Name: Azure Data and AI Architect Handbook
Author: Olivier Mertens Breght Van Baelen
ISBN: 9781803230733

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Azure Data and AI Architect Handbook

Olivier Mertens Breght Van Baelen

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文前
Contributors
Acknowledgement
Preface
Who is this book for?
What this book covers
Conventions used
Get in touch
Reviews
Share Your Thoughts
Part 1:Introduction to Azure Data Architect
1 Introduction to Data Architectures
Understanding the value of data
Types of analytics
A complexity-value matrix
A data architecture reference diagram
The ingestion layer
The storage layer
The serving layer
The consumption layer
Data orchestration and processing
Advanced analytics
Data governance and compliance
Security
Monitoring
Challenges of on-premises architectures
Scalability
Cost optimization
Agility
Flexibility
Summary
2 Preparing for Cloud Adoption
The Azure WAF
Reliability
Security
Cost optimization
Estimating the initial costs
Operational excellence
Performance efficiency
Data landing zones
Fundamentals of Azure core services
Data management landing zone architecture
Data landing zone architecture
Summary
Part 2:Data Engineering on Azure
3 Ingesting Data into the Cloud
Batch and streaming ingestion
Why batch ingestion?
Why streaming ingestion?
When to use both batch and streaming ingestion?
ADLS for raw data ingestion
Azure storage accounts
ADLS
Redundancy
Access tiers
Why data lakes?
Data lake tiers
Batch ingestion architectures
Ingesting data from cloud sources
Ingesting data from on-premises sources
Streaming ingestion architectures
Lambda architecture
Kappa architecture
Delta lake architecture
Streaming ingestion with Event Hubs
Streaming ingestion with IoT Hub
Remote asset monitoring
Summary
4 Transforming Data on Azure
Designing data pipelines on Azure
ETL versus ELT
Azure Data Factory versus Synapse pipelines
Transforming data on Azure
Batch transformation–mapping data flows
Batch transformation–Spark notebooks
Batch transformation–SQL scripts
Batch transformation–SSIS
Real-time transformation–Azure Stream Analytics
Real-time transformation–Azure Databricks
Data transformation architectures
Batch transformation architecture
Stream transformation architecture
Deciding on the right data transformation service
Data transformations in data lake tiers
Bronze-to-silver transformations
Silver-to-gold transformations
Operationalizing data pipelines on Azure
Scheduling data pipelines on Azure
Monitoring data pipelines on Azure
CI/CD for data pipelines on Azure
Summary
5 Storing Data for Consumption
Classifying the data type
Structured data
Semi-structured data
Unstructured data
Determining how the data will be used
Relational databases
Non-relational or NoSQL databases
Online transactional processing
Online analytical processing
Choosing the right storage solution on Azure
Azure OLTP services
Azure OLAP services
Azure NoSQL services
Summary
Part 3:Data Warehousing and Analytics
6 Data Warehousing
Fundamental concepts of data warehousing
Database normalization
Data marts versus data warehouses
Design methods of data warehouses
Data warehouse characteristics
Approaches to data warehousing
Normalized approach by Bill Inmon
Dimensional approach by Ralph Kimball
SCDs
Type 1 SCD
Type 2 SCD
Type 3 SCD
Type 6 SCD
Building a data warehouse in the cloud
Using Azure SQL Database
Using Synapse serverless SQL pools
Using Synapse dedicated SQL pools
Summary
7 The Semantic Layer
Multidimensional versus tabular models
The VertiPaq engine for tabular models
Modes in tabular models
Tools for the semantic layer
Power BI as a superset of AAS
Summary
8 Visualizing Data Using Power BI
Learning how Power BI works
Get data
Transform and visualize
Share and collaborate
Consume
Choosing the right license and pricing
Practicing your skills
Getting the data
Preparing the data
Enriching data with DAX
Modeling the data
Creating visuals
Moving to self-service BI
Summary
9 Advanced Analytics Using AI
Knowing the roles in data science
Designing AI solutions
Is AI the right solution?
Do we opt for pre-trained models or a custom model?
Is data available?
Is the data of acceptable quality?
Low code or code first?
What are the requirements for the AI model?
Batch or real-time inferencing?
Is explainability required?
What is the expected ROI?
Understanding AI on Azure
Azure Cognitive Services
Azure OpenAI Service
Fine-tuning and private deployments
Grounding LLMs
Azure Machine Learning and MLOps
AI architectures on Azure
Scoring data in batches
Scoring streaming data with a custom model
Summary
Part 4:Data Security, Governance, and Compliance
10 Enterprise-Level Data Governance and Compliance
The importance of data governance and compliance
The roles in data governance and compliance
Governing data with Microsoft Purview
Data Map
Data Catalog
Data Estate Insights
Applying enterprise-level data governance
Preparing the business for data governance and management
Data governance frameworks
Summary
11 Introduction to Data Security
Data protection
Encryption at rest
Encryption in transit
Access control
RBAC
Shared access signatures
Restricting internal data access
Authentication
Network security
Threat protection
Microsoft Defender for Cloud
Microsoft Sentinel
Azure services supporting data security
Summary
Index
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Azure Data and AI Architect Handbook