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Total Size:
11.5 MB
Info Hash:
06E5AA1B108E727F9164CAD92CE66E63B6BEF0E7
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Nov. 1, 2025, 1:53 p.m.
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(Last updated: Nov. 1, 2025, 1:54 p.m.)
| File | Size |
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| Code .zip | 2.2 MB |
| Borthen J. Building Data Integration Solutions...Enhanced Decision Making 2026.pdf | 9.3 MB |
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11.5 MB
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2025-11-01
| Uploaded by andryold1 | Size 11.5 MB | Health [ 19 /11 ] | Added 2025-11-01 |
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SOURCE: Borthen J. Building Data Integration Solutions...Enhanced Decision Making 2026
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COVER
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MEDIAINFO
Textbook in PDF format 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. This book presents a pragmatic, hands-on approach to data integration by first baselining the reader’s knowledge with important terminology and concepts and eventually walking through the building of a plausible, real-life data integration solution, step by step. For the hands-on parts in the later chapters of the book, familiarity with Linux, Python, Structured Query Language (SQL), and Amazon Web Services (AWS) would be beneficial, but I’ll attempt to explain what takes place at each step in simple terms. It should be noted that containerization is not utilized in the hands-on sections of this book. However, it may be prudent for the practitioner to use containers (e.g., Docker) and perhaps even a distributed container management tool like Kubernetes for large, enterprise data integration initiatives. Also, while the book focuses on aspects directly related to data integration, because of inherent complexity within data engineering and data management, topic tangents and parallel subject matter discussions are inevitable. There are also many concepts included in the book that live within the blurred lines between data engineering and software development. Supplemental material (code examples, exercises, etc.) is available for download at GitHub. You will: Understand various data integration solutions and how different technologies can be employed Gain insights into the relationship between data integration and the overall data lifecycle 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 Contents: Preface I. Foundations of Data Integration Introduction to Data Integration Key Concepts in Data Integration Data Integration Challenges Models, Architectures, Methods, and Patterns II. Tools, Technologies, and Frameworks Data Integration Tool Options Data Stores and Management Systems Data Ingestion and Streaming Tools Comprehensive Integration Suites III. Introducing the Example Data Integration Solution Introducing the Example Solution Implementing a Batch Solution mplementing a Streaming Solution A. Setting Up the Data Integration Solution Example B. References Key Terms Glossary Acronyms Glossary Index
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