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Today, investment in financial technology and digital transformation is reshaping the financial landscape and generating many opportunities. Too often, however, engineers and professionals in financial institutions lack a practical and comprehensive understanding of the concepts, problems, techniques, and technologies necessary to build a modern, reliable, and scalable financial data infrastructure. This is where financial data engineering is needed.
A data engineer developing a data infrastructure for a financial product possesses not only technical data engineering skills but also a solid understanding of financial domain-specific challenges, methodologies, data ecosystems, providers, formats, technological constraints, identifiers, entities, standards, regulatory requirements, and governance.
This book offers a comprehensive, practical, domain-driven approach to financial data engineering, featuring real-world use cases, industry practices, and hands-on projects.
You'll learn:
The data engineering landscape in the financial sector
Specific problems encountered in financial data engineering
The structure, players, and particularities of the financial data domain
Approaches to designing financial data identification and entity systems
Financial data governance frameworks, concepts, and best practices
The financial data engineering lifecycle from ingestion to production
The varieties and main characteristics of financial data workflows
How to build financial data pipelines using open source tools and APIs
Tamer Khraisha, PhD, is a senior data engineer and scientific author with more than a decade of experience in the financial sector.
Who Should Read This Book?
This book serves a wide audience. This includes individuals working at institutions such as banks, investment firms, financial data providers, asset management companies, security exchanges, regulatory bodies, financial software vendors, and many more. It is designed for data engineers, software developers, quantitative developers, financial analysts, and Machine Learning practitioners who are managing and/or working with financial data and financial data-driven products. Furthermore, the book appeals to scholars and researchers working on data-driven financial analysis, reflecting the growing interest in big data research in the financial sector. Whether you’re a practitioner seeking insights into data-driven financial services, a scholar investigating finance-related problems, or a newcomer eager to venture into the financial field with a technology-oriented role, this book is designed to meet your needs.
Prerequisites:
To get the most out of the hands-on exercises in Chapter 12, I recommend having some basic knowledge of the following:
Python programming
SQL and PostgreSQL
Using tools like Python JupyterLab, Python Notebooks, and Pandas
Running Docker containers locally
Basic Git commands
However, if you’re unfamiliar with all of these, don’t worry! You can still dive into the projects and learn as you go along