AWS Certified Data Engineer Associate 2025 – Complete Hands-On Course

What You'll Learn

• Gain a comprehensive understanding of the AWS Certified Data Engineer Associate (DEA-C01) certification exam objectives.
  
  

• Build and manage scalable, efficient data pipelines using AWS tools and services.
  
  

• Architect data storage and management solutions tailored for structured, semi-structured, and unstructured data.
  
  

• Implement data processing workflows using batch and stream processing techniques.
  
  

• Operate and monitor end-to-end data pipelines with orchestrators like EventBridge, Step Functions, and Apache Airflow.
  
  

• Apply cloud data security, compliance, and governance best practices across the data lifecycle.
  
  

• Create and maintain data lakes using Amazon S3, AWS Glue, Lake Formation, and Redshift.
  
  

• Work with real-time data streams and implement transformation logic using Kinesis, EMR, containers, and AWS Lambda.

Requirements

To follow along with the course content, you will need an active AWS account. Throughout this course, you will engage in numerous hands-on labs and exercises that will help you apply the knowledge you acquire. Please note that using certain AWS services may incur minimal costs depending on usage, such as for running compute instances, storing data, or utilizing services like Amazon S3 or Lambda. AWS offers a free tier for many services, which can help you get started without significant costs, but it's essential to keep track of your usage to avoid unexpected charges. You can always monitor and manage your AWS usage and billing directly from the AWS console.

It's also highly recommended to have 2 to 3 years of practical experience in data engineering or a related field before taking this course. AWS recommends that you have at least 1 to 2 years of hands-on experience working with AWS services. This could include working with core AWS technologies such as EC2, S3, RDS, and IAM, as well as services used in data engineering like Lambda, Redshift, Kinesis, and AWS Glue. Familiarity with cloud architecture, networking, and security will also help you get the most out of the course, as the concepts covered often require an understanding of how data systems fit into larger cloud infrastructures.

While this course will guide you through the necessary AWS services and tools, it’s designed for individuals who are not complete beginners in cloud technologies or data engineering. It assumes you have some foundational knowledge of how cloud platforms work, as well as some experience with databases, programming, or data analysis. If you are new to cloud computing or data engineering, it may be helpful to first complete introductory courses on AWS or data engineering concepts before tackling this one.

This course will challenge you, but it will also provide you with the tools and support needed to master AWS data engineering workflows. You’ll be introduced to a variety of AWS services and architectures used in real-world data processing scenarios, from batch and real-time data handling to security and optimization. By the end of the course, you should feel confident navigating AWS’s broad data ecosystem and be well-prepared to pass the AWS Certified Data Engineer Associate (DEA-C01) exam.

Course Description

New for 2025: This course now includes a comprehensive practice exam designed to match the structure, format, and level of difficulty of the real AWS Certified Data Engineer Associate (DEA-C01) exam. This added feature ensures you get an authentic testing experience, helping you pinpoint areas of strength and those requiring further study.

The AWS Certified Data Engineer Associate certification is one of the most technically demanding certifications available at the associate level. It covers a wide spectrum of topics essential to the role of a data engineer, from data ingestion and transformation to orchestration and analytics. Additionally, it delves into advanced subjects like machine learning integration, security practices, and cloud architecture best practices—making it a robust certification for anyone looking to deepen their expertise in data engineering on AWS.

This course is built not just to help you pass the exam, but also to ensure you gain a solid foundation in data engineering concepts as they apply to the AWS platform. The exam itself requires a strategic, hands-on approach to mastering a wide range of tools and services. This course provides that strategy, breaking down each subject into easy-to-digest modules, reinforced with hands-on labs and exercises. Our goal is to transform your theoretical knowledge into practical, real-world experience.

The curriculum is meticulously designed to cover all the domains of the exam, but it also goes beyond that, helping you build the expertise necessary to implement data engineering solutions in AWS. From the basics of setting up data pipelines to implementing complex workflows that scale, this course touches on everything you need to know.

You will learn how to design, build, and operate data systems using key AWS services, such as Amazon Kinesis, AWS Lambda, Redshift, S3, Glue, Athena, and more. The course provides deep insights into how to integrate and orchestrate these services to form efficient data engineering pipelines. You'll learn how to handle real-time data, process batch jobs, manage security policies, automate workflows, and leverage machine learning tools at scale—all within the AWS environment.

Led by two industry veterans—Stéphane Maarek and Frank Kane—this course brings together over a decade of combined expertise. Stéphane offers an expert-level understanding of AWS, specifically in the context of cloud certifications, while Frank shares insights gained from his nine years at Amazon, where he worked on large-scale, mission-critical data systems. Together, they deliver a unique blend of practical knowledge, exam-focused strategies, and real-world scenarios that make this course one of the most comprehensive on the market.

The course also includes various real-world use cases and demonstrations that show you how to architect complex data systems for modern cloud environments. Throughout the course, you’ll encounter hands-on labs where you can apply your skills and see the practical results of your learning. These labs cover the full range of data engineering activities, from ingesting and processing data, to setting up data lakes and warehouses, to applying machine learning algorithms.

In addition to helping you prepare for the DEA-C01 exam, this course equips you with the technical acumen and problem-solving skills needed to be successful as an AWS data engineer. It’s ideal for individuals who want to expand their AWS knowledge, improve their cloud architecture skills, and pursue roles in data engineering.

Whether you're looking to pass the exam or become proficient in real-world AWS data engineering tasks, this course is your complete guide. It’s an investment in both your certification and your career in cloud data engineering.

Course Highlights

This course includes detailed discussions, real-world case studies, and hands-on exercises for each of the major topic areas covered in the AWS Certified Data Engineer - Associate (DEA-C01) certification exam. The course is structured to provide both theoretical understanding and practical skills, ensuring you're not just ready for the exam, but also prepared for real-world AWS data engineering challenges. Here's a breakdown of the key highlights:

1. Comprehensive Coverage of Key AWS Services
   We dive into essential AWS services required for data engineering, including Amazon S3, Kinesis, Glue, Redshift, Lambda, and more. You'll learn how to use these services to create data pipelines, store and manage data, and automate data processing workflows. The focus will be on using these services in tandem to create end-to-end data engineering solutions.
   
   

2. Hands-On Labs and Demos
   Each module is designed to give you hands-on experience with AWS services. You'll be working on real-world problems, such as building data lakes with Amazon S3, processing streaming data with Kinesis, creating serverless pipelines with Lambda, and designing analytics workflows with AWS Glue. By completing these labs, you'll reinforce your understanding of how these services are used in practice.
   
   

3. Data Pipeline Design and Orchestration
   Data pipelines are at the heart of data engineering. The course covers the design, deployment, and orchestration of data pipelines using tools like AWS Step Functions, Amazon EventBridge, and Apache Airflow (via MWAA). You’ll learn how to automate workflows, manage dependencies, and ensure that data moves seamlessly between different AWS services.
   
   

4. Real-Time Data Processing
   Learn how to process both batch and real-time data using services like Amazon Kinesis, Elastic MapReduce (EMR), and AWS Lambda. We will cover various methods for transforming and processing large volumes of data in real-time, giving you the skills to work with live, streaming data systems.
   
   

5. Data Security, Governance, and Privacy Best Practices
   Data security and privacy are critical when working with sensitive data. This course will guide you through best practices for securing data in AWS using services like IAM, KMS, Macie, and Secrets Manager. You’ll also learn how to manage data access, apply encryption, and implement privacy policies to meet regulatory requirements.
   
   

6. Building Data Lakes and Data Warehouses
   In-depth coverage of how to build scalable data lakes with S3, AWS Glue, and Lake Formation, and how to implement data warehouses using Redshift and Redshift Spectrum. These systems are designed to handle vast amounts of data, and you'll learn how to design them for performance, scalability, and cost efficiency.
   
   

7. Integrating Analytics and Machine Learning
   As part of modern data engineering workflows, machine learning plays a critical role in data processing. This course will show you how to integrate AWS SageMaker for building and deploying machine learning models, and how to incorporate Amazon Athena and Amazon OpenSearch for querying and analyzing large datasets.
   
   

8. Performance Tuning and Optimization
   You'll gain insights into performance tuning for data processing workflows. Whether it's optimizing Redshift queries, DynamoDB performance, or EMR jobs, the course will cover various techniques to ensure your systems are running at peak performance. We will also look into cost optimization strategies, such as choosing the right instance types, utilizing auto-scaling, and reducing data storage costs.
   
   

9. Full-Length Practice Exam
   A crucial part of your exam preparation, the practice exam simulates the structure and difficulty of the real DEA-C01 exam. You'll receive feedback on your performance, enabling you to identify weak areas and focus your study efforts. This full-length practice test will give you the confidence to walk into the exam day fully prepared.
   
   

10. Exam Tips and Test-Taking Strategies
    In addition to covering technical material, we provide expert advice on exam strategy. Learn how to approach multiple-choice questions, manage your time during the exam, and use elimination techniques effectively. These strategies will boost your chances of passing the exam and help you work more efficiently through the test.

By the end of the course, you will have a deep understanding of the core AWS services for data engineering, the skills to design and operate data systems, and the confidence to sit for the AWS Certified Data Engineer - Associate (DEA-C01) exam.

Data Ingestion and Real-Time Processing

Learn how to stream data using Amazon Kinesis, including Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics. Understand when and how to implement Amazon Managed Streaming for Apache Kafka (MSK) for high-throughput messaging scenarios.

Message Queueing and Event Handling

Explore Amazon Simple Queue Service (SQS) and Simple Notification Service (SNS) to manage asynchronous workflows. Learn how to buffer workloads and handle spikes in data volume using decoupled systems.

Data Orchestration and Workflow Automation

Set up and manage workflows using AWS Step Functions, Amazon EventBridge, AppFlow, and Amazon Managed Workflows for Apache Airflow (MWAA). Understand how to coordinate microservices and automate pipeline stages with these services.

Data Transfer and Migration

Master tools such as AWS DataSync and the AWS Database Migration Service (DMS) to move structured and unstructured data into AWS at scale. Work with AWS Snow Family and Transfer Family to handle large offline or FTP-based migrations securely and efficiently.

Storage Solutions for Big Data

Dive into Amazon S3 for object storage and learn how to design data lakes that are scalable, cost-effective, and queryable. Learn about managing storage classes and life cycle policies to optimize cost and performance. Work with S3-compatible tools like Athena and Lake Formation for building modern data lake architectures.

NoSQL and Transactional Databases

Discover when to use DynamoDB, DocumentDB, Amazon Keyspaces (for Cassandra), and MemoryDB. Learn how to design highly available and scalable NoSQL solutions suitable for various real-time and transactional use cases.

Serverless Data Processing

Understand how to use AWS Lambda functions to integrate various stages of a data pipeline. Learn best practices for event-driven data processing and handling various data formats and sources dynamically.

Data Transformation and ETL

Utilize AWS Glue and Glue DataBrew to build and run extract, transform, and load (ETL) jobs. Automate data transformation and cleanup processes for both batch and real-time data pipelines. Learn schema detection, versioning, and partitioning to prepare data for analytics.

Big Data Processing with Apache Spark

Run distributed computing jobs using Amazon EMR, leveraging frameworks like Apache Spark for large-scale data transformations. Learn how to configure clusters and process massive datasets with parallel processing.

Machine Learning Integration

Integrate your data pipelines with Amazon SageMaker to apply machine learning models at scale. Explore use cases where data engineering and data science converge to deliver predictive insights and business value.

Search and Analytics

Set up Amazon OpenSearch to perform full-text search and real-time analytics on massive datasets. Understand indexing strategies and visualizations. Query large volumes of log data or semi-structured data efficiently.

SQL-Based Analytics and Data Warehousing

Query data directly from S3 using Amazon Athena. Build highly scalable, columnar data warehouses with Amazon Redshift and extend its capabilities using Redshift Spectrum. Learn to integrate Redshift with other AWS analytics tools.

Relational Databases in Data Pipelines

Use Amazon RDS (MySQL, PostgreSQL, SQL Server) to store, retrieve, and join structured data with broader big data platforms. Understand replication, sharding, and high availability in relational workloads.

Security, Compliance, and Monitoring

Implement data encryption using AWS Key Management Service (KMS). Explore how AWS Macie can detect sensitive data. Use Secrets Manager to securely manage access credentials. Apply role-based access using Identity and Access Management (IAM).

Leverage services like AWS CloudTrail, CloudWatch, and AWS Config to monitor and audit your infrastructure. Automate compliance and ensure your systems meet industry standards.

Practice Exams, Labs, and Resources

The course includes an entire section dedicated to exam preparation. You’ll get access to:

• A full-length, timed practice exam modeled after the real certification test
  
  

• Dozens of practice questions and scenario-based exercises
  
  

• Hands-on demonstrations for nearly every AWS service discussed
  
  

• Real-world project examples and architecture walkthroughs
  
  

• Study guides and downloadable resources to support your review

These resources ensure that you not only understand the theory but also gain hands-on experience working with AWS.

Who This Course is For

This course is perfect for:

• Data engineers aiming to validate their AWS expertise through certification
  
  

• Cloud professionals seeking to expand their skillset in data architecture and analytics
  
  

• Developers and system architects looking to move into data engineering roles
  
  

• Anyone with a solid understanding of AWS services who wants to master the data domain

This course assumes familiarity with cloud computing and some experience with AWS. If you're new to the platform or data engineering in general, consider taking foundational courses first.

Meet Your Instructors

Stéphane Maarek is a seasoned AWS trainer with millions of students worldwide. He has built a reputation for breaking down complex AWS topics into understandable lessons, and his courses are known for their clarity and depth. Stéphane is deeply invested in helping learners pass AWS certifications and apply their skills in real-world roles.

Frank Kane brings a wealth of experience from his nine-year tenure at Amazon, where he built scalable data solutions used across the company. He has taught over one million students on topics such as big data, machine learning, and cloud architecture. Frank's ability to simplify tough technical content makes him a favorite among learners worldwide.

Additional Benefits

When you enroll in this course, you’ll also receive several exclusive benefits designed to enhance your learning experience and ensure you have everything you need to succeed:

1. Lifetime Access to All Course Content and Updates
   Once enrolled, you’ll have lifetime access to the entire course, including any updates, new modules, or additional content added in the future. This means you can revisit the material anytime you need to refresh your knowledge, ensuring your AWS skills stay current as cloud technologies evolve.
   
   

2. Downloadable Study Materials and Cheat Sheets
   To make your study process easier, you’ll have access to downloadable study materials and cheat sheets. These resources are perfect for quick reference and help you grasp complex concepts more efficiently. Whether you’re preparing for the exam or working on a project, these materials will serve as valuable tools to support your learning.
   
   

3. A Dedicated Q&A Section for Active Engagement
   Learning is more effective when you can ask questions and engage with others. In this course, you’ll have access to a dedicated Q&A section where instructors and fellow learners actively interact. If you’re ever stuck or need clarification on a topic, you can ask questions directly, and either the instructor or your peers will offer support. This collaborative learning environment ensures you're never left behind.
   
   

4. An Official Udemy Certificate of Completion
   Upon successfully completing the course, you’ll receive an official Udemy certificate of completion. This certificate serves as proof of your dedication and newly acquired skills, which you can showcase to employers or include in your professional portfolio.
   
   

5. A 30-Day Money-Back Guarantee
   We’re confident that this course will provide immense value, but if for any reason you’re not satisfied, Udemy offers a 30-day money-back guarantee. If you find that the course isn’t right for you, simply let us know, and you can receive a full refund with no questions asked.

Enroll Today

Are you ready to take your career to the next level and become an AWS Certified Data Engineer? This course is designed to equip you with everything you need to succeed in the AWS Certified Data Engineer - Associate (DEA-C01) exam, as well as provide you with the practical knowledge to build advanced data engineering solutions in AWS.

By enrolling in this course, you’ll gain access to comprehensive training, hands-on labs, and real-world case studies that will deepen your understanding of AWS data engineering concepts and best practices. You'll walk through every step of the certification process, from designing scalable data pipelines to implementing advanced security measures and performing complex analytics—all while mastering the AWS services that power these processes.

This course isn’t just about passing the exam; it’s about preparing you to tackle real-world challenges in AWS data engineering. Whether you're looking to advance in your current role or pursue new career opportunities, the skills you develop here will be essential for building cutting-edge data systems in the cloud.

Why wait? Enroll today and start your journey toward becoming a certified AWS Data Engineer. The tools, knowledge, and confidence you need to succeed are right at your fingertips.

Don’t miss this opportunity to:

• Master AWS data engineering concepts with hands-on exercises
  
  

• Gain practical experience with AWS services like S3, Redshift, Glue, Kinesis, and more
  
  

• Build data pipelines, data lakes, and data warehouses at scale
  
  

• Optimize performance and security for large-scale data systems
  
  

• Pass the DEA-C01 exam with confidence and boost your career

Take the first step toward mastering AWS data engineering—enroll now and unlock your future success!