Cloud scale analytics with Azure data services :: build modern data warehouses on Microsoft Azure /
A practical guide to implementing a scalable and fast state-of-the-art analytical data estate Key Features Store and analyze data with enterprise-grade security and auditing Perform batch, streaming, and interactive analytics to optimize your big data solutions with ease Develop and run parallel dat...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing, Limited,
2021.
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| Online-Zugang: | Volltext |
| Zusammenfassung: | A practical guide to implementing a scalable and fast state-of-the-art analytical data estate Key Features Store and analyze data with enterprise-grade security and auditing Perform batch, streaming, and interactive analytics to optimize your big data solutions with ease Develop and run parallel data processing programs using real-world enterprise scenarios Book DescriptionAzure Data Lake, the modern data warehouse architecture, and related data services on Azure enable organizations to build their own customized analytical platform to fit any analytical requirements in terms of volume, speed, and quality. This book is your guide to learning all the features and capabilities of Azure data services for storing, processing, and analyzing data (structured, unstructured, and semi-structured) of any size. You will explore key techniques for ingesting and storing data and perform batch, streaming, and interactive analytics. The book also shows you how to overcome various challenges and complexities relating to productivity and scaling. Next, you will be able to develop and run massive data workloads to perform different actions. Using a cloud-based big data-modern data warehouse-analytics setup, you will also be able to build secure, scalable data estates for enterprises. Finally, you will not only learn how to develop a data warehouse but also understand how to create enterprise-grade security and auditing big data programs. By the end of this Azure book, you will have learned how to develop a powerful and efficient analytical platform to meet enterprise needs. What you will learn Implement data governance with Azure services Use integrated monitoring in the Azure Portal and integrate Azure Data Lake Storage into the Azure Monitor Explore the serverless feature for ad-hoc data discovery, logical data warehousing, and data wrangling Implement networking with Synapse Analytics and Spark pools Create and run Spark jobs with Databricks clusters Implement streaming using Azure Functions, a serverless runtime environment on Azure Explore the predefined ML services in Azure and use them in your app Who this book is for This book is for data architects, ETL developers, or anyone who wants to get well-versed with Azure data services to implement an analytical data estate for their enterprise. The book will also appeal to data scientists and data analysts who want to explore all the capabilities of Azure data services, which can be used to store, process, and analyze any kind of data. A beginner-level understanding of data analysis and streaming will be required. |
| Beschreibung: | 1 online resource |
| ISBN: | 9781800562141 1800562144 |
Internformat
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| 505 | 0 | |a Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Section 1: Data Warehousing and Considerations Regarding Cloud Computing -- Chapter 1: Balancing the Benefits of Data Lakes Over Data Warehouses -- Distinguishing between Data Warehouses and Data Lakes -- Understanding Data Warehouse patterns -- Investigating ETL/ELT -- Understanding Data Warehouse layers -- Implementing reporting and dashboarding -- Loading bigger amounts of data -- Starting with Data Lakes -- Understanding the Data Lake ecosystem -- Comparing Data Lake zones | |
| 505 | 8 | |a Discovering caveats -- Understanding the opportunities of modern cloud computing -- Understanding Infrastructure-as-a-Service -- Understanding Platform-as-a-Service -- Understanding Software-as-a-Service -- Examining the possibilities of virtual machines -- Understanding Serverless Functions -- Looking at the importance of containers -- Exploring the advantages of scalable environments -- Implementing elastic storage and compute -- Exploring the benefits of AI and ML -- Understanding ML challenges -- Sorting ML into the Modern Data Warehouse -- Understanding responsible ML/AI | |
| 505 | 8 | |a Answering the question -- Summary -- Chapter 2: Connecting Requirements and Technology -- Formulating your requirements -- Asking in the right direction -- Understanding basic architecture patterns -- Examining the scalable storage component -- Looking at data integration -- Sorting in compute -- Adding a presentation layer -- Planning for dashboard/reporting -- Adding APIs/API management -- Relying on SSO/MFA/networking -- Not forgetting DevOps and CI/CD -- Finding the right Azure tool for the right purpose -- Understanding Industry Data Models -- Thinking about different sizes | |
| 505 | 8 | |a Planning for S size -- Planning for M size -- Planning for L size -- Understanding the supporting services -- Requiring data governance -- Establishing security -- Establishing DevOps and CI/CD -- Summary -- Questions -- Section 2: The Storage Layer -- Chapter 3: Understanding the Data Lake Storage Layer -- Technical requirements -- Setting up your Cloud Big Data Storage -- Provisioning a standard storage account instead -- Creating an Azure Data Lake Gen2 storage account -- Organizing your data lake -- Talking about zones in your data lake -- Creating structures in your data lake | |
| 505 | 8 | |a Planning the leaf level -- Understanding data life cycles -- Investigating storage tiers -- Planning for criticality -- Setting up confidentiality -- Using filetypes -- Implementing a data model in your Data Lake -- Understanding interconnectivity between your data lake and the presentation layer -- Examining key implementation and usage -- Monitoring your storage account -- Creating alerts for Azure storage accounts -- Talking about backups -- Configuring delete locks for the storage service -- Backing up your data -- Implementing access control in your Data Lake -- Understanding RBAC | |
| 520 | |a A practical guide to implementing a scalable and fast state-of-the-art analytical data estate Key Features Store and analyze data with enterprise-grade security and auditing Perform batch, streaming, and interactive analytics to optimize your big data solutions with ease Develop and run parallel data processing programs using real-world enterprise scenarios Book DescriptionAzure Data Lake, the modern data warehouse architecture, and related data services on Azure enable organizations to build their own customized analytical platform to fit any analytical requirements in terms of volume, speed, and quality. This book is your guide to learning all the features and capabilities of Azure data services for storing, processing, and analyzing data (structured, unstructured, and semi-structured) of any size. You will explore key techniques for ingesting and storing data and perform batch, streaming, and interactive analytics. The book also shows you how to overcome various challenges and complexities relating to productivity and scaling. Next, you will be able to develop and run massive data workloads to perform different actions. Using a cloud-based big data-modern data warehouse-analytics setup, you will also be able to build secure, scalable data estates for enterprises. Finally, you will not only learn how to develop a data warehouse but also understand how to create enterprise-grade security and auditing big data programs. By the end of this Azure book, you will have learned how to develop a powerful and efficient analytical platform to meet enterprise needs. What you will learn Implement data governance with Azure services Use integrated monitoring in the Azure Portal and integrate Azure Data Lake Storage into the Azure Monitor Explore the serverless feature for ad-hoc data discovery, logical data warehousing, and data wrangling Implement networking with Synapse Analytics and Spark pools Create and run Spark jobs with Databricks clusters Implement streaming using Azure Functions, a serverless runtime environment on Azure Explore the predefined ML services in Azure and use them in your app Who this book is for This book is for data architects, ETL developers, or anyone who wants to get well-versed with Azure data services to implement an analytical data estate for their enterprise. The book will also appeal to data scientists and data analysts who want to explore all the capabilities of Azure data services, which can be used to store, process, and analyze any kind of data. A beginner-level understanding of data analysis and streaming will be required. | ||
| 650 | 0 | |a Data warehousing. |0 http://id.loc.gov/authorities/subjects/sh97003695 | |
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| contents | Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Section 1: Data Warehousing and Considerations Regarding Cloud Computing -- Chapter 1: Balancing the Benefits of Data Lakes Over Data Warehouses -- Distinguishing between Data Warehouses and Data Lakes -- Understanding Data Warehouse patterns -- Investigating ETL/ELT -- Understanding Data Warehouse layers -- Implementing reporting and dashboarding -- Loading bigger amounts of data -- Starting with Data Lakes -- Understanding the Data Lake ecosystem -- Comparing Data Lake zones Discovering caveats -- Understanding the opportunities of modern cloud computing -- Understanding Infrastructure-as-a-Service -- Understanding Platform-as-a-Service -- Understanding Software-as-a-Service -- Examining the possibilities of virtual machines -- Understanding Serverless Functions -- Looking at the importance of containers -- Exploring the advantages of scalable environments -- Implementing elastic storage and compute -- Exploring the benefits of AI and ML -- Understanding ML challenges -- Sorting ML into the Modern Data Warehouse -- Understanding responsible ML/AI Answering the question -- Summary -- Chapter 2: Connecting Requirements and Technology -- Formulating your requirements -- Asking in the right direction -- Understanding basic architecture patterns -- Examining the scalable storage component -- Looking at data integration -- Sorting in compute -- Adding a presentation layer -- Planning for dashboard/reporting -- Adding APIs/API management -- Relying on SSO/MFA/networking -- Not forgetting DevOps and CI/CD -- Finding the right Azure tool for the right purpose -- Understanding Industry Data Models -- Thinking about different sizes Planning for S size -- Planning for M size -- Planning for L size -- Understanding the supporting services -- Requiring data governance -- Establishing security -- Establishing DevOps and CI/CD -- Summary -- Questions -- Section 2: The Storage Layer -- Chapter 3: Understanding the Data Lake Storage Layer -- Technical requirements -- Setting up your Cloud Big Data Storage -- Provisioning a standard storage account instead -- Creating an Azure Data Lake Gen2 storage account -- Organizing your data lake -- Talking about zones in your data lake -- Creating structures in your data lake Planning the leaf level -- Understanding data life cycles -- Investigating storage tiers -- Planning for criticality -- Setting up confidentiality -- Using filetypes -- Implementing a data model in your Data Lake -- Understanding interconnectivity between your data lake and the presentation layer -- Examining key implementation and usage -- Monitoring your storage account -- Creating alerts for Azure storage accounts -- Talking about backups -- Configuring delete locks for the storage service -- Backing up your data -- Implementing access control in your Data Lake -- Understanding RBAC |
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| discipline | Informatik |
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| spelling | Borosch, Patrik, author. Cloud scale analytics with Azure data services : build modern data warehouses on Microsoft Azure / Patrik Borosch. Birmingham : Packt Publishing, Limited, 2021. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Section 1: Data Warehousing and Considerations Regarding Cloud Computing -- Chapter 1: Balancing the Benefits of Data Lakes Over Data Warehouses -- Distinguishing between Data Warehouses and Data Lakes -- Understanding Data Warehouse patterns -- Investigating ETL/ELT -- Understanding Data Warehouse layers -- Implementing reporting and dashboarding -- Loading bigger amounts of data -- Starting with Data Lakes -- Understanding the Data Lake ecosystem -- Comparing Data Lake zones Discovering caveats -- Understanding the opportunities of modern cloud computing -- Understanding Infrastructure-as-a-Service -- Understanding Platform-as-a-Service -- Understanding Software-as-a-Service -- Examining the possibilities of virtual machines -- Understanding Serverless Functions -- Looking at the importance of containers -- Exploring the advantages of scalable environments -- Implementing elastic storage and compute -- Exploring the benefits of AI and ML -- Understanding ML challenges -- Sorting ML into the Modern Data Warehouse -- Understanding responsible ML/AI Answering the question -- Summary -- Chapter 2: Connecting Requirements and Technology -- Formulating your requirements -- Asking in the right direction -- Understanding basic architecture patterns -- Examining the scalable storage component -- Looking at data integration -- Sorting in compute -- Adding a presentation layer -- Planning for dashboard/reporting -- Adding APIs/API management -- Relying on SSO/MFA/networking -- Not forgetting DevOps and CI/CD -- Finding the right Azure tool for the right purpose -- Understanding Industry Data Models -- Thinking about different sizes Planning for S size -- Planning for M size -- Planning for L size -- Understanding the supporting services -- Requiring data governance -- Establishing security -- Establishing DevOps and CI/CD -- Summary -- Questions -- Section 2: The Storage Layer -- Chapter 3: Understanding the Data Lake Storage Layer -- Technical requirements -- Setting up your Cloud Big Data Storage -- Provisioning a standard storage account instead -- Creating an Azure Data Lake Gen2 storage account -- Organizing your data lake -- Talking about zones in your data lake -- Creating structures in your data lake Planning the leaf level -- Understanding data life cycles -- Investigating storage tiers -- Planning for criticality -- Setting up confidentiality -- Using filetypes -- Implementing a data model in your Data Lake -- Understanding interconnectivity between your data lake and the presentation layer -- Examining key implementation and usage -- Monitoring your storage account -- Creating alerts for Azure storage accounts -- Talking about backups -- Configuring delete locks for the storage service -- Backing up your data -- Implementing access control in your Data Lake -- Understanding RBAC A practical guide to implementing a scalable and fast state-of-the-art analytical data estate Key Features Store and analyze data with enterprise-grade security and auditing Perform batch, streaming, and interactive analytics to optimize your big data solutions with ease Develop and run parallel data processing programs using real-world enterprise scenarios Book DescriptionAzure Data Lake, the modern data warehouse architecture, and related data services on Azure enable organizations to build their own customized analytical platform to fit any analytical requirements in terms of volume, speed, and quality. This book is your guide to learning all the features and capabilities of Azure data services for storing, processing, and analyzing data (structured, unstructured, and semi-structured) of any size. You will explore key techniques for ingesting and storing data and perform batch, streaming, and interactive analytics. The book also shows you how to overcome various challenges and complexities relating to productivity and scaling. Next, you will be able to develop and run massive data workloads to perform different actions. Using a cloud-based big data-modern data warehouse-analytics setup, you will also be able to build secure, scalable data estates for enterprises. Finally, you will not only learn how to develop a data warehouse but also understand how to create enterprise-grade security and auditing big data programs. By the end of this Azure book, you will have learned how to develop a powerful and efficient analytical platform to meet enterprise needs. What you will learn Implement data governance with Azure services Use integrated monitoring in the Azure Portal and integrate Azure Data Lake Storage into the Azure Monitor Explore the serverless feature for ad-hoc data discovery, logical data warehousing, and data wrangling Implement networking with Synapse Analytics and Spark pools Create and run Spark jobs with Databricks clusters Implement streaming using Azure Functions, a serverless runtime environment on Azure Explore the predefined ML services in Azure and use them in your app Who this book is for This book is for data architects, ETL developers, or anyone who wants to get well-versed with Azure data services to implement an analytical data estate for their enterprise. The book will also appeal to data scientists and data analysts who want to explore all the capabilities of Azure data services, which can be used to store, process, and analyze any kind of data. A beginner-level understanding of data analysis and streaming will be required. Data warehousing. http://id.loc.gov/authorities/subjects/sh97003695 Microsoft Azure (Computing platform) http://id.loc.gov/authorities/subjects/sh2016001752 Entrepôts de données (Informatique) Microsoft Azure (Plateforme informatique) Data warehousing fast Microsoft Azure (Computing platform) fast has work: Cloud Scale Analytics with Azure Data Services (Text) https://id.oclc.org/worldcat/entity/E39PCYKVkXkVHVMdVkkH93VdpK https://id.oclc.org/worldcat/ontology/hasWork Print version: BOROSCH, PATRIK. CLOUD SCALE ANALYTICS WITH AZURE DATA SERVICES. [Place of publication not identified] : PACKT PUBLISHING LIMITED, 2021 1800562934 (OCoLC)1252413479 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2959064 Volltext |
| spellingShingle | Borosch, Patrik Cloud scale analytics with Azure data services : build modern data warehouses on Microsoft Azure / Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Section 1: Data Warehousing and Considerations Regarding Cloud Computing -- Chapter 1: Balancing the Benefits of Data Lakes Over Data Warehouses -- Distinguishing between Data Warehouses and Data Lakes -- Understanding Data Warehouse patterns -- Investigating ETL/ELT -- Understanding Data Warehouse layers -- Implementing reporting and dashboarding -- Loading bigger amounts of data -- Starting with Data Lakes -- Understanding the Data Lake ecosystem -- Comparing Data Lake zones Discovering caveats -- Understanding the opportunities of modern cloud computing -- Understanding Infrastructure-as-a-Service -- Understanding Platform-as-a-Service -- Understanding Software-as-a-Service -- Examining the possibilities of virtual machines -- Understanding Serverless Functions -- Looking at the importance of containers -- Exploring the advantages of scalable environments -- Implementing elastic storage and compute -- Exploring the benefits of AI and ML -- Understanding ML challenges -- Sorting ML into the Modern Data Warehouse -- Understanding responsible ML/AI Answering the question -- Summary -- Chapter 2: Connecting Requirements and Technology -- Formulating your requirements -- Asking in the right direction -- Understanding basic architecture patterns -- Examining the scalable storage component -- Looking at data integration -- Sorting in compute -- Adding a presentation layer -- Planning for dashboard/reporting -- Adding APIs/API management -- Relying on SSO/MFA/networking -- Not forgetting DevOps and CI/CD -- Finding the right Azure tool for the right purpose -- Understanding Industry Data Models -- Thinking about different sizes Planning for S size -- Planning for M size -- Planning for L size -- Understanding the supporting services -- Requiring data governance -- Establishing security -- Establishing DevOps and CI/CD -- Summary -- Questions -- Section 2: The Storage Layer -- Chapter 3: Understanding the Data Lake Storage Layer -- Technical requirements -- Setting up your Cloud Big Data Storage -- Provisioning a standard storage account instead -- Creating an Azure Data Lake Gen2 storage account -- Organizing your data lake -- Talking about zones in your data lake -- Creating structures in your data lake Planning the leaf level -- Understanding data life cycles -- Investigating storage tiers -- Planning for criticality -- Setting up confidentiality -- Using filetypes -- Implementing a data model in your Data Lake -- Understanding interconnectivity between your data lake and the presentation layer -- Examining key implementation and usage -- Monitoring your storage account -- Creating alerts for Azure storage accounts -- Talking about backups -- Configuring delete locks for the storage service -- Backing up your data -- Implementing access control in your Data Lake -- Understanding RBAC Data warehousing. http://id.loc.gov/authorities/subjects/sh97003695 Microsoft Azure (Computing platform) http://id.loc.gov/authorities/subjects/sh2016001752 Entrepôts de données (Informatique) Microsoft Azure (Plateforme informatique) Data warehousing fast Microsoft Azure (Computing platform) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh97003695 http://id.loc.gov/authorities/subjects/sh2016001752 |
| title | Cloud scale analytics with Azure data services : build modern data warehouses on Microsoft Azure / |
| title_auth | Cloud scale analytics with Azure data services : build modern data warehouses on Microsoft Azure / |
| title_exact_search | Cloud scale analytics with Azure data services : build modern data warehouses on Microsoft Azure / |
| title_full | Cloud scale analytics with Azure data services : build modern data warehouses on Microsoft Azure / Patrik Borosch. |
| title_fullStr | Cloud scale analytics with Azure data services : build modern data warehouses on Microsoft Azure / Patrik Borosch. |
| title_full_unstemmed | Cloud scale analytics with Azure data services : build modern data warehouses on Microsoft Azure / Patrik Borosch. |
| title_short | Cloud scale analytics with Azure data services : |
| title_sort | cloud scale analytics with azure data services build modern data warehouses on microsoft azure |
| title_sub | build modern data warehouses on Microsoft Azure / |
| topic | Data warehousing. http://id.loc.gov/authorities/subjects/sh97003695 Microsoft Azure (Computing platform) http://id.loc.gov/authorities/subjects/sh2016001752 Entrepôts de données (Informatique) Microsoft Azure (Plateforme informatique) Data warehousing fast Microsoft Azure (Computing platform) fast |
| topic_facet | Data warehousing. Microsoft Azure (Computing platform) Entrepôts de données (Informatique) Microsoft Azure (Plateforme informatique) Data warehousing |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2959064 |
| work_keys_str_mv | AT boroschpatrik cloudscaleanalyticswithazuredataservicesbuildmoderndatawarehousesonmicrosoftazure |