Book Image

Data Modeling for Azure Data Services

By : Peter ter Braake
Book Image

Data Modeling for Azure Data Services

By: Peter ter Braake

Overview of this book

Data is at the heart of all applications and forms the foundation of modern data-driven businesses. With the multitude of data-related use cases and the availability of different data services, choosing the right service and implementing the right design becomes paramount to successful implementation. Data Modeling for Azure Data Services starts with an introduction to databases, entity analysis, and normalizing data. The book then shows you how to design a NoSQL database for optimal performance and scalability and covers how to provision and implement Azure SQL DB, Azure Cosmos DB, and Azure Synapse SQL Pool. As you progress through the chapters, you'll learn about data analytics, Azure Data Lake, and Azure SQL Data Warehouse and explore dimensional modeling, data vault modeling, along with designing and implementing a Data Lake using Azure Storage. You'll also learn how to implement ETL with Azure Data Factory. By the end of this book, you'll have a solid understanding of which Azure data services are the best fit for your model and how to implement the best design for your solution.

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Table of Contents (16 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
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1
Section 1 – Operational/OLTP Databases
Section 1 – Operational/OLTP Databases
Free Chapter
2
Chapter 1: Introduction to Databases
Chapter 1: Introduction to Databases
Overview of relational databases
Introduction to Structured Query Language
Impact of intended usage patterns on database design
Understanding relational theory
Keys
Types of workload
Summary
3
Chapter 2: Entity Analysis
Chapter 2: Entity Analysis
Scope
Understanding entity relationship diagrams
Entities
Relationships
Creating your first ERD
Context of an ERD
Summary
Exercises
4
Chapter 3: Normalizing Data
Chapter 3: Normalizing Data
When to use normalization as a design strategy
Preventing redundancy
The normalization steps
An alternative approach to normalizing data
Integrating separate results
Entity relationship diagram
Summary
Exercises
5
Chapter 4: Provisioning and Implementing an Azure SQL DB
Chapter 4: Provisioning and Implementing an Azure SQL DB
Technical requirements
Understanding SQL Server data types
Quantifying the data model
Provisioning an Azure SQL database
Connecting to the database
Data definition language
Inserting data
Indexing
Summary
6
Chapter 5: Designing a NoSQL Database
Chapter 5: Designing a NoSQL Database
Understanding big data
Understanding big data clusters
Getting to know Cosmos DB
Key-value databases
Other NoSQL databases
Extra considerations
Summary
Exercise
7
Chapter 6: Provisioning and Implementing an Azure Cosmos DB Database
Chapter 6: Provisioning and Implementing an Azure Cosmos DB Database
Technical requirements
Provisioning a Cosmos DB database
Creating a container
Uploading documents to a container
Cosmos DB container settings
Importing data using the Azure Cosmos DB Data Migration tool
Summary
8
Section 2 – Analytics with a Data Lake and Data Warehouse
Section 2 – Analytics with a Data Lake and Data Warehouse
9
Chapter 7: Dimensional Modeling
Chapter 7: Dimensional Modeling
Background to dimensional modeling
Understanding dimensional modeling
Steps in dimensional modeling
Designing dimensions
Designing fact tables
Using a Kimball data warehouse versus data marts
Summary
Exercise
10
Chapter 8: Provisioning and Implementing an Azure Synapse SQL Pool
Chapter 8: Provisioning and Implementing an Azure Synapse SQL Pool
Overview of Synapse Analytics
Provisioning a Synapse Analytics workspace
Creating a dedicated SQL pool
Implementing tables in Synapse SQL pools
Understanding workload management
Using PolyBase to load data
Connecting to and using a dedicated SQL pool
Summary
11
Chapter 9: Data Vault Modeling
Chapter 9: Data Vault Modeling
Background to Data Vault modeling
Designing Hub tables
Designing Link tables
Designing Satellite tables
Using hash keys
Designing a Data Vault structure
Designing business vaults
Implementing a Data Vault
Summary
Exercise
12
Chapter 10: Designing and Implementing a Data Lake Using Azure Storage
Chapter 10: Designing and Implementing a Data Lake Using Azure Storage
Technical requirements
Background of data lakes
Modeling a data lake
Using different file formats
Choosing the proper file size
Provisioning an Azure storage account
Creating a data lake filesystem
Creating multiple storage accounts
Summary
13
Section 3 – ETL with Azure Data Factory
Section 3 – ETL with Azure Data Factory
14
Chapter 11: Implementing ETL Using Azure Data Factory
Chapter 11: Implementing ETL Using Azure Data Factory
Technical requirements
Introducing Azure Data Factory
Introducing the main components of Azure Data Factory
Using the copy activity
Implementing a data flow
Executing SQL code from Data Factory
Summary
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15
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Cosmos DB container settings

When you query a container, like the examples in the previous section, you want your container to be indexed to ensure correct performance. By default, the indexing is set to automatic. You can adjust the indexing policy to change the types of indexes being created. Indexes require extra writes when inserting or updating data in your container. That means that you need more request units for the same workload when writing. So, indexes come at a cost. Because indexes help to locate data quickly, they enhance the read performance:

  1. Open the Storage Explorer page.
  2. Open the Product container.
  3. Click on Scale & Settings.

Notice the first tab, Scale, is where you find the provisioned throughput. This is also where you can switch from Manual to Autoscale or back.

The second tab, Settings, lets you configure Time to Live. The Time to Live feature on Cosmos DB will automatically delete documents after an expiry period. Session data for...

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