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Practical Lakehouse Architecture Designing and Implementing Modern Data Platforms at Scale 1st Edition by Gaurav Ashok Thalpati ISBN 1098153014 9781098153014

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Practical Lakehouse Architecture Designing and Implementing Modern Data Platforms at Scale Gaurav Ashok Thalpati Digital Instant Download

Author(s): Gaurav Ashok Thalpati
ISBN(s): 9781098153014, 1098153014
Edition: converted
File Details: PDF, 6.22 MB
Year: 2024
Language: english
SKU: EB-58622920 Category: Tag:

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ISBN 10: 1098153014 
ISBN 13: 9781098153014
Author: Gaurav Ashok Thalpati

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

Practical Lakehouse Architecture Designing and Implementing Modern Data Platforms at Scale 1st Table of contents:

1. Introduction to Lakehouse Architecture

  • Understanding Data Architecture

  • What Is Data Architecture?

  • Core Components of a Data Platform

  • Why Do We Need a New Data Architecture?

  • Lakehouse Architecture: A New Pattern

  • Lakehouse Architecture Characteristics and Benefits

  • Key Takeaways

  • References

2. Traditional Architectures and Modern Data Platforms

  • Data Warehouses vs. Data Lakes

  • Modern Data Platforms in the Cloud

  • Architectural Approaches

  • Comparing Data Warehouse, Data Lake, and Lakehouse

  • Capabilities, Limitations, and Business Outcomes

  • Key Takeaways

  • References

3. Storage: The Heart of the Lakehouse

  • Key Concepts: Row vs. Columnar Storage

  • Storage Components and Optimization

  • Cloud Object Storage, File and Table Formats

  • Design Considerations and Performance

  • Product and Community Support

  • Key Takeaways

  • References

4. Data Catalogs

  • Metadata: Technical and Business

  • Metastores vs. Data Catalogs

  • Catalog Features: Search, Governance, Lineage

  • Unified Data Catalog Benefits

  • Implementation Options (Hive, AWS, Azure, GCP, Databricks)

  • Key Takeaways

  • References

5. Compute Engines for Lakehouse Architectures

  • Benefits of Lakehouse Compute

  • Engine Types: Open Source, Cloud, Third-Party

  • Design Considerations and Ecosystem Support

  • Workload-Based Preferences

  • Key Takeaways

  • References

6. Data (and AI) Governance and Security in Lakehouse Architecture

  • Governance, Security, and Compliance

  • Unified Framework for Data & ML Models

  • Processes: Metadata, Ownership, Lineage, Access

  • Handling Sensitive Data and Auditing

  • Key Takeaways

  • References

7. The Big Picture: Designing and Implementing a Lakehouse Platform

  • Pre-Design Activities and Requirement Gathering

  • Architecture Principles: Scalability, Security, Cost

  • Implementation Best Practices

  • Blueprint: Ingestion, Storage, Processing, Delivery

  • Step-by-Step Design Guide and Questionnaire

  • Key Takeaways

  • References

8. Lakehouse in the Real World

  • Real Project Phases: Estimation, Design, Testing

  • Migration, Quality, and Reverse Engineering

  • Maintenance: Auditing, Disaster Recovery

  • Project Deliverables and Reference Architectures

  • Key Takeaways

  • References

9. Lakehouse of the Future

  • Future Trends: Data Mesh, HTAP, Zero ETL

  • Universal Formats and Interoperability

  • Emerging Table/File Formats

  • Managed Platforms (e.g., Microsoft Fabric)

  • Role of AI in Lakehouse Platforms

  • Key Takeaways

  • Book Conclusion

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Tags: Gaurav Ashok Thalpati, Practical Lakehouse, Data Platforms