Azure Data Fundamentals. A Guide to DP-900 Certification and Beyond - Helion

ebook

Autor: Michael John Pena
ISBN: 9781098164690
stron: 336, Format: ebook
Data wydania: 2025-12-08
Księgarnia: Helion

Cena książki: 189,00 zł

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Learn the essential skills and concepts for working with data in the cloud using Microsoft Azure. With this practical guide, professionals new to data management and Azure will learn how to leverage Azure services such as Azure Cosmos DB, Azure Storage, Azure SQL, and Microsoft Fabric to create, store, process, analyze, and visualize data.

Author Michael John Peña, principal data and application engineer, also shows you how to apply security, monitoring, and optimization techniques to your data solutions in Azure. Ideal as a resource for the Azure Data Fundamentals certification, this book also provides knowledge you can apply in your daily work even after platforms evolve and new technologies emerge.

You'll gain insights that will help you apply the fundamentals of data operations and engineering in the Azure ecosystem.

Learn core data concepts and the corresponding Microsoft Azure data services
Discover how to work with relational and nonrelational data on Azure
Build data analytics solutions using various Azure data services
Identify and describe core data workloads and data analytics workloads on Azure
Articulate how to work with data security and privacy on Azure
Explain the use cases, benefits, and limitations of different Azure data services

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Spis treści

Azure Data Fundamentals. A Guide to DP-900 Certification and Beyond eBook -- spis treści

Preface
- Who Should Read This Book
- Why I Wrote This Book
- Navigating This Book
- Staying Current
- Conventions Used in This Book
- Using Code Examples
- OReilly Online Learning
- How to Contact Us
- Acknowledgments
I. Core Data Concepts
1. Introduction to Data Representation
- Understanding the Data Spectrum
- Structured Data
  - Characteristics of Structured Data
  - Common Examples of Structured Data
  - Use Cases in Azure Environments
  - Benefits and Limitations of Structured Data
- Semi-Structured Data
  - Characteristics of Semi-Structured Data
  - Common Examples of Semi-Structured Data
  - Use Cases in Azure Environments
  - Benefits and Limitations of Semi-Structured Data
- Unstructured Data
  - Characteristics of Unstructured Data
  - Common Examples of Unstructured Data
  - Use Cases in Azure Environments
  - Benefits and Limitations of Unstructured Data
- Bringing It All Together: Data Representation in Practice
  - Making an Informed Decision About Data Representation
  - Implementing Evolution and Adaptation Strategies
- Summary
- Beyond the Exam
  - Common Misconceptions About Data Types and When to Use Each
  - Real-World Examples of Data Type Decisions Gone Wrong
  - Practical Tips for Evaluating Data Structure Needs
  - How to Future-Proof Your Data Representation Choices
2. Data Storage Fundamentals
- Data File Format Essentials
  - CSV: The Universal Translator
    - Strengths of the CSV format
    - Limitations that matter in practice
    - When to choose CSV
  - JSON: The Modern Standard
    - The power of a flexible structure
    - Real-world applications
    - Trade-offs to consider
  - Parquet: The Analytics Powerhouse
    - Why columnar storage matters
    - Exceptional performance benefits
    - When Parquet excels
    - Considerations for implementation
  - Avro: The Evolution Engine
    - Schema evolution in practice
    - Optimal use cases
  - How to Choose the Right Format for Your Scenario
- Types of Databases: Choosing Your Data Foundation
  - Relational Databases: The Reliable Foundation
    - The ACID foundation
    - Powerful query capabilities
    - When relational databases shine
    - Limitations
  - NoSQL Databases: The Flexibility Champions
    - Document databases: Natural data representation
    - Key-value databases: Simple and fast
    - Column-family databases: Big data optimization
    - Graph databases: Relationship excellence
    - NoSQL decisions
  - Data Warehouses: The Analytics Specialists
    - Analytical architecture
    - Data processing
    - Data warehouse use cases
- Cloud Versus On-Premises Storage: The Strategic Choice
  - Advantages of Cloud Storage
    - Scalability without limits
    - Cost efficiency through scale
    - Global capabilities
    - Built-in advanced features
  - Considerations for Cloud Storage
    - Cost management complexity
    - Compliance and control considerations
  - Advantages of On-Premises Storage
    - Complete control and customization
    - Cost predictability
    - Performance and latency
    - Compliance advantages
  - Considerations for On-Premises Storage
    - Capital and operational requirements
    - Scalability limitations
  - Azure Hybrid Solutions
    - Best of both worlds
- Summary
- Beyond the Exam
  - Choosing the Right Storage Solution: A Practical Decision Framework
  - Identifying the Hidden Costs in Cloud Storage: What to Watch For
  - Future-Proofing Your Data Storage Choices
3. Understanding Data Workloads
- Data Workloads in Everyday Terms
- The Workload Spectrum
  - Transactional Workloads (OLTP)
    - A real-world OLTP scenario
      - The rush is on
      - Cascading real-time operations
      - Why every millisecond matters
      - The OLTP foundation
    - Key characteristics of OLTP systems
      - High-volume, short transactions
      - Strong consistency and ACID properties
      - High concurrency requirements
      - Current, operational data focus
    - Common OLTP use cases
      - Ecommerce and retail operations
      - Banking and financial services
      - Healthcare information systems
  - Analytical Workloads (OLAP)
    - A real-world OLAP scenario
      - The big picture analysis
      - Complex analytical processing
      - Strategic decision making
      - The OLAP foundation
    - Key characteristics of OLAP systems
      - Complex queries analyzing large datasets
      - Query performance over transaction consistency
      - Fewer concurrent users, resource-intensive queries
      - Historical data focus
    - Common OLAP use cases
      - Business intelligence and reporting
      - Financial analysis and planning
      - Marketing analytics and customer intelligence
- Bringing It All Together: Modern Workload Scenarios
  - The Modern Data Architecture
  - A Comprehensive Real-World Example
- Summary
- Beyond the Exam
  - Real-World Mixed Workload Examples
  - Performance Considerations
  - Common Implementation Pitfalls
4. Data Roles and Responsibilities in Azure
- Modern Data Roles
  - Database Administrators
    - Typical scenario for database administrators
    - Core responsibilities of database administrators in Azure
    - Azure-related tools and platforms for database administrators
    - Future evolution of database administrators in Azure
  - Data Engineers
    - Core responsibilities of data engineers in Azure
    - Azure-related tools and platforms for data engineers
    - Future evolution of data engineers in Azure
  - Data Analysts
    - Core responsibilities of data analysts in Azure
    - Azure-related tools and platforms for data analysts
    - Future evolution of data analysts in Azure
  - Data Scientists
    - Core responsibilities of data scientists in Azure
    - Azure-related tools and platforms for data scientists
    - Future evolution of data scientists in Azure
  - Data Governors
    - Core responsibilities of data governors in Azure
    - Azure-related tools and platforms for data governors
    - Future evolution of data governors in Azure
- Bringing It All Together: Role Interactions in Practice
- Summary
- Beyond the Exam
  - Real-World Role Dynamics
  - Career Progression and Emerging Roles
  - Organizational Impact
  - Future Outlook
II. Relational Data on Azure
5. Relational Data Concepts
- Core Relational Concepts
  - Understanding Tables
    - Columns and fields
    - Records and rows
    - Primary keys
  - Data Types and Constraints
    - Basic data types
    - Data rules with constraints
- Understanding Relationships and Keys
  - Types of Table Relationships
    - One-to-one relationships
    - One-to-many relationships
    - Many-to-many relationships
  - Use of Foreign Keys to Establish Table Relationships
  - A Practical Example
- Basic SQL Queries
  - Retrieving Data with SELECT
    - Filtering with WHERE
    - Sorting with ORDER BY
  - Modifying Data
    - Adding records with INSERT
    - Modifying records with UPDATE
    - Removing records with DELETE
    - Visualizing your SQL operations
  - Joining Tables
- Common Database Objects
  - Views: Simplifying Data Access
  - Indexes: Improving Data Retrieval
  - Stored Procedures: Creating Reusable Code
  - Triggers: Creating Automatic Responses
- Bringing It All Together: The Library Database in Practice
  - Basic Banking Database Structure
  - Common Database Operations
    - Calculating account balances
    - Processing transactions
    - Generating monthly statements
  - A Practical Example
- Summary
- Beyond the Exam
  - Modern Relational Database Evolution
  - Implementation Realities
  - The Scale Challenge
  - Emerging Directions
6. Azure SQL Services and Open Source Options
- Understanding the Azure SQL Family
  - Azure SQL Database: Cloud Native Database Solution
    - Features that drive cloud adoption
    - Deployment decisions
    - The shared responsibility model
  - Azure SQL Managed Instance: The Bridge to the Cloud
    - The value proposition
    - Real-world migration scenarios
    - Operational considerations
  - SQL Server on Azure Virtual Machines: Maximum Control and Flexibility
    - Control and responsibility
    - Cost and performance optimization
- Exploring Open Source Databases in Azure
  - Azure Database for MySQL: Powering Web Applications
    - Service capabilities
    - Deployment models
      - Burstable tier
      - General Purpose tier
      - Business Critical tier
    - Integration and development
  - Azure Database for PostgreSQL: Enterprise-Grade Open Source
    - Service capabilities
    - Deployment models
      - Burstable tier
      - General Purpose tier
      - Memory Optimized tier
    - Extensions and ecosystem
- Bringing It All Together: Choosing the Right Database Service
  - Starting with the Basics
  - Understanding Your Workload
  - Thinking About Growth
  - Simplifying Cost Considerations
- Summary
- Beyond the Exam
  - Making the Right Choice in Practice
  - Navigating Cost Management
  - Understanding Differences in Implementation
  - Looking Ahead
III. Nonrelational Data on Azure
7. Azure Storage Solutions
- Azure Blob Storage
  - Understanding Object Storage
  - Types of Blobs
    - Block blobs: The workhorses of cloud storage
    - Append blobs: The digital logbook
    - Page blobs: The virtual disk specialists
  - Access Tiers and Cost Management
    - Hot tier: Ready for immediate access
    - Cool tier: Balancing access and economy
    - Archive tier: The digital time capsule
  - Security Features of Blob Storage
    - Encryption: Protecting data at rest and in motion
    - Role-based access control: The digital gatekeeper
    - Shared access signatures: Temporary keys to your data kingdom
  - Managing the Data Lifecycle
- Azure File Storage
  - Understanding Cloud File Shares
  - SMB and NFS: Speaking Your Language
    - The power of SMB integration
    - NFS and Linux integration
  - Advanced Features That Transform File Sharing
    - Snapshots: Time travel for your files
    - Identity and access management
    - Microsoft Entra ID integration
    - Scale and performance
  - Azure File Sync: Extending to Hybrid Scenarios
    - Cloud tiering: Intelligence at the edge
    - Disaster recovery simplified
  - Managing at Scale
- Azure Table Storage
- Understanding NoSQL in Azure Table Storage
  - Entities and Properties: A Flexible Foundation
  - Partitioning Strategy: The Key to Performance
    - The power of the partition key
    - Row keys: Ensuring uniqueness
  - Query Patterns and Performance
    - Optimized query patterns
    - Advanced query capabilities
      - Filtering and projection
      - Continuation tokens
    - Table Storage versus Azure Cosmos DB
- Bringing It All Together: Storage Solutions in Practice
  - The Modern Data Estate
  - Integration Patterns in Action
  - Bridging Worlds with Hybrid Solutions
  - The Art of Cost Optimization
- Summary
- Beyond the Exam
  - Modern Storage Evolution
  - Implementation Realities
  - The Scale Challenge
  - Emerging Directions
8. Azure Cosmos DB
- Understanding the Cosmos DB Architecture
- The Power of Global Distribution
  - Understanding Consistency Models
    - Strong consistency
    - Eventual consistency
    - Consistent prefix
    - Bounded staleness
    - Session consistency
  - Understanding Data Modeling in Azure Cosmos DB
- Azure Cosmos DB API Types
  - NoSQL (Core) API in Azure Cosmos DB
  - MongoDB API in Azure Cosmos DB
  - Cassandra API in Azure Cosmos DB
  - Table API in Azure Cosmos DB
  - Gremlin API in Azure Cosmos DB
- Bringing It All Together: Azure Cosmos DB in Practice
  - Embracing the Modern Data Platform
  - Harmonizing Different Data Models
  - Building Connections Through Graph Data
  - Implementing Cost Optimization and Performance Tuning
- Summary
- Beyond the Exam
  - The Reality of Global Distribution in the Supply Chain
  - Cost Management in Supply Chain Operations
  - Supply Chain Data Modeling Evolution
  - Integration Challenges in Multiregion Operations
  - Performance Tuning for the Global Supply Chain
  - The Future of Global Supply Chain Technologies
IV. Analytics on Azure
9. Large-Scale Analytics
- Understanding Large-Scale Analytics
  - The Scale Challenge
  - Components of Large-Scale Analytics
- Data Ingestion and Processing
  - The Ingestion Challenge
  - Batch and Streaming Paradigms
  - Processing Considerations
- Analytical Data Stores
  - The Storage Challenge
  - Data Lakes
  - Data Warehouses
  - Analytical Databases
  - How to Choose the Right Analytical Store
- Microsoft Cloud Services for Large-Scale Analytics
  - Azure Databricks
  - Azure Synapse Analytics
  - Microsoft Fabric
  - How to Choose Between Analytics Services
- Bringing It All Together: Large-Scale Analytics in Practice
  - The Data Landscape
  - The Analytics Architecture
  - Implementation Approach
- Summary
- Beyond the Exam
  - The Organizational Factor
  - Implementation Realities
  - The Scale Challenge
  - Emerging Directions
10. Real-Time Analytics
- Understanding Real-Time Analytics
  - The Time-Value Relationship of Data
  - The Evolution from Batch to Real-Time Analytics
  - The Architecture of Real-Time Analytics
- Batch Versus Streaming Data
  - Characteristics of Batch Processing
  - Characteristics of Streaming Data
  - When to Use Each Approach
- Microsoft Cloud Services for Real-Time Analytics
  - Azure Event Hubs
  - Azure Stream Analytics
  - Azure Synapse Analytics
  - Azure Data Explorer
  - Azure Event Grid
  - Azure Functions
  - Azure IoT Hub
  - How to Choose the Right Real-Time Analytics Services
- Bringing It All Together: Real-Time Analytics in Practice
  - The Data Landscape
  - The Analytics Architecture
  - Implementation Approach
- Summary
- Beyond the Exam
  - Organizational Readiness
  - Implementation Realities
  - Emerging Directions
11. Data Visualization with Power BI
- Understanding Data Visualization
  - The Power of Visual Communication
  - The Evolution of Business Intelligence
  - The Analytical Workflow
- Power BI Capabilities
  - Power BI Components
  - Connectivity and Data Access
  - Data Transformation and Preparation
  - Data Modeling
  - Visualization and Analysis
  - Sharing and Collaboration
- Data Models in Power BI
  - Model Structures and Relationships
  - Calculated Columns and Measures
  - Row-Level Security
  - DirectQuery and Import Modes
- Visualizations in Power BI
  - Choosing the Right Visualization
  - Understanding Dashboard and Report Design
  - Implementing Advanced Visualization Techniques
- Bringing It All Together: Power BI in Practice
  - The Data Landscape
  - The Power BI Implementation
  - The Implementation Approach
  - The Business Outcomes
- Summary
- Beyond the Exam
  - Organizational Factors
  - Implementation Realities
  - Emerging Directions
V. Beyond DP-900
12. Advanced Azure Data Concepts
- The Modern Data Governance Imperative
  - Understanding Microsoft Purview
  - Implementing Data Governance at Scale
- The Rise of AI and Large Language Models
  - Understanding Large Language Models in Azure
  - Building AI Agents with Azure
  - Practicing Responsible AI and Data Ethics
- The Convergence: Governed AI
  - Governance-Aware AI Systems
  - Building the Future of Data Work
- Your Journey Beyond DP-900
  - The Skills That Matter
  - Pathways Forward
  - The Azure Advantage
- Summary
- Conclusion: Your Data Future Starts Now
- Beyond This Book
  - Practical Next Steps
  - How to Stay Current
  - Final Thoughts
Index