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Financial Data Warehousing & Lakehouse Architecture: Designing Scalable SQL-First Systems for Trading, FP&A, Risk, and Real-Time Analytics - Couverture souple
Synopsis
Reactive Publishing
Financial organizations do not suffer from a lack of data. They suffer from fragmented systems, inconsistent definitions, and architectures that fail under scale.
Financial Data Warehousing & Lakehouse Architecture is a practical guide to designing SQL-first financial data platforms that remain coherent, auditable, and performant as complexity increases. It bridges the gap between classical data warehousing theory and modern lakehouse implementations, translating both into financial systems that support decision-making, risk management, and real-time analytics.
This book is written for professionals responsible for building and maintaining the data backbone of financial organizations. It focuses on architectural decisions that determine whether financial data becomes a strategic asset or a long-term liability.
What this book coversCore principles of financial data warehousing
Star, snowflake, and hybrid schemas for financial use cases
Designing fact and dimension tables for markets, accounting, and planning
Lakehouse architectures and their role in modern finance stacks
Batch vs near-real-time pipelines for financial reporting and analytics
Establishing a single source of truth across FP&A, risk, and trading
Data governance, lineage, and auditability in financial systems
Cost, performance, and scalability tradeoffs in large financial platforms
The book emphasizes engine-agnostic design, allowing readers to apply the concepts across Snowflake, BigQuery, Redshift, Delta Lake, Iceberg, and other modern data platforms without locking into vendor-specific implementations.
Who this book is forData architects and analytics engineers
Finance and FP&A leaders designing enterprise reporting systems
Trading and risk infrastructure professionals
Senior analysts moving beyond spreadsheet-based workflows
Engineering managers responsible for financial data platforms
This book assumes familiarity with SQL and financial data concepts. It is intended for readers who already understand how to query data and now need to understand how to design systems that scale, remain trustworthy, and support high-stakes financial decisions.
Financial Data Warehousing & Lakehouse Architecture provides a structured framework for building financial data platforms that are resilient, transparent, and capable of supporting both historical analysis and real-time operational insight.
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Financial Data Warehousing & Lakehouse Architecture (Paperback)
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Paperback. Etat : new. Paperback. Reactive PublishingFinancial organizations do not suffer from a lack of data. They suffer from fragmented systems, inconsistent definitions, and architectures that fail under scale.Financial Data Warehousing & Lakehouse Architecture is a practical guide to designing SQL-first financial data platforms that remain coherent, auditable, and performant as complexity increases. It bridges the gap between classical data warehousing theory and modern lakehouse implementations, translating both into financial systems that support decision-making, risk management, and real-time analytics.This book is written for professionals responsible for building and maintaining the data backbone of financial organizations. It focuses on architectural decisions that determine whether financial data becomes a strategic asset or a long-term liability.What this book coversCore principles of financial data warehousingStar, snowflake, and hybrid schemas for financial use casesDesigning fact and dimension tables for markets, accounting, and planningLakehouse architectures and their role in modern finance stacksBatch vs near-real-time pipelines for financial reporting and analyticsEstablishing a single source of truth across FP&A, risk, and tradingData governance, lineage, and auditability in financial systemsCost, performance, and scalability tradeoffs in large financial platformsThe book emphasizes engine-agnostic design, allowing readers to apply the concepts across Snowflake, BigQuery, Redshift, Delta Lake, Iceberg, and other modern data platforms without locking into vendor-specific implementations.Who this book is forData architects and analytics engineersFinance and FP&A leaders designing enterprise reporting systemsTrading and risk infrastructure professionalsSenior analysts moving beyond spreadsheet-based workflowsEngineering managers responsible for financial data platformsThis book assumes familiarity with SQL and financial data concepts. It is intended for readers who already understand how to query data and now need to understand how to design systems that scale, remain trustworthy, and support high-stakes financial decisions.Financial Data Warehousing & Lakehouse Architecture provides a structured framework for building financial data platforms that are resilient, transparent, and capable of supporting both historical analysis and real-time operational insight. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798278766254
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Financial Data Warehousing & Lakehouse Architecture (Paperback)
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Paperback. Etat : new. Paperback. Reactive PublishingFinancial organizations do not suffer from a lack of data. They suffer from fragmented systems, inconsistent definitions, and architectures that fail under scale.Financial Data Warehousing & Lakehouse Architecture is a practical guide to designing SQL-first financial data platforms that remain coherent, auditable, and performant as complexity increases. It bridges the gap between classical data warehousing theory and modern lakehouse implementations, translating both into financial systems that support decision-making, risk management, and real-time analytics.This book is written for professionals responsible for building and maintaining the data backbone of financial organizations. It focuses on architectural decisions that determine whether financial data becomes a strategic asset or a long-term liability.What this book coversCore principles of financial data warehousingStar, snowflake, and hybrid schemas for financial use casesDesigning fact and dimension tables for markets, accounting, and planningLakehouse architectures and their role in modern finance stacksBatch vs near-real-time pipelines for financial reporting and analyticsEstablishing a single source of truth across FP&A, risk, and tradingData governance, lineage, and auditability in financial systemsCost, performance, and scalability tradeoffs in large financial platformsThe book emphasizes engine-agnostic design, allowing readers to apply the concepts across Snowflake, BigQuery, Redshift, Delta Lake, Iceberg, and other modern data platforms without locking into vendor-specific implementations.Who this book is forData architects and analytics engineersFinance and FP&A leaders designing enterprise reporting systemsTrading and risk infrastructure professionalsSenior analysts moving beyond spreadsheet-based workflowsEngineering managers responsible for financial data platformsThis book assumes familiarity with SQL and financial data concepts. It is intended for readers who already understand how to query data and now need to understand how to design systems that scale, remain trustworthy, and support high-stakes financial decisions.Financial Data Warehousing & Lakehouse Architecture provides a structured framework for building financial data platforms that are resilient, transparent, and capable of supporting both historical analysis and real-time operational insight. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798278766254
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