Designing Cloud Data Platforms
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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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》一書中，Danil Zburivsky和Lynda Partner揭示了一種六層方法，可以增加靈活性並降低成本。您將了解從各種來源提取數據的模式，然後學習如何利用雲端供應商提供的預建服務。總結：集中式數據倉庫，長期以來一直是存儲分析數據的事實標準，正迅速讓位於多方面的雲端數據平台。採用現代雲端數據平台的公司可以從整合的業務視圖中受益，使用所有數據並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，用於構想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。閱讀本書時，您將學習雲端數據平台設計的核心組件，以及Spark和Kafka Streams等關鍵技術的作用。您還將探索建立管理基於雲端的數據的流程，確保其安全性，並使用先進的分析和商業智能工具進行分析。購買印刷版書籍將包括Manning Publications提供的PDF、Kindle和ePub格式的免費電子書。關於技術：精心設計的管道、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，都能最大程度地提高性能。關於本書：在《設計雲端數據平台》一書中，Danil Zburivsky和Lynda Partner揭示了一種六層方法，可以增加靈活性並降低成本。您將了解從各種來源提取數據的模式，然後學習如何利用雲端供應商提供的預建服務。內容包括：結構化和非結構化數據集的最佳實踐、適用於雲端的機器學習工具、元數據和實時分析、防禦性架構、訪問和安全性。關於讀者：適合熟悉雲端計算、Hadoop或Spark基礎知識的數據專業人士。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，並在數據業務方面擁有超過20年的經驗。目錄：1.介紹數據平台、2.為什麼需要數據平台而不僅僅是數據倉庫、3.變得更大並利用Amazon、Microsoft Azure和Google等三大雲端供應商、4.將數據載入平台、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.模式管理、9.數據訪問和安全性、10.利用數據平台推動業務價值。
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在全球各地的企業中擁有超過10年的設計和支援大規模數據基礎設施的經驗。
Lynda Partner是Pythian的分析即服務副總裁，並且在數據業務方面已有超過20年的經驗。
