AWS Certified Data Engineer Study Guide : Associate (DEA-C01) Exam.

Format:

Book

Author/Creator:

Humair, Syed.

Contributor:

Gumbo, Chenjerai.

Gatt, Adam.

Abbasi, Asif.

Nair, Lakshmi.

Series:

Sybex Study Guide Series

Language:

English

Subjects (All):

Electronic data processing personnel--Certification--Examinations--Study guides.

Electronic data processing personnel.

Computer networks--Management--Examinations--Study guides.

Computer networks.

Computer systems--Examinations--Study guides.

Computer systems.

Cloud computing--Examinations--Study guides.

Cloud computing.

Computer technicians--Certification.

Computer technicians.

Physical Description:

1 online resource (659 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2025.

Summary:

Your complete Guide to preparing for the AWS® Certified Data Engineer: Associate exam The AWS® Certified Data Engineer Study Guide is your one-stop resource for complete coverage of the challenging DEA-C01 Associate exam. This Sybex Study Guide covers 100% of the DEA-C01 objectives. Prepare for the exam faster and smarter with Sybex thanks to accurate content including, an assessment test that validates and measures exam readiness, real-world examples and scenarios, practical exercises, and challenging chapter review questions. Reinforce and retain what you've learned with the Sybex online learning environment and test bank, accessible across multiple devices. Get ready for the AWS Certified Data Engineer exam - quickly and efficiently - with Sybex. Coverage of 100% of all exam objectives in this Study Guide means you'll be ready for: Data Ingestion and Transformation Data Store Management Data Operations and Support Data Security and Governance ABOUT THE AWS DATA ENGINEER - ASSOCIATE CERTIFICATION The AWS Data Engineer - Associate certification validates skills and knowledge in core data-related Amazon Web Services. It recognizes your ability to implement data pipelines and to monitor, troubleshoot, and optimize cost and performance issues in accordance with best practices Interactive learning environment Take your exam prep to the next level with Sybex's superior interactive online study tools. To access our learning environment, simply visit www.wiley.com/go/sybextestprep, register your book to receive your unique PIN, and instantly gain one year of FREE access after activation to: • Interactive test bank with 5 practice exams to help you identify areas where further review is needed. Get more than 90% of the answers correct, and you're ready to take the certification exam. • 100 electronic flashcards to reinforce learning and last-minute prep before the exam • Comprehensive glossary in PDF format gives you instant access to the key terms so you are fully prepared.

Contents:

Cover

Title Page

Copyright

Acknowledgments

About the Authors

Contents at a Glance

Contents

Foreword

Introduction

The AWS Certified Data Engineering Associate Certification

The Purpose of This Book

The AWS Certified Data Engineer - Associate Exam

Study Guide Features

Interactive Online Learning Environment and TestBank

AWS Certified Data Engineer - Associate Exam (DEA-C01) Objectives

How to Contact the Publisher

Assessment Test

Answers to Assessment Test

Chapter 1 Streaming and Batch Data Ingestion

The Evolution of Application Architectures and Data Stores

Introduction to the Modern Data Architecture

Introduction to Data Ingestion

Data Generation

Understanding Data Sources and Storage

Ingestion Patterns and AWS Services

Data Ingestion

Streaming Ingestion

Amazon Kinesis Introduction

Amazon Kinesis Data Streams

Amazon Data Firehose

Amazon Managed Service for Apache Flink

Amazon Managed Streaming for Apache Kafka

Comparison of Streaming Services

Batch Ingestion

AWS Glue

Amazon Data Migration Service

AWS DataSync

Large-Scale Data Transfer Solutions

AWS Direct Connect

Summary

Exam Essentials

Review Questions

Chapter 2 Building Automated Data Pipelines

Introduction to Automated Data Pipelines

Data Pipeline Orchestration

AWS Step Functions

Amazon Managed Workflows for Apache Airflow

AWS Glue Workflows for Data Pipeline Orchestration

When to Use What (AWS Step Functions, Amazon MWAA, or AWS Glue)

Best Practices for Data Pipelines Orchestration

Supporting AWS Services for Enhanced Orchestration

AWS Lambda

Amazon EventBridge

Notification and Queuing Services

Applying Programming Concepts

CI/CD

Using AWS SAM for Serverless Data Pipeline Deployment.

SQL Queries in Data Pipeline Orchestration

Infrastructure as Code for Repeatable Data Pipeline Deployments

Data Structures and Algorithms

Optimizing Code to Reduce Runtime for Data Ingestion and Transformation

Structuring SQL Queries to Meet Data Pipeline Requirements

Using Git Commands for Data Pipeline Development

Testing and Debugging Techniques

Logging, Monitoring, and Auditing for Data Pipeline Orchestration

Extracting Logs for Audits

Deploying Logging and Monitoring Solutions

Using Notifications for Alerts

Case Studies and Real-World Examples

Case Study 1: Batch Data Processing Pipeline for Financial Transactions

Case Study 2: Real-Time Streaming Data Pipeline for IoT Sensor Data

Case Study 3: Data Lake Ingestion and Processing Pipeline

Case Study 4: Machine Learning Pipeline for Image Classification

Case Study 5: ETL Pipeline for Data Warehouse Loading

Chapter 3 Data Transformation

Introduction to Data Integration

Data Transformation

Amazon Athena

Amazon Redshift

Amazon Elastic MapReduce

Stream Processing

Introduction to Containers

Amazon ECS

Amazon Elastic Container Registry

Amazon Elastic Kubernetes Service

Optimizing Container Usage

API-Driven Data Pipelines on AWS

Amazon API Gateway

Introduction to Data Quality

Data Quality Challenges to Overcome

AWS Glue Data Quality

Data Quality Definition Language

Data Quality in Transit

Alerts and Monitoring

Extended Use Cases

Chapter 4 Storage Services

Introduction to Data Stores

Storage Platforms

Object Storage

Block Storage

Cloud File Storage

Comparison of Storage Types

Data Storage Formats.

Storage Services and Configurations for Specific Performance Demands

Amazon Simple Storage Service

S3 Storage Classes

Amazon S3 Store Management

Access Management and Security

Data Processing

Storage Logging and Monitoring

Analytics and Insights

Strong Consistency

Accessing S3

Paying for Amazon S3

Getting Started with S3 Buckets

Amazon Elastic Block Storage

EBS Volume Types

Data Protection

Amazon Elastic File System

Amazon EFS Features

Amazon File Cache

Amazon File Cache Features

Amazon FSx

Implementing the Appropriate Storage Services for Specific Cost and Performance Requirements

Aligning Data Storage with Data Migration Requirements

Determining the Appropriate Storage Solution for Specific Access Patterns

Managing the Lifecycle of Data

Legal and Compliance Requirements

Cost Optimization

Performance Improvement

Data Security

Disaster Recovery and Business Continuity

Selecting the Appropriate Storage Solutions for Hot and Cold Data

Optimizing Storage Costs Based on the Data Lifecycle

Deleting Data to Meet Business and Legal Requirements

Implementing Data Retention Policies and Archiving Strategies

Protecting Data with Appropriate Resiliency and Availability

Chapter 5 Databases and Data Warehouses on AWS

What Is a Data Warehouse?

Redshift Architecture

Data API

Data Distribution

Data Sorting

Vacuum

Compression Encoding

SQL Query Optimization

Workload Management (WLM)

Data Sharing

Cluster Resizing

Loading Data into Redshift

Unloading Data from Redshift

Transforming Data in Redshift

Materialized Views

Data Modeling on Redshift

Data Security in Redshift

Amazon DynamoDB

What Is a NoSQL Database?.

DynamoDB Main Concepts

DynamoDB Read Consistency

Global Tables

DynamoDB Read/Write Capacity

DynamoDB Accelerator

Amazon Relational Database Service

RDS Scalability

RDS Availability

Amazon Aurora

Amazon Neptune

What Is a Graph Database?

Amazon DocumentDB (with MongoDB Compatibility)

What Is a Document Database?

Amazon DocumentDB Architecture

Connecting to Amazon DocumentDB

Amazon MemoryDB for Redis

What Is Redis?

What Is Amazon MemoryDB?

Amazon Keyspaces (for Apache Cassandra)

What Is Apache Cassandra?

What Is a Wide-Column Store Database?

What Is Amazon Keyspaces?

AWS Database Comparison

Chapter 6 Data Catalogs

Data Catalogs

Benefits of Data Catalogs

AWS Glue Data Catalog

Data Quality at Rest

Rule Recommendations

Data Quality Rules

Chapter 7 Visualizing Your Data

Introduction to Data Visualization

Types of Data Visualizations

Bar Charts

Histograms

Line Charts

Scatter Plots

Pie and Donut Charts

Heatmaps

Treemaps

Geospatial Maps

Principles of Effective Data Visualization

Trade-offs Between Provisioned and Serverless Services

AWS Services for Data Analysis and Visualization

Visualizing Data with Amazon QuickSight

AWS Glue DataBrew

Amazon SageMaker Data Wrangler

Advanced SQL Techniques for Data Analysis

The Evolution of SQL in Modern Data Platforms

Complex Join Operations: Beyond Simple Table Combinations

Window Functions: The Analytical Powerhouse

The Art of Subqueries and Common Table Expressions

Pivot and Unpivot: Transforming Data Perspectives

The Future of SQL Analysis

Data Cleansing and Preparation

Common Data Quality Issues in Modern Analytics.

Data Cleansing and Transformation Techniques

Data Aggregation and Transformation Techniques

Best Practices for Data Analysis

Chapter 8 Monitoring and Auditing Data

How to Log Application Data

Logging Best Practices

Logging Levels

Cautions and Exclusions

Special Data Types

Access and Change Management

How to Log Access to AWS Services

AWS CloudTrail

Amazon CloudWatch

CloudWatch Logs

VPC Flow Logs

AWS X-Ray

Amazon Macie

Analyzing Logs Using AWS Services

Chapter 9 Maintaining and Troubleshooting Data Operations

Introduction to Automating Data Processing Using AWS Services

Maintaining and Troubleshooting Data Processing

API Calls for Data Processing

Services That Accept Scripting

Orchestrating Data Pipelines

Troubleshooting Amazon Managed Workflows

Airflow Logs

Audit Logs

Monitoring and Alarms

Using AWS Services for Data Processing

Consuming and Maintaining Data APIs

Amazon Redshift Data API

Calling the Data API and Available Commands

Example Statements and Their Output

Considerations When Using the Redshift Data API

Amazon Redshift Data API Use Case

Monitoring the Redshift Data API

Troubleshooting Common Issues for the Redshift Data API

Using Lambda for Data Processing

Chapter 10 Authentication and Authorization

Introduction to Authentication

API Endpoints

AWS Identity and Acess Management

IAM Users and Groups

IAM Roles

Access Keys and Credentials

Multi-Factor Authentication

AWS Security Token Service

Assuming Roles

Federation

Amazon Cognito

Kerberos-Based Authentication

Data Services Authentication Mechanisms

Authentication in the Data Engineering Exam.

Introduction to Authorization.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9781394286591

1394286597

9781394286607

1394286600

OCLC:

1507845637