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The Definitive Guide to Data Integration

You're reading from  The Definitive Guide to Data Integration

Product type Book
Published in Mar 2024
Publisher Packt
ISBN-13 9781837631919
Pages 490 pages
Edition 1st Edition
Languages
Authors (4):
Pierre-Yves BONNEFOY Pierre-Yves BONNEFOY
Profile icon Pierre-Yves BONNEFOY
Emeric CHAIZE Emeric CHAIZE
Profile icon Emeric CHAIZE
Raphaël MANSUY Raphaël MANSUY
Profile icon Raphaël MANSUY
Mehdi TAZI Mehdi TAZI
Profile icon Mehdi TAZI
View More author details

Table of Contents (19) Chapters

Preface 1. Chapter 1: Introduction to Our Data Integration Journey 2. Chapter 2: Introducing Data Integration 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

Why, when, and where to perform data preparation

In the dynamic world of data management and analysis, the preparation of data holds a pivotal role. This section aims to shed light on the key components and concepts of data preparation. It also underscores its significance in the broader framework of data integration.

Data preparation is often likened to setting the stage before a grand performance. It’s the process of cleaning, structuring, and enriching raw data to improve its quality and usefulness. Similar to a well-prepared stage enhancing an artist’s performance, well-prepared data can significantly improve the accuracy and reliability of data analysis, thereby leading to more insightful and actionable business intelligence.

First and foremost, let’s delve into the essence of data preparation. At its core, it’s about making raw data more suitable for analytics or other business processes. This involves various sub-processes, including data cleansing...

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