Extending Resiliency -- Utilizing Data Factory Fault Tolerance -- Checking File Structure Using Data Factory -- Creating Alerts from Skipped Rows -- Monitoring the Data Movement Process -- Chapter 8: Using Scripting and Automation -- The Power of PowerShell -- Commonly Used Scripts -- Code Generation -- Invoke Data Factory Pipeline -- Recurse Data Lake Structures -- Chapter 9: Beyond the Modern Data Warehouse -- Microsoft Power BI -- Working with Power BI -- Building a Power BI Report -- Publish Report to Power BI Service -- Azure Analysis Services -- The Basics of Azure Analysis Services -- Analysis Services as a Semantic Layer -- Analysis Services Security Model -- The Vertipaq Engine -- Creating an Analysis Services Project -- Create Analysis Objects -- Create a Calculated Column -- Create a Measure -- Create a KPI -- Create a Hierarchy -- Create a Perspective -- Creating Roles (RBAC) -- Deploy Analysis Services to Azure -- Processing an Azure Analysis Services Model -- Azure Cosmos DB -- The Cosmos DB Architecture -- Horizontal Partitioning -- Resource Units -- Consistency -- Write Data to Azure Cosmos DB -- Index Data warehousing |
| title | The Modern Data Warehouse in Azure Building with Speed and Agility on Microsoft's Cloud Platform |
| title_auth | The Modern Data Warehouse in Azure Building with Speed and Agility on Microsoft's Cloud Platform |
| title_exact_search | The Modern Data Warehouse in Azure Building with Speed and Agility on Microsoft's Cloud Platform |
| title_exact_search_txtP | The Modern Data Warehouse in Azure Building with Speed and Agility on Microsoft's Cloud Platform |
| title_full | The Modern Data Warehouse in Azure Building with Speed and Agility on Microsoft's Cloud Platform |
| title_fullStr | The Modern Data Warehouse in Azure Building with Speed and Agility on Microsoft's Cloud Platform |
| title_full_unstemmed | The Modern Data Warehouse in Azure Building with Speed and Agility on Microsoft's Cloud Platform |
| title_short | The Modern Data Warehouse in Azure |
| title_sort | the modern data warehouse in azure building with speed and agility on microsoft s cloud platform |
| title_sub | Building with Speed and Agility on Microsoft's Cloud Platform |
| topic | Data warehousing |
| topic_facet | Data warehousing |
| work_keys_str_mv | AT howmatt themoderndatawarehouseinazurebuildingwithspeedandagilityonmicrosoftscloudplatform |