فهرست مطالب

• Foreword
• Contributors
• About the authors
• About the reviewers
• Preface
• Chapter 1: Introduction to Our Data Integration Journey
• The essence of data integration
• The contemporary landscape
• Challenges and opportunities
• The purpose and vision of this book
• Summary
• Chapter 2: Introducing Data Integration
• Defining data integration
• Introducing the modern data stack
• Data culture and strategy
• Data integration techniques, tools, and technologies
• Summary
• Chapter 3: Architecture and History of Data Integration
• History of data integration
• Influential open source data technologies
• The impact of open source on data integration and analytics
• Data integration architectures
• The future of data integration
• Summary
• Chapter 4: Data Sources and Types
• Understanding the data sources: Relational databases, NoSQL, flat files, APIs, and more
• Working with data types and structures
• Going through data formats: CSV, JSON, XML, and more
• Summary
• Chapter 5: Columnar Data Formats and Comparisons
• Exploring columnar data formats
• Understanding the advantages and challenges of working with different data formats
• Summary
• Chapter 6: Data Storage Technologies and Architectures
• Central analytics data storage technologies
• Data architectures
• Positions and roles in data management
• Summary
• Chapter 7: Data Ingestion and Storage Strategies
• The goal of ingestion
• Data storage and modeling techniques
• Optimizing storage performance
• Defining the adapted strategy
• Summary
• Chapter 8: Data Integration Techniques
• Data integration models – point-to-point and middleware-based integration
• Data integration architectures – batch, micro-batching, real-time, and incremental
• Data integration patterns – ETL, ELT, and others
• Data integration organizational models
• Summary
• Chapter 9: Data Transformation and Processing
• The power of SQL in data transformation
• Data transformation possibilities
• Massively parallel processing
• Spark and data transformation
• Different types of data transformation
• Summary
• Chapter 10: Transformation Patterns, Cleansing, and Normalization
• Transformation patterns
• Data cleansing and normalization
• Summary
• Chapter 11: Data Exposition and APIs
• Understanding the strategic motives for data exposure
• Going through the data exposition technologies
• A focus on APIs and strategy
• A comparative analysis of data exposure solutions
• Summary
• Chapter 12: Data Preparation and Analysis
• Why, when, and where to perform data preparation
• Strategy and the choice of transformations
• Key concepts for reporting and self-analysis
• Summary
• Chapter 13: Workflow Management, Monitoring, and Data Quality
• Going through the concepts of workflow management, event management, and monitoring
• Understanding data quality and data observability
• Summary
• Chapter 14: Lineage, Governance, and Compliance
• Understanding the concept of data lineage
• Adhering to regulations and implementing robust governance frameworks
• Summary
• Chapter 15: Various Architecture Use Cases
• Data integration for real-time data analysis
• Data integration for cloud-based data analysis
• Data integration for geospatial data analysis
• Data integration for IoT data analysis
• Summary
• Chapter 16: Prospects and Challenges
• Prospects of data integration in the current data stack
• Future challenges and opportunities of data integration
• Advancing your understanding of data integration in the modern stack
• Summary
• Index
• Other Books You May Enjoy