Are you struggling to manage and make sense of the vast streams of data flowing into your organization? In today's data-driven world, the ability to effectively unify and organize disparate data sources is not just an advantage—it's a necessity. The challenge lies in navigating the complexities of data diversity, volume, and regulatory demands, which can overwhelm even the most seasoned data professionals.

In this essential book, Jay Borthen offers a comprehensive guide to understanding the art of data integration. This book dives deep into the processes and strategies necessary for creating effective data pipelines that ensure consistency, accuracy, and accessibility of your data. Whether you're a novice looking to understand the basics or an experienced professional aiming to refine your skills, Borthen's insights and practical advice, grounded in real-world case studies, will empower you to transform your organization's data handling capabilities.

• Understand various data integration solutions and how different technologies can be employed
• Gain insights into the relationship between data integration and the overall data life cycle
• Learn to effectively design, set up, and manage data integration components within pipelines
• Acquire the knowledge to configure pipelines, perform data migrations, transformations, and more

Table of Contents

Part I. Foundations of Data Integration

Chapter 1. Introduction to Data Integration

Chapter 2. Key Concepts in Data Integration

Chapter 3. Data Integration Challenges

Chapter 4. Models, Architectures, Methods, and Patterns

Part II. Tools, Technologies, and Frameworks

Chapter 5. Data Integration Tool Options

Chapter 6. Data Stores and Management Systems

Chapter 7. Data Ingestion and Streaming Tools

Chapter 8. Comprehensive Integration Suites

Part Ill. Introducing the Example Data Integration Solution

Chapter 9. Introducing the Example Solution

Chapter 10. Implementing a Batch Solution

Chapter 11. Implementing a Streaming Solution

About the Author

Jay Borthen is the head of data science and engineering at Swish Data. After graduate school, he migrated from engineering into data science-specific roles and has since worked with clients including the IRS, the FDA, the US Navy, the US Department of State, and a handful of commercial enterprises. Jay has almost 20 years of experience analyzing complex systems, leading tech teams, and leveraging technologies, such as AWS, Databricks, Kafka/Confluent, Qlik, and Snowflake. He has an undergraduate degree in engineering from Virginia Tech and a master's degree in mathematics and statistics from Georgetown University.