Azure Storage, Streaming, and Batch Analytics: A guide for data engineers

Nuckolls, Richard

  • 出版商: Manning
  • 出版日期: 2020-11-10
  • 定價: $1,750
  • 售價: 9.0$1,575
  • 語言: 英文
  • 頁數: 375
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1617296309
  • ISBN-13: 9781617296307
  • 相關分類: Microsoft Azure
  • 立即出貨 (庫存 < 4)



The Microsoft Azure cloud is an ideal platform for data-intensive applications. Designed for productivity, Azure provides pre-built services that make collection, storage, and analysis much easier to implement and manage.

Azure Data Engineering teaches you how to design a reliable, performant, and cost-effective data infrastructure in Azure by progressively building a complete working analytics system.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.


微軟 Azure 雲端平台是適合資料密集型應用程式的理想平台。Azure 提供了預先建置的服務,使得資料的收集、儲存和分析更容易實施和管理。

《Azure 資料工程》教導您如何在 Azure 中設計可靠、高效且具有成本效益的資料基礎架構,並逐步建立一個完整的工作分析系統。

購買印刷書籍將包含一本免費的電子書,可提供 PDF、Kindle 和 ePub 格式,由 Manning Publications 提供。


Richard Nuckolls is a senior developer building a big data analytics and reporting system in Azure. During his nearly 20 years of experience, he's done server and database administration, desktop and web development, and more recently has led teams in building a production content management system in Azure.


Richard Nuckolls 是一位資深開發人員,在 Azure 上建立大數據分析和報告系統。在他近 20 年的經驗中,他從事過伺服器和資料庫管理、桌面和網頁開發,最近更帶領團隊在 Azure 上建立了一個生產內容管理系統。


1. What is data engineering
2 Building an analytics system in Azure
3 Azure Storage Blob service
4 Azure Data Lake storage
5 Message handling with Event Hubs
6 Real-time queries with Azure Stream Analytics
7 Batch queries with Azure Data Lake Analytics
8 U-SQL for complex analytics
9 Integrating with Azure Data Lake Analytics
10 Service integration with Azure Data Factory
11 Managed SQL with Azure SQL Database
12 Integrating Data Factory with SQL Database
13 Where to go next

A Appendix A. Set up of Azure resources through Powershell


1. 什麼是資料工程
2. 在 Azure 中建立分析系統
3. Azure 儲存 Blob 服務
4. Azure Data Lake 儲存
5. 使用 Event Hubs 處理訊息
6. 使用 Azure Stream Analytics 進行即時查詢
7. 使用 Azure Data Lake Analytics 進行批次查詢
8. 使用 U-SQL 進行複雜分析
9. 與 Azure Data Lake Analytics 整合
10. 使用 Azure Data Factory 進行服務整合
11. 使用 Azure SQL Database 進行管理 SQL
12. 將 Data Factory 與 SQL Database 整合
13. 接下來該去哪裡

附錄 A. 透過 Powershell 設定 Azure 資源