AWS Data Engineer Associate Certification 2025 – Practical Training

The demand for skilled data engineers is rapidly growing as organizations continue to build, scale, and optimize data-driven systems. With the explosion of big data, cloud services, and advanced analytics, businesses now depend heavily on engineers who can design, maintain, and optimize large-scale data pipelines. Amazon Web Services (AWS) offers one of the most respected and recognized certifications in this field: the AWS Certified Data Engineer Associate (DEA-C01).

This course has been created to help you not only prepare for the AWS Data Engineer Associate exam but also to build hands-on experience with AWS tools and services that are widely used in real-world data engineering projects. Whether you are already an experienced technologist or someone looking to specialize further in data engineering, this course is structured to guide you step by step.

The program combines practical, hands-on labs with in-depth theoretical knowledge, ensuring you understand both how AWS services work and how they fit into broader data architectures. Along with practice questions and a full-length exam simulation, this course equips you with the knowledge, strategies, and confidence needed to succeed.

Why Data Engineering Matters Today

Data has become the lifeblood of modern organizations. Every industry, from healthcare to finance, relies on data pipelines to collect, process, and analyze information for better decision-making. However, managing large-scale data is complex, involving challenges in ingestion, transformation, orchestration, and governance.

Data engineers bridge this complexity by designing systems that can efficiently move, clean, and prepare data for downstream analytics and machine learning. On AWS, this means working with services such as S3 for data storage, Glue for transformation, Redshift for warehousing, Athena for query execution, and Kinesis for real-time streaming.

By earning the AWS Certified Data Engineer Associate credential, you prove your ability to work with these technologies at scale. This makes you highly valuable to employers seeking professionals who can implement secure, cost-efficient, and scalable data solutions.

What You Will Learn

This course covers everything required for the AWS Certified Data Engineer Associate exam, while also going far beyond by providing hands-on demonstrations and real-world use cases. Some of the core learning outcomes include:

• How to maximize your chances of passing the AWS DEA-C01 exam through structured preparation and hands-on practice
  
  

• Designing and implementing pipelines to ingest, store, and transform structured and unstructured data
  
  

• Choosing the right data storage services,, such as S3, DynamoDB, Redshift, or R,,DS depending on business requirements
  
  

• Creating data models, schemas, and defining data lifecycles for efficiency and compliance
  
  

• Building orchestration flows using EventBridge, Airflow, Step Functions, and AppFlow.
  
  

• Applying best practices for security, governance, and privacy across your data pipelines
  
  

• Constructing data lakes with services such as S3, Glue, Redshift Spectrum, and Lake Formation
  
  

• Processing both batch and streaming data using tools like Kinesis, EMR, Lambda, and containerized environments
  
  

• Understanding cost optimization strategies while handling petabyte-scale data

Requirements to Follow the Course

To get the most out of this course, several important requirements and recommendations will help you succeed. The first and most practical need is an active AWS account. Since this is a hands-on course, you will be building pipelines, setting up services, and experimenting directly with AWS tools. Without an account, you would miss out on the practical experience that is critical for both the exam and real-world application. While AWS offers a free tier for many services, certain exercises will go beyond the free usage limits, which means you should expect to spend a few dollars as you complete the labs. This cost is generally minimal, but it is important to keep in mind so you can budget accordingly and avoid unexpected charges by monitoring your usage. Having your own AWS account also gives you the freedom to explore beyond the scope of the course, which can deepen your learning and help you experiment with more advanced use cases.

A second recommendation is having a basic understanding of core AWS services such as S3, IAM, and EC2. Amazon S3 is central to almost every data engineering workflow on AWS because it provides reliable, scalable storage for structured and unstructured data. Understanding how to create buckets, manage permissions, and optimize costs will allow you to move quickly when the course introduces advanced topics like building data lakes or integrating with Glue and Athena. IAM, or Identity and Access Management, is equally important because it governs security and access control. Data engineering often involves connecting multiple services, and ensuring those services have the right permissions is critical to both functionality and security. EC2, Amazon’s compute service, is another building block that frequently appears in labs and scenarios. Having some familiarity with spinning up instances, configuring networks, and understanding the basics of compute capacity will provide a foundation for tackling larger architectures that involve EMR clusters or containerized environments. While you do not need deep expertise in these services before starting the course, some prior exposure will reduce the learning curve and help you progress more smoothly.

AWS itself recommends that candidates attempting the Data Engineer Associate exam have two to three years of experience in data engineering and at least one to two years of hands-on AWS service usage. This recommendation is not meant to exclude beginners but to highlight the exam’s difficulty level. Unlike entry-level certifications, this exam requires a broad and deep understanding of how AWS services interact and how data flows across complex systems. If you already have experience designing ETL pipelines, working with data warehouses, or processing large datasets, you will find that the material connects naturally to your background. For those with limited experience, the course will still be valuable, but you may need to spend extra time revisiting concepts, repeating labs, and practicing in your AWS account to gain confidence. The good news is that this course has been structured with clear explanations and practical demonstrations, so even learners who are not fully aligned with AWS’s recommended experience level can build up the skills needed to succeed.

Finally, familiarity with SQL, Python, or other general programming concepts will provide a strong advantage throughout the course. SQL is the language of querying and is widely used in services such as Athena, Redshift, and RDS. Knowing how to write queries, filter data, and perform aggregations will help you make the most of lessons on analytics and warehousing. Python is commonly used for scripting, automation, and data transformation tasks on AWS, whether through Lambda functions, Glue jobs, or EMR notebooks. Even if you are not an advanced programmer, understanding loops, functions, and variables will make it easier to follow along and customize code examples. For learners without this background, the course provides guidance and context so you will not be left behind, but the more comfortable you are with these tools, the faster you will be able to absorb the material.

Exam Overview

The AWS Certified Data Engineer Associate exam, known as DEA-C01, evaluates your ability to work with large data systems in AWS. It is recognized as one of the more challenging associate-level exams, due to its wide coverage of AWS services and data engineering practices.

The exam tests your knowledge across several domains, including:

• Data ingestion and transformation
  
  

• Data storage and lifecycle management
  
  

• Data security, governance, and compliance
  
  

• Orchestration and workflow automation
  
  

• Optimization and troubleshooting
  
  

Preparing for this exam requires not only understanding individual AWS services but also how they interconnect to form complete solutions. This course has been structured with that in mind.

Key AWS Services Covered

Data Ingestion and Streaming

• Amazon Kinesis for real-time ingestion
  
  

• Managed Streaming for Apache Kafka (MSK)
  
  

• Simple Queue Service (SQS) for message buffering
  
  

• AWS DataSync and Transfer Family for migrating data from on-premises to the cloud
  
  

Data Storage and Data Lakes

• Amazon S3 for storing large-scale unstructured data
  
  

• Redshift and Redshift Spectrum for warehousing and analytics
  
  

• DynamoDB, DocumentDB, and Keyspaces for transactional and NoSQL workloads
  
  

• Lake Formation for managing permissions in data lakes
  
  

Data Transformation and Preparation

• AWS Glue and Glue DataBrew for ETL (Extract, Transform, Load)
  
  

• Elastic MapReduce (EMR) with Apache Spark for big data processing
  
  

• AWS Lambda for serverless transformations
  
  

Orchestration and Workflow Automation

• Amazon EventBridge for event-driven automation
  
  

• AWS Step Functions for coordinating distributed workflows
  
  

• Managed Workflows for Apache Airflow (MWAA)
  
  

• Amazon AppFlow for SaaS integrations
  
  

Security and Governance

• AWS IAM for access control
  
  

• KMS for encryption management
  
  

• Macie for sensitive data discovery
  
  

• Secrets Manager for credential storage
  
  

• CloudTrail and AWS Config for governance and auditing
  
  

Analytics and Querying

• Amazon Athena for querying S3 data with SQL
  
  

• Amazon OpenSearch Service for indexing and analyzing large datasets
  
  

• Redshift for business intelligence and reporting
  
  

Machine Learning and AI

• Amazon SageMaker for building, training, and deploying ML models
  
  

• Integrating ML workflows with data pipelines for predictive analytics

Hands-On Practice

Theory alone is not enough for success in either the exam or in real-world projects. This course includes step-by-step hands-on labs where you will:

• Build a real-time streaming pipeline with Kinesis
  
  

• Create an S3-based data lake and query it with Athena.
  
  

• Implement an ETL pipeline with AWS Glue.e
  
  

• Use Step Functions to orchestrate workflows across multiple services

• Deploy Spark jobs on EMR to process large datasets. Set up permissions and governance controls using Lake Formation.
  
  

• Automate data ingestion using AppFlow and EventBridge
  
  

Each of these labs is designed to reinforce concepts by applying them practically, so you walk away with real skills.

Practice Exam and Test-Taking Strategies

In addition to detailed lessons and labs, this course provides:

• A full-length practice exam mirroring the actual AWS DEA-C01 exam
  
  

• Additional practice questions to reinforce your learning
  
  

• Tips and strategies for managing time, eliminating wrong answers, and recognizing common question patterns
  
  

By simulating the exam environment, you will build confidence and reduce test-day anxiety.

Course Structure

The course is divided into modules that follow a logical progression, starting from core foundations and moving towards more advanced and complex topics:

1. Introduction to AWS Data Engineering
   
   

2. Data Ingestion with AWS Services
   
   

3. Data Storage Solutions and Data Lakes
   
   

4. Data Transformation and ETL Workflows
   
   

5. Workflow Orchestration and Automation
   
   

6. Batch and Streaming Data Processing
   
   

7. Security, Governance, and Compliance
   
   

8. Analytics, Querying, and Warehousing
   
   

9. Machine Learning Integration with Data Pipelines
   
   

10. Hands-On Projects and Labs
    
    

11. Practice Exam and Final Preparation

Who Should Take This Course

This course is suitable for:

• Data engineers who want to validate their expertise with AWS certification
  
  

• Software developers and architects seeking to expand their knowledge of AWS data services
  
  

• Business intelligence and analytics professionals transitioning into cloud data engineering ro.les
  
  

• IT professionals with AWS experience looking to specialize in data solutions.
  
  

• Anyone preparing for the AWS Certified Data Engineer Associate exam

Instructors

This course is taught by two highly respected instructors whose combined expertise in AWS, big data, and professional training brings immense value to every learner who enrolls. Their teaching styles complement one another, creating a balance between deep technical understanding and practical real-world applications. The collaboration of Stéphane Maarek and Frank Kane ensures that students not only prepare for the AWS Certified Data Engineer Associate exam but also gain long-term skills that can be directly applied in professional settings.

Stéphane Maarek is widely recognized as one of the leading trainers in the field of AWS certifications. Over the years, he has taught millions of students across the globe and has developed a reputation for making complex topics understandable. His background in cloud computing and his ability to break down difficult concepts into simple, digestible lessons allow learners from diverse backgrounds to progress with confidence. Whether you are a beginner seeking a structured entry point into AWS data engineering or an experienced professional aiming to refine your knowledge, his approach ensures that you can follow along and grasp the essential ideas without being overwhelmed. Stéphane’s teaching philosophy revolves around building strong foundations and then layering advanced concepts gradually, which helps learners develop both confidence and depth of knowledge.

Frank Kane brings another dimension to the course with his extensive industry experience. He spent nine years at Amazon in senior engineering and management roles, where he worked on some of the largest and most complex data problems in the world. His hands-on involvement in building large-scale data pipelines, managing data analytics platforms, and working with cutting-edge technologies gives him a perspective few instructors can offer. Frank has also taught over a million learners worldwide and has become known for his ability to connect theory with practice. Students value the way he shares insights from his time at Amazon, offering lessons that are not only technically correct but also grounded in real challenges faced by data engineers in industry.

Together, Stéphane and Frank create a powerful combination. Stéphane ensures that every technical concept is explained clearly and systematically, while Frank enriches the material with real-world case studies, anecdotes, and lessons learned from his career. This dual approach means the course does not just teach you what you need to know to pass an exam but also shows you how these tools are applied in professional environments. Learners benefit from both structured teaching and practical wisdom, which makes the experience more engaging and meaningful.

Their combined teaching also emphasizes balance between theory and practice. AWS services can sometimes feel abstract when presented only in documentation, but the instructors make sure that learners see how services interact, how they fit into end-to-end pipelines, and how to make informed choices between different tools. By walking through practical labs, live demonstrations, and carefully chosen examples, they prepare you not only for certification but also for day-to-day tasks in a data engineering role.

Another important aspect of their instruction is their responsiveness and engagement with students. Both instructors have a history of maintaining active communication through Q&A sections and community forums. They understand the challenges learners face when tackling complex technologies and make themselves available to provide guidance, answer questions, and encourage students along the way. This level of support is rare in large-scale online courses and adds significant value for those who may feel uncertain or stuck during their studies.

What You Get

By enrolling in this course, you gain much more than access to lessons. You receive lifetime access to all course materials and updates, which means that as AWS continues to evolve and new features are introduced, you will always stay up to date without needing to re-enroll. This allows you to revisit lessons at any time, refresh your knowledge for projects or interviews, and ensure that your skills remain relevant for years. Alongside the main content, you also receive a full-length practice exam that is carefully designed to reflect the format, style, and difficulty of the actual AWS Certified Data Engineer Associate exam. Taking this practice exam will help you understand your strengths and weaknesses, highlight areas that need more attention, and reduce anxiety on the real test day by giving you the confidence of familiarity with the exam environment.

Another key benefit is the inclusion of hands-on labs and demonstrations. Rather than just reading or hearing about AWS services, you will build pipelines, configure orchestration tools, and manage data in real AWS environments. This practical experience is invaluable, since employers place high value on engineers who can move from theory to implementation. These labs also help you retain knowledge more effectively by giving you the chance to solve real problems and troubleshoot issues directly. To support you along the way, the course offers responsive assistance in the Q&A section, where instructors and fellow learners provide guidance, clarifications, and explanations. Having this support system ensures you are not left feeling stuck and that you can continue progressing with confidence.

On completion, you will receive a certificate that not only marks your achievement but also serves as proof of your dedication and continuous professional development. This can be a valuable addition to your resume or portfolio, showing potential employers that you have committed time and effort to mastering AWS data engineering. To make your enrollment completely risk-free, the course also comes with a 30-day money-back guarantee. This allows you to explore the content, evaluate the teaching style, and try out the labs before fully committing. If you feel it does not meet your expectations, you can withdraw within that time frame with no questions asked.

Beyond these features, the course also provides less tangible but equally important benefits. You save time by following a structured learning path that is fully aligned with the exam blueprint, instead of wasting hours searching for scattered resources. You build motivation and discipline through guided progression and gain access to a global learning community that shares insights and experiences, which can spark collaboration and networking opportunities. Combined, these advantages make the course not just an exam-preparation tool but a long-term investment in your career. You walk away with practical skills, confidence in your ability to pass the exam, and the knowledge to design, build, and manage large-scale data engineering systems on AWS that deliver real business value.