Mastering Azure Analytics : Architecting in the Cloud with Azure Data Lake, HDInsight, and Spark (Paperback)
Zoiner Tejada
- 出版商: O'Reilly
- 出版日期: 2017-05-23
- 定價: $1,650
- 售價: 9.5 折 $1,568
- 貴賓價: 9.0 折 $1,485
- 語言: 英文
- 頁數: 412
- 裝訂: Paperback
- ISBN: 1491956658
- ISBN-13: 9781491956656
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相關分類:
Microsoft Azure、Spark
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相關翻譯:
精通 Azure Analytics|在雲端上使用 Azure Data Lake、HDInsight 與 Spark (Mastering Azure Analytics : Architecting in the Cloud with Azure Data Lake, HDInsight, and Spark) (繁中版)
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商品描述
Microsoft Azure has over 20 platform-as-a-service (PaaS) offerings that can act in support of a big data analytics solution. So which one is right for your project? This practical book helps you understand the breadth of Azure services by organizing them into a reference framework you can use when crafting your own big data analytics solution.
You’ll not only be able to determine which service best fits the job, but also learn how to implement a complete solution that scales, provides human fault tolerance, and supports future needs.
- Understand the fundamental patterns of the data lake and lambda architecture
- Recognize the canonical steps in the analytics data pipeline and learn how to use Azure Data Factory to orchestrate them
- Implement data lakes and lambda architectures, using Azure Data Lake Store, Data Lake Analytics, HDInsight (including Spark), Stream Analytics, SQL Data Warehouse, and Event Hubs
- Understand where Azure Machine Learning fits into your analytics pipeline
- Gain experience using these services on real-world data that has real-world problems, with scenarios ranging from aviation to Internet of Things (IoT)
商品描述(中文翻譯)
Microsoft Azure擁有超過20種平台即服務(PaaS)提供,可以支援大數據分析解決方案。那麼,哪一種適合您的專案呢?這本實用的書籍將幫助您通過將這些服務組織成一個參考框架,以便在打造自己的大數據分析解決方案時使用。
您不僅能夠確定哪種服務最適合您的工作,還能學習如何實施一個完整的解決方案,該方案具有可擴展性、提供人為容錯能力並支持未來需求。
本書內容包括:
- 理解數據湖和Lambda架構的基本模式
- 認識分析數據管道中的基本步驟,並學習如何使用Azure Data Factory來協調它們
- 使用Azure Data Lake Store、Data Lake Analytics、HDInsight(包括Spark)、Stream Analytics、SQL Data Warehouse和Event Hubs來實施數據湖和Lambda架構
- 理解Azure Machine Learning在分析管道中的位置
- 通過使用真實世界的數據和場景(從航空到物聯網)來獲得使用這些服務的實踐經驗