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Practical Lakehouse Architecture
Practical Lakehouse Architecture
Knygos.lt klubas Knygos.lt nariams
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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. Practical Lakehouse Architectur…

Practical Lakehouse Architecture (el. knyga) (skaityta knyga) | knygos.lt

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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.

Practical Lakehouse Architecture shows you how to:

  • Understand key lakehouse concepts and features like transaction support, time travel, and schema evolution
  • Understand the differences between traditional and lakehouse data architectures
  • Differentiate between various file formats and table formats
  • Design lakehouse architecture layers for storage, compute, metadata management, and data consumption
  • Implement data governance and data security within the platform
  • Evaluate technologies and decide on the best technology stack to implement the lakehouse for your use case
  • Make critical design decisions and address practical challenges to build a future-ready data platform
  • Start your lakehouse implementation journey and migrate data from existing systems to the lakehouse
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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.

Practical Lakehouse Architecture shows you how to:

  • Understand key lakehouse concepts and features like transaction support, time travel, and schema evolution
  • Understand the differences between traditional and lakehouse data architectures
  • Differentiate between various file formats and table formats
  • Design lakehouse architecture layers for storage, compute, metadata management, and data consumption
  • Implement data governance and data security within the platform
  • Evaluate technologies and decide on the best technology stack to implement the lakehouse for your use case
  • Make critical design decisions and address practical challenges to build a future-ready data platform
  • Start your lakehouse implementation journey and migrate data from existing systems to the lakehouse

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