東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Architecting a modern data warehouse for large enterprises = build multi-cloud modern distributed data warehouses with Azure and AWS /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Architecting a modern data warehouse for large enterprises/ by Anjani Kumar, Abhishek Mishra, Sanjeev Kumar.
其他題名:	build multi-cloud modern distributed data warehouses with Azure and AWS /
作者:	Kumar, Anjani.
其他作者:	Mishra, Abhishek.
出版者:	Berkeley, CA :Apress : : 2024.,
面頁冊數:	xv, 368 p. :ill., digital ;24 cm.
內容註:	Chapter 1: Introduction -- Chapter 2: Modern Data Warehouses -- Chapter 3: Data Lake, Lake House, and Delta Lake -- Chapter 4: Data Mesh -- Chapter 5: Data Orchestration Techniques -- Chapter 6: Data Democratization, Governance, and Security -- Chapter 7: Business Intelligence.
Contained By:	Springer Nature eBook
標題:	Big data. -
電子資源:	https://doi.org/10.1007/979-8-8688-0029-0
ISBN:	9798868800290

Architecting a modern data warehouse for large enterprises = build multi-cloud modern distributed data warehouses with Azure and AWS /
Kumar, Anjani.

Architecting a modern data warehouse for large enterprisesbuild multi-cloud modern distributed data warehouses with Azure and AWS /[electronic resource] :by Anjani Kumar, Abhishek Mishra, Sanjeev Kumar. - Berkeley, CA :Apress :2024. - xv, 368 p. :ill., digital ;24 cm.

Chapter 1: Introduction -- Chapter 2: Modern Data Warehouses -- Chapter 3: Data Lake, Lake House, and Delta Lake -- Chapter 4: Data Mesh -- Chapter 5: Data Orchestration Techniques -- Chapter 6: Data Democratization, Governance, and Security -- Chapter 7: Business Intelligence.

Design and architect new generation cloud-based data warehouses using Azure and AWS. This book provides an in-depth understanding of how to build modern cloud-native data warehouses, as well as their history and evolution. The book starts by covering foundational data warehouse concepts, and introduces modern features such as distributed processing, big data storage, data streaming, and processing data on the cloud. You will gain an understanding of the synergy, relevance, and usage data warehousing standard practices in the modern world of distributed data processing. The authors walk you through the essential concepts of Data Mesh, Data Lake, Lakehouse, and Delta Lake. And they demonstrate the services and offerings available on Azure and AWS that deal with data orchestration, data democratization, data governance, data security, and business intelligence. After completing this book, you will be ready to design and architect enterprise-grade, cloud-based modern data warehouses using industry best practices and guidelines. You will: Understand the core concepts underlying modern data warehouses Design and build cloud-native data warehouses Gain a practical approach to architecting and building data warehouses on Azure and AWS Implement modern data warehousing components such as Data Mesh, Data Lake, Delta Lake, and Lakehouse Process data through pandas and evaluate your model's performance using metrics such as F1-score, precision, and recall Apply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications.

ISBN: 9798868800290

Standard No.: 10.1007/979-8-8688-0029-0doiSubjects--Corporate Names:

3242822
Amazon Web Services (Firm)
Subjects--Topical Terms:

2045508
Big data.

LC Class. No.: QA76.9.D37

Dewey Class. No.: 658.403802855745

Architecting a modern data warehouse for large enterprises = build multi-cloud modern distributed data warehouses with Azure and AWS /
LDR:02999nmm a2200325 a 4500 001 2368316
003 DE-He213
005 20231227132645.0
006 m d
007 cr nn 008maaau
008 241127s2024 cau s 0 eng d
020 $a 9798868800290 $q (electronic bk.)
020 $a 9798868800283 $q (paper)
024 7 $a 10.1007/979-8-8688-0029-0 $2 doi
035 $a 979-8-8688-0029-0
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D37
072 7 $a UTC $2 bicssc
072 7 $a COM000000 $2 bisacsh
072 7 $a UTC $2 thema
082 0 4 $a 658.403802855745 $2 23
090 $a QA76.9.D37 $b K96 2024
100 1 $a Kumar, Anjani. $3 3449780
245 1 0 $a Architecting a modern data warehouse for large enterprises $h [electronic resource] : $b build multi-cloud modern distributed data warehouses with Azure and AWS / $c by Anjani Kumar, Abhishek Mishra, Sanjeev Kumar.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2024.
300 $a xv, 368 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1: Introduction -- Chapter 2: Modern Data Warehouses -- Chapter 3: Data Lake, Lake House, and Delta Lake -- Chapter 4: Data Mesh -- Chapter 5: Data Orchestration Techniques -- Chapter 6: Data Democratization, Governance, and Security -- Chapter 7: Business Intelligence.
520 $a Design and architect new generation cloud-based data warehouses using Azure and AWS. This book provides an in-depth understanding of how to build modern cloud-native data warehouses, as well as their history and evolution. The book starts by covering foundational data warehouse concepts, and introduces modern features such as distributed processing, big data storage, data streaming, and processing data on the cloud. You will gain an understanding of the synergy, relevance, and usage data warehousing standard practices in the modern world of distributed data processing. The authors walk you through the essential concepts of Data Mesh, Data Lake, Lakehouse, and Delta Lake. And they demonstrate the services and offerings available on Azure and AWS that deal with data orchestration, data democratization, data governance, data security, and business intelligence. After completing this book, you will be ready to design and architect enterprise-grade, cloud-based modern data warehouses using industry best practices and guidelines. You will: Understand the core concepts underlying modern data warehouses Design and build cloud-native data warehouses Gain a practical approach to architecting and building data warehouses on Azure and AWS Implement modern data warehousing components such as Data Mesh, Data Lake, Delta Lake, and Lakehouse Process data through pandas and evaluate your model's performance using metrics such as F1-score, precision, and recall Apply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications.
610 2 0 $a Amazon Web Services (Firm) $3 3242822
650 0 $a Big data. $3 2045508
650 0 $a Business enterprises $x Data processing. $3 750112
650 0 $a Data warehousing. $3 606996
650 0 $a Microsoft Azure (Computing platform) $3 3201298
650 1 4 $a Cloud Computing. $3 3231328
650 2 4 $a Microsoft. $3 3593799
650 2 4 $a Computer Communication Networks. $3 775497
700 1 $a Mishra, Abhishek. $3 3512988
700 1 $a Kumar, Sanjeev. $3 3236355
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/979-8-8688-0029-0
950 $a Professional and Applied Computing (SpringerNature-12059)