{"product_id":"financial-data-engineering-with-python-market-accounting-and-forecasting-pipeline-design-9798247274483","title":"Financial Data Engineering with Python: Market, Accounting, and Forecasting Pipeline Design","description":"\u003cp\u003e • Author(s): Danny Munrow | James Preston\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Finance - Financial Engineering\u003c\/p\u003e\u003cp\u003e\u003cb\u003eReactive Publishing\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eFinancial data is no longer just stored. It is engineered, validated, versioned, and deployed like production software.\u003c\/p\u003e\u003cp\u003e\u003ci\u003eFinancial Data Engineering with Python\u003c\/i\u003e is a practical, system-level guide for building robust financial data pipelines that support market analytics, accounting infrastructure, and forward-looking forecasting models. Designed for financial analysts, data engineers, quant researchers, and technical finance professionals, this book bridges the gap between traditional financial data handling and modern production-grade data architecture.\u003c\/p\u003e\u003cp\u003eInstead of focusing on theory alone, this book shows how real financial data systems are structured in high-performance environments where data latency, accuracy, auditability, and reproducibility directly impact decision-making and risk exposure.\u003c\/p\u003e\u003cp\u003eInside, you will learn how to: \u003c\/p\u003e\u003cp\u003e- Design resilient market data pipelines for pricing, trading, and risk systems\u003cbr\u003e- Engineer accounting data flows that support reconciliation, audit trails, and reporting integrity\u003cbr\u003e- Build forecasting data layers that integrate historical, real-time, and external macro datasets\u003cbr\u003e- Implement Python-based ETL, validation, and monitoring frameworks for financial workloads\u003cbr\u003e- Structure financial data models for scalability across research, reporting, and production systems\u003cbr\u003e- Reduce data fragility using schema controls, versioning, and automated quality checks\u003c\/p\u003e\u003cp\u003eThe book emphasizes production reality: messy source data, regulatory constraints, system interoperability, and the need for repeatable, testable data processes across financial organizations.\u003c\/p\u003e\u003cp\u003eWhether you are modernizing legacy finance workflows, building institutional-grade analytics infrastructure, or developing next-generation financial data platforms, this guide provides a clear, implementation-focused blueprint grounded in real-world financial data engineering practice.\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47570281267351,"sku":"9798247274483","price":2949.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798247274483.webp?v=1774881077","url":"https:\/\/atlanticbooks.com\/products\/financial-data-engineering-with-python-market-accounting-and-forecasting-pipeline-design-9798247274483","provider":"Atlantic Books","version":"1.0","type":"link"}