Financial Data Engineering Tamer Khraisha

Financial Data Engineering Tamer Khraisha - okladka książki

Autor:: Tamer Khraisha
Wydawnictwo:: O'Reilly Media (Z chęcią przeczytam książkę w języku polskim)
Ocena:: Bądź pierwszym, który oceni tę książkę
Stron:: 506
Dostępne formaty:: ePub

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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.

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Szczegóły książki

ISBN Ebooka:: 978-10-981-5995-5, 9781098159955
Data wydania ebooka :: 2024-10-09 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@ebookpoint.pl.
Język publikacji:: 1
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Rozmiar pliku Mobi:: 28.8MB

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Informatyka » Biznes IT » Big data » Analiza danych

Spis treści książki

Foreword
Preface
- Who Should Read This Book?
- Prerequisites
- What to Expect from This Book
- Book Resources and References
- Conventions Used in This Book
- Using Code Examples
- OReilly Online Learning
- How to Contact Us
- Acknowledgments
I. Foundations of Financial Data Engineering
1. Financial Data Engineering Clarified
- Defining Financial Data Engineering
  - First of All, What Is Finance?
    - Finance as an economic function
    - Finance as a market
    - Finance as a research field
    - Finance as a technology
  - Defining Data Engineering
  - Defining Financial Data Engineering
- Why Financial Data Engineering?
  - Volume, Variety, and Velocity of Financial Data
    - Volume
    - Velocity
    - Variety
  - Finance-Specific Data Requirements and Problems
  - Financial Machine Learning
    - Supervised learning
    - Unsupervised learning
    - Reinforcement learning
  - The Disruptive FinTech Landscape
  - Regulatory Requirements and Compliance
- The Financial Data Engineer Role
  - Description of the Role
  - Where Do Financial Data Engineers Work?
    - FinTech
    - Commercial banks
    - Investment banks
    - Asset management firms
    - Hedge funds
    - Regulatory institutions
    - Financial data vendors
    - Security exchanges
    - Big tech firms
  - Responsibilities and Activities of a Financial Data Engineer
    - Starting with data
    - Scaling with data
    - Leading with data
  - Skills of a Financial Data Engineer
    - Financial domain knowledge
    - Technical data engineering skills
    - Business and soft skills
- Summary
2. Financial Data Ecosystem
- Sources of Financial Data
  - Public Financial Data
    - Regulatory disclosure requirements
    - Public institutional and governmental data
    - Public research data
    - Free stock market APIs
  - Security Exchanges
  - Commercial Data Vendors, Providers, and Distributors
    - Bloomberg
    - LSEG Eikon
    - FactSet
    - S&P Global Market Intelligence
    - Wharton Research Data Services
  - Survey Data
  - Alternative Data
  - Confidential and Proprietary Data
- Structures of Financial Data
  - Time Series Data
  - Cross-Sectional Data
  - Panel Data
  - Matrix Data
  - Graph Data
    - Simple graphs
    - Directed graphs
    - Weighted graphs
    - Multipartite graphs
    - Temporal graphs
    - Multilayer graphs
  - Text Data
- Types of Financial Data
  - Fundamental Data
  - Market Data
  - Transaction Data
    - Transaction specifications
    - Initiation date
    - Settlement date
    - Settlement method
    - Transaction parties
  - Analytics Data
  - Alternative Data
  - Reference Data
  - Entity Data
- Benchmark Financial Datasets
  - Center for Research in Security Prices
  - Compustat Financials
  - Trade and Quote Database
  - Institutional Brokers Estimate System
  - IvyDB OptionMetrics
  - Trade Reporting and Compliance Engine
  - Orbis Global Database
  - SDC Platinum
  - Standard & Poors Dow Jones Indices
  - Alternative Datasets
    - BitSight Security Ratings
    - Global New Vehicle Registrations
    - Weather Source
    - Patent data
- Summary
3. Financial Identification Systems
- Financial Identifiers
  - Financial Identifier and Identification System Defined
  - The Need for Financial Identifiers
  - Who Creates Financial Identification Systems?
    - International Organization for Standardization (ISO)
    - National Numbering Agencies
    - Financial data vendors
    - Financial institutions
- Desired Properties of a Financial Identifier
  - Uniqueness
  - Globality
  - Scalability
  - Completeness
  - Accessibility
  - Timeliness
  - Authenticity
  - Granularity
  - Permanence
  - Immutability
  - Security
- Financial Identification Systems Landscape
  - International Securities Identification Number
  - Classification of Financial Instruments
  - Financial Instrument Short Name
  - Committee on Uniform Security Identification Procedures
  - Legal Entity Identifier
  - Transaction Identifiers
  - Stock Exchange Daily Official List
  - Ticker Symbols
  - Derivative Identifiers
    - Option symbol
    - CFI, UPI, and OTC ISIN
    - Alternative Instrument Identifier
  - Financial Instrument Global Identifier
  - FactSet Permanent Identifier
  - LSEG Permanent Identifier
  - Digital Asset Identifiers
  - Industry and Sector Identifiers
  - Bank Identifiers
- Summary
4. Financial Entity Systems
- Financial Entity Defined
- Financial Named Entity Recognition
  - Named Entity Recognition Described
  - How Does Named Entity Recognition Work?
    - Data preprocessing
    - Entity extraction
    - Entity categorization
    - Entity disambiguation
    - Evaluation
  - Approaches to Named Entity Recognition
    - Lexicon/dictionary-based approach
    - Rule-based approach
    - Feature-engineering machine learning approach
    - Deep learning approach
    - Large language models
    - Wikification
    - Knowledge graphs
  - Named Entity Recognition Software Libraries
- Financial Entity Resolution
  - Entity Resolution Described
  - The Importance of Entity Resolution in Finance
    - Multiple identifiers
    - Missing identifiers
    - Data aggregation and integration
    - Data deduplication
  - How Does Entity Resolution Work?
    - Data preprocessing
    - Indexing
    - Comparison
    - Classification
    - Evaluation
  - Approaches to Entity Resolution
    - Deterministic linkage
      - Link tables
      - Exact matching
      - Rule-based matching
    - Probabilistic linkage
    - Supervised machine learning approach
  - Entity Resolution Software Libraries
- Summary
5. Financial Data Governance
- Financial Data Governance
  - Financial Data Governance Defined
  - Financial Data Governance Justified
- Data Quality
  - Dimension 1: Data Errors
  - Dimension 2: Data Outliers
  - Dimension 3: Data Biases
  - Dimension 4: Data Granularity
  - Dimension 5: Data Duplicates
  - Dimension 6: Data Availability and Completeness
  - Dimension 7: Data Timeliness
  - Dimension 8: Data Constraints
  - Dimension 9: Data Relevance
- Data Integrity
  - Principle 1: Data Standards
  - Principle 2: Data Backups
  - Principle 3: Data Archiving
  - Principle 4: Data Aggregation
  - Principle 5: Data Lineage
  - Principle 6: Data Catalogs
  - Principle 7: Data Ownership
  - Principle 8: Data Contracts
  - Principle 9: Data Reconciliation
- Data Security and Privacy
  - Data Privacy
  - Data Anonymization
    - Anonymization strategy
    - Anonymization techniques
  - Data Encryption
  - Access Control
- Summary
II. The Financial Data Engineering Lifecycle
6. Overview of the Financial Data Engineering Lifecycle
- Financial Data Engineering Lifecycle Defined
- Criteria for Building the Financial Data Engineering Stack
  - Criterion 1: Open Source Versus Commercial Software
  - Criterion 2: Ease of Use Versus Performance
  - Criterion 3: Cloud Versus On Premises
    - On premises
    - Cloud computing
  - Criterion 4: Public Versus Private Versus Hybrid Cloud
    - Public cloud
    - Private cloud
    - Hybrid cloud
  - Criterion 5: Single Versus Multi-Cloud
  - Criterion 6: Monolithic Versus Modular Codebase
    - Monolith architecture
    - Modular architecture
- Summary
7. Data Ingestion Layer
- Data Transmission and Arrival Processes
  - Data Transmission Protocols
    - Application layer
    - Transport layer
    - Network layer
    - Network access layer
  - Data Arrival Processes
    - Scheduled data arrival process
    - Event-driven data arrival process
    - Homogeneous data arrival process
    - Heterogeneous data arrival process
    - Single-item data arrival process
    - Bulk data arrival process
- Data Ingestion Formats
  - General-Purpose Formats
  - Big Data Formats
  - In-Memory Formats
  - Standardized Financial Formats
    - Financial Information eXchange (FIX)
    - eXtensible Business Reporting Language (XBRL)
    - Financial products Markup Language (FpML)
    - Open Financial Exchange (OFX)
    - Universal Financial Industry Message Scheme (ISO 20022)
- Data Ingestion Technologies
  - Financial APIs
  - Financial Data Feeds
  - Secure File Transfer
  - Cloud Access
  - Web Access
  - Specialized Financial Software
- Data Ingestion Best Practices
  - Meet Business Requirements
  - Design for Change
  - Enforce Data Governance
  - Perform Benchmarking and Stress Testing
- Summary
8. Data Storage Layer
- Principles of Data Storage System Design
  - Principle 1: Business Requirements
  - Principle 2: Data Modeling
  - Principle 3: Transactional Guarantee
  - Principle 4: Consistency Tradeoffs
  - Principle 4: Scalability
  - Principle 5: Security
- Data Storage Modeling
  - SQL Versus NoSQL
  - Primary Versus Secondary
  - Operational Versus Analytical
  - Native Versus Non-Native
  - Multi-Model Versus Polyglot Persistence
- Data Storage Models
  - The Data Lake Model
    - Why data lakes?
    - Technological implementations of data lakes
    - Data modeling with data lakes
    - Data governance
    - Financial use cases of data lakes
  - The Relational Model
    - Why relational databases?
      - SQL standards
      - ACID transactions
      - Analytical querying
      - Schema enforcement
    - Data modeling with relational databases
      - Normalization
      - Constraints
      - Indexing of relational databases
    - Technological implementations of relational databases
    - Financial use cases of relational databases
  - The Document Model
    - Why document databases?
    - Data modeling with document databases
      - Document and collection structure
      - Denormalization
      - Indexing of document databases
    - Technological implementations of document databases
    - Financial use cases of document databases
  - The Time Series Model
    - Why time series databases?
    - Data modeling with time series
    - Technological implementations of time series databases
    - Financial use cases of time series databases
  - The Message Broker Model
    - Why message brokers?
    - Data modeling with message brokers
      - Topic modeling
      - Message schemas
    - Technological implementations of message brokers
    - Financial use cases of message brokers
  - The Graph Model
    - Why a graph model?
    - Data modeling with graph databases
    - Technological implementations of graph databases
    - Financial use cases of graph databases
  - The Warehouse Model
    - Why data warehouses?
    - Data modeling with data warehouses
    - Technological implementations of data warehousing
    - Financial use cases of data warehouses
  - The Blockchain Model
- Summary
9. Data Transformation and Delivery Layer
- Data Querying
  - Querying Patterns
    - Time series queries
    - Cross-section queries
    - Panel queries
    - Analytical queries
  - Query Optimization
    - Database-side query optimization
    - User-side query optimization
      - Scenario 1
      - Scenario 2
      - Scenario 3
      - Scenario 4
      - Scenario 5
      - Scenario 6
- Data Transformation
  - Transformation Operations
    - Format conversion
    - Data cleaning
    - Data adjustments
    - Data standardization
    - Data filtering
    - Feature engineering
    - Advanced analytical computations
  - Transformation Patterns
    - Batch versus streaming transformations
    - Memory-based versus disk-based transformations
    - Full versus incremental data transformations
  - Computational Requirements
    - Computational performance
      - Computational speed
      - Throughput
      - Computational efficiency
      - Scalability
    - Computing environments
- Data Delivery
  - Data Consumers
  - Delivery Mechanisms
- Summary
10. The Monitoring Layer
- Metrics, Events, Logs, and Traces
  - Metrics
  - Events
  - Logs
  - Traces
- Data Quality Monitoring
- Performance Monitoring
- Cost Monitoring
- Business and Analytical Monitoring
- Data Observability
- Summary
11. Financial Data Workflows
- Workflow-Oriented Software Architectures
- What Is a Data Workflow?
- Workflow Management Systems
  - Flexibility
  - Configurability
  - Dependency Management
  - Coordination Patterns
  - Scalability
  - Integration
- Types of Financial Data Workflows
  - Extract-Transform-Load Workflows
  - Stream Processing Workflows
  - Microservice Workflows
  - Machine Learning Workflows
- Summary
12. Hands-On Projects
- Prerequisites
- Project 1: Designing a Bank Account Management System Database with PostgreSQL
  - Conceptual Model: Business Requirements
    - Entities
    - Relationships
    - Constraints
  - Logical Model: Entity Relationship Diagram
  - Physical Model: Data Definition and Manipulation Language
  - Project 1: Local Testing
  - Project 1: Clean Up
  - Project 1: Summary
- Project 2: Designing a Financial Data ETL Workflow with Mage and Python
  - Project 2: Workflow Definition
  - Project 2: Database Design
  - Project 2: Local Testing
  - Project 2: Clean Up
  - Project 2: Summary
- Project 3: Designing a Microservice Workflow with Netflix Conductor, PostgreSQL, and Python
  - Project 3: Workflow Definition
  - Project 3: Database Design
  - Project 3: Local Testing
  - Project 3: Clean Up
  - Project 3: Summary
- Project 4: Designing a Financial Reference Data Store with OpenFIGI, PermID, and GLEIF APIs
  - Project 4: Prerequisites
  - Project 4: Local Testing
  - Project 4: Clean Up
  - Project 4: Summary
- Conclusion
- Follow Updates on These Projects
- Report Issues or Ask Questions
The Path Forward: Trends Shaping Financial Markets
- Financial Integration
- Digitalization of Financial Markets and Cloud Adoption
- Financial Regulation
- Financial Data Sharing and Marketplaces
- Financial Standardization
- Artificial Intelligence and Language Models
- Architectures for Specific Business Domains
- Data Collection
- Speed and Efficiency
- Tokenization, Blockchain, and Digital Currencies
- What Can You Do Next?
Afterword
Index