Share E-Book
Scan to open this page

Scan with your phone to open this page

Author: Tamer Khraisha

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.

AI Reading Assistant

Whole-book reading guide from stratified index samples; jump to passages in the text

Brief outline
【One-Line Pitch】 Today, investment in financial technology and digital transformation is reshaping the financial landscape and generat… 【Book Arc】 - **Opening (~0%–12%)**: This is where financial data engineering is needed.; itutions and money and capital markets Quantitative Finance Covers theoretical and empirical inte… - **Early (~12%–35%)**: ent of LLMs for the financial domain.; edge funds an ideal workplace for financial data engineers. - **Middle (~35%–65%)**: In some cases, reference data is simple.; is typically used to represent the company’s common stock. - **Late (~65%–88%)**: Approaches to Entity Resolution Numerous ER techniques have been proposed in the literature and by market partici‐ pants.; ed within the finan‐ cial institution’s data infrastructure. - **Ending (~88%–100%)**: Without a systematic approach in mind, this complexity may lead to chaotic situations and accumulating costly tech‐ nical debt.; traction, transformation, storage, and consumption of data. 【Key Takeaways】 - **This is where financia…** (Opening): This is where financial data engineering is needed. - **itutions and money and…** (Opening): itutions and money and capital markets Quantitative Finance Covers theoretical and empirical inte… - **For example** (Opening): For example, assume we have an initial sample of 10 firms and 5 features about their performance (e.g., revenues, net profit, and so on). - **ent of LLMs for the fi…** (Early): ent of LLMs for the financial domain. - **edge funds an ideal wo…** (Early): edge funds an ideal workplace for financial data engineers. - **the right vendor can b…** (Early): the right vendor can be quite challenging. 【Reading Tips】 - Use Passage locations below to jump into the text and set reading anchors - If this is a brief outline, click Regenerate (top right) for a synthesized guide 【Coverage Limits】 Compressed outline without the model (~33 index chunks). Full structured guide needs AI available.
Excerpt 1
isha Foreword by Martijn Groot Afterword by Brian Buzzelli The Path Forward: Trends Shaping Financial Markets. . . . . . . . . . . . . . . . . . . . . . . ....
View in text
Excerpt 2
edge funds an ideal workplace for financial data engineers. Regulatory institutions A variety of national and international regulatory bodies have been estab...
View in text
Excerpt 3
he latest version (120). Table 2-7. PIT versus non-PIT data Data type Preliminary result Fiscal year-end release Correction Version 101 110 120 PIT 101 110 1...
View in text
Excerpt 4
hanges in the UK. The LSE (the National Numbering Agency of the UK) assigns SEDOL codes upon request from the security issuer.12 Over the years, the SEDOL sy...
View in text
Excerpt 5
ce on commodity exchanges NAL U.S. Country in North America ORG Federal Reserve Central Bank of the United States of America O rose more than 1% on Wednesday...
View in text
Excerpt 6
of the system) or at the individual institution level (e.g., Bank A holds $1mln at Bank B, $33mln at Bank C, and so on). Financial time series These can be r...
View in text
Excerpt 7
How It Works and Best Practices for Businesses”, by Stripe. 198 | Chapter 5: Financial Data Governance Table 5-3. Anonymized data after suppression ID Compan...
View in text
Excerpt 8
Such a feature is quite crucial for financial applications. For example, for a digital bank‐ ing firm that is branchless, online services must always be on a...
View in text
Tags
AI categories
Framework
ISBN: 1098159993
Publisher: O'Reilly Media
Publish Year: 2024
Language: English
Pages: 507
File Format: PDF
File Size: 12.6 MB
Text Preview (First 20 pages)
Registered users can read the full content for free

Register as a Gaohf Library member to read the complete e-book online for free and enjoy a better reading experience.

Generating text preview…