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Data Modeling for Azure Data Services

You're reading from   Data Modeling for Azure Data Services Implement professional data design and structures in Azure

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781801077347
Length 428 pages
Edition 1st Edition
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Author (1):
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Peter ter Braake Peter ter Braake
Author Profile Icon Peter ter Braake
Peter ter Braake
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Table of Contents (16) Chapters Close

Preface 1. Section 1 – Operational/OLTP Databases
2. Chapter 1: Introduction to Databases FREE CHAPTER 3. Chapter 2: Entity Analysis 4. Chapter 3: Normalizing Data 5. Chapter 4: Provisioning and Implementing an Azure SQL DB 6. Chapter 5: Designing a NoSQL Database 7. Chapter 6: Provisioning and Implementing an Azure Cosmos DB Database 8. Section 2 – Analytics with a Data Lake and Data Warehouse
9. Chapter 7: Dimensional Modeling 10. Chapter 8: Provisioning and Implementing an Azure Synapse SQL Pool 11. Chapter 9: Data Vault Modeling 12. Chapter 10: Designing and Implementing a Data Lake Using Azure Storage 13. Section 3 – ETL with Azure Data Factory
14. Chapter 11: Implementing ETL Using Azure Data Factory 15. Other Books You May Enjoy

Background of data lakes

Let's start with the definition of a data lake:

A data lake is an environment where you collect and store (vast amounts of) raw data in its original format.

The term data lake comes from a water analogy. You can make money using water, just as you can make money by using data. But you will need to store the water somewhere until you find a use case for it. You don't necessarily know beforehand what that use case is going to be. This means you need a cheap and easy way to store the water. Putting all your water in bottles is optimal when you want to sell it as drinking water. But pouring water out of a bottle over a house that is on fire in the hope of extinguishing the fire would probably be useless. So, you wouldn't bottle it until you started selling drinking water:

Figure 10.1 – Data lake

Data is analogous to water. When you store it for later use, but you don't necessarily know all the (possible) use cases (yet...

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