Building Data Integration Solutions: Unifying Data for Enhanced Decision-making

Borthen, Jay/ Wrobel, Donna

ISBN 10: 1098173066 / ISBN 13: 9781098173067

Published by Oreilly & Associates Inc, 2025

About this Item

300 pages. 9.19x7.00x9.19 inches. In Stock.

Seller Inventory # x-1098173066

Bibliographic Details

• Title: Building Data Integration Solutions: Unifying Data for Enhanced Decision-making
• Publisher: Oreilly & Associates Inc
• Publication Date: 2025
• Language: English
• ISBN 10: 1098173066
• ISBN 13: 9781098173067
• Binding: Paperback
• Condition: Brand New
• Weight: 0.49 kilograms

About this title

Synopsis

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

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.