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The Definitive Guide to Data Integration

You're reading from   The Definitive Guide to Data Integration Unlock the power of data integration to efficiently manage, transform, and analyze data

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Product type Paperback
Published in Mar 2024
Publisher Packt
ISBN-13 9781837631919
Length 490 pages
Edition 1st Edition
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Authors (4):
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Raphaël MANSUY Raphaël MANSUY
Author Profile Icon Raphaël MANSUY
Raphaël MANSUY
Pierre-Yves BONNEFOY Pierre-Yves BONNEFOY
Author Profile Icon Pierre-Yves BONNEFOY
Pierre-Yves BONNEFOY
Emeric CHAIZE Emeric CHAIZE
Author Profile Icon Emeric CHAIZE
Emeric CHAIZE
Mehdi TAZI Mehdi TAZI
Author Profile Icon Mehdi TAZI
Mehdi TAZI
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Toc

Table of Contents (19) Chapters Close

Preface 1. Chapter 1: Introduction to Our Data Integration Journey 2. Chapter 2: Introducing Data Integration FREE CHAPTER 3. Chapter 3: Architecture and History of Data Integration 4. Chapter 4: Data Sources and Types 5. Chapter 5: Columnar Data Formats and Comparisons 6. Chapter 6: Data Storage Technologies and Architectures 7. Chapter 7: Data Ingestion and Storage Strategies 8. Chapter 8: Data Integration Techniques 9. Chapter 9: Data Transformation and Processing 10. Chapter 10: Transformation Patterns, Cleansing, and Normalization 11. Chapter 11: Data Exposition and APIs 12. Chapter 12: Data Preparation and Analysis 13. Chapter 13: Workflow Management, Monitoring, and Data Quality 14. Chapter 14: Lineage, Governance, and Compliance 15. Chapter 15: Various Architecture Use Cases 16. Chapter 16: Prospects and Challenges 17. Index 18. Other Books You May Enjoy

Data Transformation and Processing

In today’s data-centric world, the art and science of data transformations form a cornerstone of any data integration process. Their importance lies in their ability to modify or rearrange data in ways that render it primed for analysis, display, or subsequent processing. By understanding and leveraging data transformations, we can unlock the latent potential of data, offering insights that might otherwise remain obscured.

The primary aim of this chapter is to elucidate what data transformations are and to explore their most prevalent types, including filters, aggregations, and joins. We recognize that each dataset, with its unique characteristics and challenges, requires a tailored approach. Thus, our goal is to empower you with the knowledge and tools to discern which strategies are best suited for your specific data needs.

Data transformations are not merely about understanding data, but acting upon that understanding. It’s...

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