English | December 9, 2024 | ASIN: B0DQ2FNQ8M | 148 pages | PDF
Prepare for the Microsoft DP-700 exam with our comprehensive guide to Data Engineering Solutions using Microsoft Fabric. This book is your ultimate resource to pass the DP-700 Microsoft certification exam, designed for data engineers looking to level up their skills and boost their careers.
Syllabus Outline
Chapter 1: Introduction to Microsoft Fabric
- Introduction to DP-700
- Overview of Microsoft Fabric
- Key components of Microsoft Fabric
- Prerequisites for the DP-700 Exam
- Preparing for the Exam
- Career and Job Opportunities
Chapter 2: Planning and Designing Data Engineering Solutions
- Medallion architecture principles
- Data modeling best practices
- Choosing appropriate data storage solutions (e.g., Azure Data Lake, Delta Lake)
- Real-time vs. batch data processing considerations
Chapter 3: Implementing Data Ingestion
- Batch data ingestion using pipelines and dataflows
- Streaming data ingestion using EventStream and Spark Structured Streaming
- Handling different ingestion patterns: full load, incremental load, and mirroring
- Data quality and duplication management
Chapter 4: Transforming Data in Microsoft Fabric
- Data transformation tools: PySpark, T-SQL, and KQL
- Delta Lake API: Optimizations, VACUUM, and V-Order
- Denormalization and aggregation techniques
- Handling late-arriving and missing data
Chapter 5: Orchestrating Data Pipelines
- Creating pipelines with triggers and schedules
- Orchestration with notebooks and parameterized workflows
- Integration of pipelines with Git for version control
- Dynamic data flow patterns
- Error handling and debugging in pipelines
Chapter 6: Securing Data Solutions
- Workspace and item-level access controls
- Row-level, column-level, and object-level security
- Dynamic data masking
- Sensitivity labels and data governance practices
Chapter 7: Real-Time Intelligence
- Processing and querying real-time data
- Implementing windowing functions
- Monitoring real-time data flows with EventStream and KQL
- Building and optimizing real-time analytics solutions
Chapter 8: Monitoring and Optimizing Data Solutions
- Monitoring data pipelines and transformations
- Resolving errors in pipelines, dataflows, and notebooks
- Performance optimization for data ingestion and transformations
- Configuring alerts and real-time monitoring
Chapter 9: Deploying and Managing Solutions
- Using deployment pipelines in Microsoft Fabric
- Multi-environment deployment strategies
- Managing semantic models and data warehouses
- Best practices for deployment and scaling
Chapter 10: Practice Questions and Exam Preparation
- Practice MCQs with detailed explanations
- Case-study-based scenarios
- Tips for exam success
Key Features of This Book:
- Planning and Designing Data Solutions
- Batch and Streaming Data Ingestion
- Data Transformation
- Securing Data
- Real-Time Analytics
- Exam Preparation
