Designing Cloud Data Platforms
暫譯: 設計雲端數據平台
Zburivsky, Danil, Partner, Lynda
- 出版商: Manning
- 出版日期: 2021-06-11
- 售價: $2,100
- 貴賓價: 9.5 折 $1,995
- 語言: 英文
- 頁數: 336
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1617296449
- ISBN-13: 9781617296444
-
相關分類:
大數據 Big-data、資料庫、雲端運算
-
相關翻譯:
雲數據平台：設計、實現與管理 (簡中版)
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相關主題
商品描述
In Designing Cloud Data Platforms, Danil Zburivsky and Lynda Partner reveal a six-layer approach that increases flexibility and reduces costs. Discover patterns for ingesting data from a variety of sources, then learn to harness pre-built services provided by cloud vendors. Summary
Centralized data warehouses, the long-time defacto standard for housing data for analytics, are rapidly giving way to multi-faceted cloud data platforms. Companies that embrace modern cloud data platforms benefit from an integrated view of their business using all of their data and can take advantage of advanced analytic practices to drive predictions and as yet unimagined data services. Designing Cloud Data Platforms is a hands-on guide to envisioning and designing a modern scalable data platform that takes full advantage of the flexibility of the cloud. As you read, you'll learn the core components of a cloud data platform design, along with the role of key technologies like Spark and Kafka Streams. You'll also explore setting up processes to manage cloud-based data, keep it secure, and using advanced analytic and BI tools to analyze it. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology
Well-designed pipelines, storage systems, and APIs eliminate the complicated scaling and maintenance required with on-prem data centers. Once you learn the patterns for designing cloud data platforms, you'll maximize performance no matter which cloud vendor you use. About the book
In Designing Cloud Data Platforms, Danil Zburivsky and Lynda Partner reveal a six-layer approach that increases flexibility and reduces costs. Discover patterns for ingesting data from a variety of sources, then learn to harness pre-built services provided by cloud vendors. What's inside
Best practices for structured and unstructured data sets
Cloud-ready machine learning tools
Metadata and real-time analytics
Defensive architecture, access, and security About the reader
For data professionals familiar with the basics of cloud computing, and Hadoop or Spark. About the author
Danil Zburivsky has over 10 years of experience designing and supporting large-scale data infrastructure for enterprises across the globe. Lynda Partner is the VP of Analytics-as-a-Service at Pythian, and has been on the business side of data for over 20 years. Table of Contents
1 Introducing the data platform
2 Why a data platform and not just a data warehouse
3 Getting bigger and leveraging the Big 3: Amazon, Microsoft Azure, and Google
4 Getting data into the platform
5 Organizing and processing data
6 Real-time data processing and analytics
7 Metadata layer architecture
8 Schema management
9 Data access and security
10 Fueling business value with data platforms
Centralized data warehouses, the long-time defacto standard for housing data for analytics, are rapidly giving way to multi-faceted cloud data platforms. Companies that embrace modern cloud data platforms benefit from an integrated view of their business using all of their data and can take advantage of advanced analytic practices to drive predictions and as yet unimagined data services. Designing Cloud Data Platforms is a hands-on guide to envisioning and designing a modern scalable data platform that takes full advantage of the flexibility of the cloud. As you read, you'll learn the core components of a cloud data platform design, along with the role of key technologies like Spark and Kafka Streams. You'll also explore setting up processes to manage cloud-based data, keep it secure, and using advanced analytic and BI tools to analyze it. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology
Well-designed pipelines, storage systems, and APIs eliminate the complicated scaling and maintenance required with on-prem data centers. Once you learn the patterns for designing cloud data platforms, you'll maximize performance no matter which cloud vendor you use. About the book
In Designing Cloud Data Platforms, Danil Zburivsky and Lynda Partner reveal a six-layer approach that increases flexibility and reduces costs. Discover patterns for ingesting data from a variety of sources, then learn to harness pre-built services provided by cloud vendors. What's inside
Best practices for structured and unstructured data sets
Cloud-ready machine learning tools
Metadata and real-time analytics
Defensive architecture, access, and security About the reader
For data professionals familiar with the basics of cloud computing, and Hadoop or Spark. About the author
Danil Zburivsky has over 10 years of experience designing and supporting large-scale data infrastructure for enterprises across the globe. Lynda Partner is the VP of Analytics-as-a-Service at Pythian, and has been on the business side of data for over 20 years. Table of Contents
1 Introducing the data platform
2 Why a data platform and not just a data warehouse
3 Getting bigger and leveraging the Big 3: Amazon, Microsoft Azure, and Google
4 Getting data into the platform
5 Organizing and processing data
6 Real-time data processing and analytics
7 Metadata layer architecture
8 Schema management
9 Data access and security
10 Fueling business value with data platforms
商品描述(中文翻譯)
在《設計雲端數據平台》中，Danil Zburivsky 和 Lynda Partner 揭示了一種六層方法，能夠提高靈活性並降低成本。探索從各種來源獲取數據的模式，然後學習如何利用雲端供應商提供的預建服務。
摘要
集中的數據倉庫，長期以來一直是分析數據的事實標準，正迅速讓位於多面向的雲端數據平台。擁抱現代雲端數據平台的公司能夠利用所有數據獲得業務的整合視圖，並能利用先進的分析實踐來推動預測和尚未想像的數據服務。《設計雲端數據平台》是一本實用指南，幫助您構思和設計一個現代可擴展的數據平台，充分利用雲端的靈活性。在閱讀過程中，您將學習雲端數據平台設計的核心組件，以及 Spark 和 Kafka Streams 等關鍵技術的角色。您還將探索設置流程以管理雲端數據，保持其安全，並使用先進的分析和商業智慧工具進行分析。
購買印刷版書籍可獲得 Manning Publications 提供的免費 PDF、Kindle 和 ePub 格式電子書。
關於技術
設計良好的管道、存儲系統和 API 消除了本地數據中心所需的複雜擴展和維護。一旦您學會了設計雲端數據平台的模式，無論使用哪個雲端供應商，您都能最大化性能。
關於本書
在《設計雲端數據平台》中，Danil Zburivsky 和 Lynda Partner 揭示了一種六層方法，能夠提高靈活性並降低成本。探索從各種來源獲取數據的模式，然後學習如何利用雲端供應商提供的預建服務。
內容概覽
結構化和非結構化數據集的最佳實踐
雲端就緒的機器學習工具
元數據和實時分析
防禦性架構、訪問和安全
讀者對象
適合熟悉雲計算基礎知識以及 Hadoop 或 Spark 的數據專業人士。
關於作者
Danil Zburivsky 擁有超過 10 年的經驗，為全球企業設計和支持大規模數據基礎設施。Lynda Partner 是 Pythian 的分析即服務副總裁，並在數據商業領域工作超過 20 年。
目錄
1 介紹數據平台
2 為什麼選擇數據平台而不僅僅是數據倉庫
3 擴大規模並利用三大雲端供應商：Amazon、Microsoft Azure 和 Google
4 將數據導入平台
5 組織和處理數據
6 實時數據處理和分析
7 元數據層架構
8 架構管理
9 數據訪問和安全
10 利用數據平台推動商業價值
作者簡介
Danil Zburivsky has over 10 years experience designing and supporting large-scale data infrastructure for enterprises across the globe. Lynda Partner is the VP of Analytics-as-a-Service at Pythian, and has been on the business side of data for over 20 years.
作者簡介(中文翻譯)
Danil Zburivsky 擁有超過 10 年的經驗，為全球企業設計和支持大規模數據基礎設施。Lynda Partner 是 Pythian 的分析即服務副總裁，並在數據業務方面擁有超過 20 年的經驗。
