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|a TK5105.86
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|a Thalpati, Gaurav Ashok
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| 245 |
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|a Practical lakehouse architecture
|c by Gaurav Ashok Thalpati
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| 250 |
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|a First edition
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| 260 |
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|a Sebastopol, CA
|b O'Reilly Media, Inc.
|c 2024
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| 300 |
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|a 250 pages
|b illustrations
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| 505 |
0 |
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|a Understanding Lakehouse Architecture -- Lakehouse Architecture Characteristics -- Lakehouse Architecture Benefits -- Key Takeaways -- References -- Chapter 2. Traditional Architectures and Modern Data Platforms -- Traditional Architectures: Data Lakes and Data Warehouses -- Data Warehouse Fundamentals -- Data Lake Fundamentals -- Modern Data Platforms -- Finding Answers in the Cloud -- Standalone Approach -- Combined Approach -- Expectations of Modern Data Platforms -- Comparison: Data Warehouse, Data Lake, Lakehouse -- Capabilities and Limitations -- Implementation Activities
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|a Intro -- Copyright -- Table of Contents -- Preface -- Who Should Read This Book? -- Why I Wrote This Book -- Navigating This Book -- O'Reilly Online Learning -- Conventions Used in This Book -- How to Contact Us -- Acknowledgments -- Chapter 1. Introduction to Lakehouse Architecture -- Understanding Data Architecture -- What Is Data Architecture? -- How Does Data Architecture Help Build a Data Platform? -- Core Components of a Data Platform -- Why Do We Need a New Data Architecture? -- Lakehouse Architecture: A New Pattern -- The Lakehouse: Best of Both Worlds
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| 505 |
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|a Administration and Management -- Business Outcomes -- Lakehouse Architecture: The Default Choice for Future Data Platforms? -- Key Takeaways -- References -- Chapter 3. Storage: The Heart of the Lakehouse -- Lakehouse Storage: Key Concepts -- Row Versus Columnar Storage -- Storage-based Performance Optimization -- Lakehouse Storage Components -- Cloud Object Storage -- File Formats -- Table Formats -- Key Design Considerations -- Ecosystem Support -- Community Support -- Supported File Formats -- Supported Compute Engines -- Supported Features -- Commercial Product Support
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|a Implementing a Data Catalog: Key Design Considerations and Options -- Using Hive metastore -- Using AWS Services -- Using Azure Services -- Using GCP Services -- Using Databricks -- Key Takeaways -- References -- Chapter 5. Compute Engines for Lakehouse Architectures -- Data Computation Benefits of Lakehouse Architecture -- Independent Scaling -- Cross-region, Cross-account Access -- Unified Batch and Real-Time Processing -- Enhanced BI Performance -- Freedom to Choose Different Engine Types -- Cross-zone Analysis -- Compute Engine Options for Lakehouse Platforms -- Open Source Tools
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|a Current and Future Versions -- Performance Benchmarking -- Comparisons -- Sharing Features -- Key Takeaways -- References -- Chapter 4. Data Catalogs -- Understanding Metadata -- Technical Metadata -- Business Metadata -- How Metastores and Data Catalogs Work Together -- Features of a Data Catalog -- Search, Explore, and Discover Data -- Data Classification -- Data Governance and Security -- Data Lineage -- Unified Data Catalog -- Challenges of Siloed Metadata Management -- What Is a Unified Data Catalog? -- Benefits of a Unified Data Catalog
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| 653 |
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|a Storage area networks (Computer networks) / http://id.loc.gov/authorities/subjects/sh2001003093
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| 653 |
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|a Cloud computing / http://id.loc.gov/authorities/subjects/sh2008004883
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| 653 |
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|a Réseaux de stockage (Informatique)
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| 653 |
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|a Computer network architectures / http://id.loc.gov/authorities/subjects/sh86007468
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| 653 |
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|a Infonuagique
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| 653 |
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|a Réseaux d'ordinateurs / Architectures
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 776 |
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|z 9781098153014
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| 856 |
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|u https://learning.oreilly.com/library/view/~/9781098153007/?ar
|x Verlag
|3 Volltext
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|a 331
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|a 004.6
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| 520 |
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|a This concise yet comprehensive guide explains how to adopt a data lakehouse architecture to implement modern data platforms. It reviews the design considerations, challenges, and best practices for implementing a lakehouse and provides key insights into the ways that using a lakehouse can impact your data platform, from managing structured and unstructured data and supporting BI and AI/ML use cases to enabling more rigorous data governance and security measures
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