Azure Data Engineer Associate Certification Guide: Ace the DP-203 exam with advanced data engineering skills

✍ Scribed by Giacinto Palmieri, Surendra Mettapalli, Newton Alex

• Publisher: Packt Publishing - ebooks Account
• Year: 2024
• Tongue: English
• Leaves: 549
• Edition: 2
• Category: Library

✦ Synopsis

Achieve Azure Data Engineer Associate certification success with this DP-203 exam guide

Purchase of this book unlocks access to web-based exam prep resources including mock exams, flashcards, and exam tips, and the eBook PDF

Key Features

• Prepare for the DP-203 exam with expert insights, real-world examples, and practice resources
• Gain up-to-date skills to thrive in the dynamic world of cloud data engineering
• Build secure and sustainable data solutions using Azure services

Book Description

One of the top global cloud providers, Azure offers extensive data hosting and processing services, driving widespread cloud adoption and creating a high demand for skilled data engineers. The Azure Data Engineer Associate (DP-203) certification is a vital credential, demonstrating your proficiency as an Azure data engineer to prospective employers. This comprehensive exam guide is designed for both beginners and seasoned professionals, aligned with the latest DP-203 certification exam, to help you pass the exam on your first try.

The book provides a foundational understanding of IaaS, PaaS, and SaaS, starting with core concepts like virtual machines (VMs), VNETS, and App Services and progressing to advanced topics such as data storage, processing, and security. What sets this exam guide apart is its hands-on approach, seamlessly integrating theory with practice through real-world examples, practical exercises, and insights into Azure's evolving ecosystem. Additionally, you'll unlock lifetime access to supplementary practice material on an online platform, including mock exams, interactive flashcards, and exam tips, ensuring a comprehensive exam prep experience.

By the end of this book, you’ll not only be ready to excel in the DP-203 exam, but also be equipped to tackle complex challenges as an Azure data engineer.

What you will learn

• Design and implement data lake solutions with batch and stream pipelines
• Secure data with masking, encryption, RBAC, and ACLs
• Perform standard extract, transform, and load (ETL) and analytics operations
• Implement different table geometries in Azure Synapse Analytics
• Write Spark code, design ADF pipelines, and handle batch and stream data
• Use Azure Databricks or Synapse Spark for data processing using Notebooks
• Leverage Synapse Analytics and Purview for comprehensive data exploration
• Confidently manage VMs, VNETS, App Services, and more

Who this book is for

This book is for data engineers who want to take the Azure Data Engineer Associate (DP-203) exam and delve deep into the Azure cloud stack. Engineers and product managers new to Azure or preparing for interviews with companies working on Azure technologies will find invaluable hands-on experience with Azure data technologies through this book.

A basic understanding of cloud technologies, ETL, and databases will assist with understanding the concepts covered.

Table of Contents

1. Introducing Azure Basics
2. Implementing a Partition Strategy
3. Designing and Implementing the Data Exploration Layer
4. Ingesting and Transforming Data
5. Developing a Batch Processing Solution
6. Developing a Stream Processing Solution
7. Managing Batches and Pipelines
8. Implementing Data Security
9. Monitoring Data Storage and Data Processing
10. Optimizing and Troubleshooting Data Storage and Data Processing

✦ Table of Contents

Cover
FM
Copyright
Contributors
Table of Contents
Preface
Part 1: Azure Basics
Chapter 1: Introducing Azure Basics
Making the Most Out of this Book – Your Certification and Beyond
Technical Requirements
Introducing the Azure Portal
Exploring Azure Accounts, Subscriptions, and Resource Groups
Azure Account
Azure Subscription
Resource Groups
Resources
Establishing a Use Case
Introducing Azure Services
Infrastructure as a Service (IaaS)
Platform as a Service (PaaS)
Software as a Service (SaaS)
Exploring Azure VMs
Creating a VM Using the Azure Portal
Creating a VM Using the Azure CLI
Installing the CLI
Exploring Azure Storage
Azure Blob Storage
Azure Data Lake Gen2
Azure Files
Creating Azure File Shares with the Azure CLI
Azure Queues
Creating Azure Queues Using the CLI
Azure Tables
Creating Azure Tables Using the CLI
Azure Managed Disks
Creating and Attaching Managed Disks to a VM Using the CLI
Exploring Azure Networking (VNet)
Creating an Azure VNet Using the CLI
Summary
Exam Readiness Drill – Chapter Review Questions
Part 2: Data Storage
Chapter 2: Implementing a Partition Strategy
Technical Requirements
Benefits of Partitioning
Improving Performance
Improving Scalability
Improving Manageability
Improving Security
Improving Availability
Designing a Partition Strategy for Files
Azure Blob Storage
Azure Data Lake Storage Gen2
Designing Partition Strategy for Analytical Workloads
Horizontal Partitioning
Selecting the Right Shard Key
Vertical Partitioning
Functional Partitioning
Implementing Partition Strategy for Streaming Workloads
Event Hubs
Stream Analytics
Azure Databricks
Partition Strategy for Efficiency and Performance
Iterative Query Performance Improvement Process
Designing Partition Strategy for Azure Synapse Analytics
Performance Improvement while Loading Data
Performance Improvement for Filtering Queries
Recognizing Partitioning Needs in ADLS Gen2
Summary
Exam Readiness Drill – Chapter Review Questions
Chapter 3: Designing and Implementing the Data Exploration Layer
Technical Requirements
Introduction to Data Exploration
SQL Serverless and Spark Clusters
BI Tools
Synapse Spark
Azure Databricks
Azure Databricks Service
Spark Cluster in Azure Databricks
Azure Synapse Analytics Database Templates
Microsoft Purview
Searching Feature in Microsoft Purview Data Catalog
Refining Search Results Using Facets and Filters
Summary
Exam Readiness Drill – Chapter Review Questions
Part 3:Data Processing
Chapter 4: Ingesting and Transforming Data
Technical Requirements
Designing and Implementing Incremental Loads
Watermarks
File Timestamps
File Partitions and Folder Structures
Transforming Data Using Apache Spark
What are Resilient Distributed Datasets (RDDs)
What Are DataFrames?
Transforming Data Using T-SQL
The Transforming Options Available in ADF
ADF Templates
Transformations Using Synapse Pipelines
Transforming Data Using Stream Analytics
Cleansing Data
Handling Duplicate Data within Azure Environments
Handling Missing Data
Handling Late-Arriving Data
Splitting Data
File Splits
Shredding JSON to Manage Data Elements
Encoding and Decoding Data
Configuring Error Handling for the Transformation
Normalizing and Denormalizing Values
Denormalizing Values Using Pivot
Normalizing Values Using Unpivot
Performing Data Exploratory Analysis
Data Exploration Using Spark
Data Exploration Using SQL
Data Exploration Using ADF
Summary
Exam Readiness Drill – Chapter Review Questions
Chapter 5: Developing a Batch Processing Solution
Technical Requirements
Batch-Processing Technologies
Storage
Data Ingestion
Transformation
Configuring an ADB Notebook Activity in ADF
Batch-Processing Technology Choices
Using PolyBase to Load Data to a SQL Pool
Options for Loading with PolyBase
Implementing Azure Synapse Link and Querying Replicated Data
Creating Data Pipelines
Scaling Resources
ADF and Synapse Pipelines
Azure Databricks
Synapse Spark
Synapse SQL
Configuring Batch Size
Creating Tests for Data Pipelines
Integrating Jupyter/Python Notebooks into a Data Pipeline
Upserting Data
Reverting Data to a Previous State
Configuring Exception Handling
Configuring Batch Retention
Reading from and Writing to a Delta Lake
Summary
Exam Readiness Drill – Chapter Review Questions
Chapter 6: Developing a Stream Processing Solution
Technical Requirements
Implementing a Streaming Use Case with Azure
Introducing Azure Event Hubs
Introducing Azure Stream Analytics (ASA)
Introducing Spark Streaming
Streaming Solution Using Event Hubs and ASA
Streaming Solution Using Event Hubs and Spark Streaming
Processing Data Using Spark Structured Streaming
Creating Windowed Aggregates
Tumbling Windows
Hopping Windows
Sliding Windows
Session Windows
Snapshot Windows
Handling Schema Drifts
Handling Schema Drifts Using Event Hubs
Registering a Schema with Schema Registry
Retrieving a Schema from Schema Registry
Handling Schema Drifts in Spark
Processing Time Series Data
Types of Timestamps
Windowed Aggregates
Checkpointing or Watermarking
Replaying Data from a Previous Timestamp
Processing Data across Partitions
What Are Partitions?
Processing within One Partition
Configuring Checkpoints and Watermarking
Checkpointing in ASA
Checkpointing in Event Hubs
Checkpointing in Spark
Scaling Resources
Scaling in Event Hubs
What Are TUs?
Auto-Inflate Scaling
Scaling in ASA
Scaling in Azure Databricks Spark Streaming
Developing Testing Processes for Data Pipelines
Optimizing Pipelines for Analytical or Transactional Purposes
Implementing HTAP Using Synapse Link and Cosmos DB
Introducing Cosmos DB
Introducing Azure Synapse Link
Handling Interruptions
Handling Interruptions in Event Hubs
Handling Interruptions in ASA
Configure Exception Handling
Upserting Data
Replaying Archived Stream Data
Summary
Exam Readiness Drill – Chapter Review Questions
Chapter 7: Managing Batches and Pipelines
Technical Requirements
Trigger Batches
Handling Failed Batch Loads
Validating Batch Loads
Managing Data Pipelines in ADF or Synapse
Integration Runtimes (IRs)
Monitoring in ADF and Synapse Analytics
Scheduling Data Pipelines in ADF or Synapse
Implementing Version Control for Pipeline Artifacts
Configuring Source Control in ADF
Integrating with Azure DevOps
Integrating with External GitHub
Managing Spark Jobs in a Pipeline
Summary
Exam Readiness Drill – Chapter Review Questions
Part 4:Secure, Monitor, and Optimize Data Storage and Processing
Chapter 8: Implementing Data Security
Technical Requirements
Implementing Data Masking
Encrypting Data at Rest and in Motion
Encryption at Rest
Encryption at Rest in Azure Storage
Encryption at Rest in Azure Synapse SQL
The Always Encrypted Feature of Azure SQL
Encryption in Transit
Enabling Encryption in Transit for Azure Storage
Enabling Encryption in Transit for Azure Synapse SQL
Enabling Encryption in Transit for VPNs
Implementing Row-Level and Column-Level Security
Designing Row-Level Security (RLS)
Designing Column-Level Security
Implementing Azure Role-Based Access Control
Implementing POSIX-Like ACLs for ADLS Gen2
Resolving Conflicting Rules: RBAC and ACLs
Limitations of RBAC and ACLs
Access Keys and Shared Access Keys in Azure Storage
Implementing a Data Retention Policy
Data Life Cycle Management
Implementing Secure Endpoints: Public and Private
Implementing Resource Tokens in Azure Databricks
Loading DataFrames with Sensitive Information
Writing Encrypted Data into Tables or Parquet Files
Managing Sensitive Information
Summary
Exam Readiness Drill – Chapter Review Questions
Chapter 9: Monitoring Data Storage and Data Processing
Technical Requirements
Implementing Logging by Azure Monitor
Configuring Monitoring Services
Monitoring Stream Processing
Measuring the Performance of Data Movement
Monitoring and Updating Statistics
Creating Statistics for Synapse Dedicated Pools
Updating Statistics for Synapse Dedicated Pools
Creating Statistics for Synapse Serverless Pools
Updating Statistics for Synapse Serverless Pools
Monitoring Synapse SQL Pool Performance
Querying System Tables
Using Query Store
Monitoring Data Pipeline Performance
Measuring Query Performance
Scheduling and Monitoring Pipeline Tests
Interpreting Azure Monitor Metrics and Logs
Interpreting Azure Monitor Metrics
Interpreting Azure Monitor Logs
Implementing a Pipeline Alert Strategy
Summary
Exam Readiness Drill – Chapter Review Questions
Chapter 10: Optimizing and Troubleshooting Data Storage and Data Processing
Technical Requirements
Managing Small Files
Creating a Table
Upserting to a Table
Reading a Table
Writing to a Table
Handling Skew in Data
Handling Skew at the Storage Level
Handling Skew at the Compute Level
Handling Data Spill
Handling Data Spill in Synapse SQL
Handling Data Spill in Spark
Optimizing Resource Management
Optimizing Synapse SQL Pools
Optimizing Spark
Tuning Queries Using Indexers
Indexers in Synapse Spark Pools
Preparing Data
Creating a Hyperspace Instance
Creating Index Configurations
Creating Hyperspace Indexes
Enabling Hyperspace Indexes
Tuning Queries Using Caching
Tuning Queries in Synapse SQL
Result Set Caching
Benefits of Caching
Important Considerations
Handling Exceptions in Result Set Caching
Considerations in Result Set Caching
Optimizing Cached Results
Tuning Queries in Spark
Caching on Azure Databricks
Troubleshooting a Failed Spark Job
Troubleshooting Resource Issues
Troubleshooting Job Issues
Troubleshooting a Failed Pipeline Run
Debugging a Failed Pipeline
Troubleshooting Activities Executed in External Services
Summary
Exam Readiness Drill – Chapter Review Questions
Chapter 11: Accessing the Online Practice Resources
Index
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