AWS Certified Data Engineer - Associate DEA-C01 certification video training course by prepaway along with practice test questions and answers, study guide and exam dumps provides the ultimate training package to help you pass.

Master AWS Data Engineer Associate (DEA-C01) Certification – Full Prep Guide

Course Overview

The AWS Certified Data Engineer – Associate (DEA-C01) certification is designed to validate an individual’s expertise in building and managing data solutions on AWS. It targets those working in roles that focus on data ingestion, storage, transformation, and analysis at scale.

Why Pursue the DEA-C01 Certification?

This certification demonstrates your ability to solve real-world data problems using the AWS ecosystem. It shows employers that you are capable of building efficient, secure, and scalable data pipelines. It also gives you an edge in the job market.

Course Objectives

This course prepares you for every domain outlined in the DEA-C01 exam guide. You’ll learn to ingest, store, process, analyze, secure, and monitor data workloads on AWS. More than just theory, the course includes real-world labs and architecture use cases.

Hands-On Approach

While the exam tests conceptual understanding, success in real-world roles demands hands-on skill. Therefore, every section of this course includes hands-on labs using AWS tools such as Glue, Redshift, Kinesis, S3, Athena, and more.

Learning Path Overview

This course is tailored to match the day-to-day responsibilities of a data engineer. You'll go beyond theory and dive into designing data pipelines, working with AWS-native analytics services, and building cost-effective, scalable architectures.

Course Duration

The course is structured to run over 10–12 weeks at a moderate pace. It can also be completed in a 4–6 week bootcamp format. Flexibility is built in for different learning preferences.

Weekly Breakdown

Each week is dedicated to one or more modules. Weekly checkpoints, quizzes, and labs allow for assessment and progression. A final mock exam concludes the course.

Key Learning Outcomes

You will gain fluency in the core services relevant to data engineering including Amazon S3, AWS Glue, Kinesis, EMR, Athena, Redshift, DynamoDB, and more.

Building Real Data Pipelines

You’ll architect pipelines that collect, transform, store, and serve data to downstream systems. Both batch and streaming pipelines are covered.

Security and Governance Skills

Security and data governance are foundational to this course. You'll learn to implement IAM roles, encryption, auditing, network boundaries, and Lake Formation permissions.

AWS Tools Covered

Amazon S3 and Storage Options

Amazon S3 is the foundation of most AWS data architectures. You’ll learn about storage classes, object versioning, encryption, and lifecycle management.

AWS Glue and Data Catalog

Master AWS Glue for building serverless ETL workflows. Understand how to catalog data using AWS Glue Data Catalog and expose metadata to Athena or Redshift.

Kinesis and Real-Time Streaming

Kinesis Data Streams, Kinesis Firehose, and Kinesis Data Analytics are covered in-depth for real-time streaming ingestion and transformation.

Amazon Redshift and Warehousing

Learn how to create Redshift clusters, use Redshift Spectrum for querying S3 data, and optimize query performance using distribution keys and sort keys.

Amazon EMR and Apache Spark

AWS EMR provides scalable compute for big data processing. Use Spark jobs on EMR to clean, transform, and enrich data in batch processing workflows.

Modular Training Plan

Module 1: Data Ingestion and Collection

You’ll explore how to ingest structured, semi-structured, and unstructured data from internal and external systems. This includes use of services like AWS DMS, Kinesis, Snowball, and Transfer Family.

Module 2: Data Storage and Lake Architecture

Understand how to design an AWS-based data lake using S3, Glue Catalog, Lake Formation, and integration with Athena and Redshift Spectrum.

Module 3: Batch Processing Techniques

Learn how to design and implement batch workflows using AWS Glue, Lambda, and Step Functions. You’ll focus on data preparation, cleaning, and schema evolution.

Module 4: Streaming Data Processing

Master stream processing concepts such as windowing, time-based aggregation, and late arrival handling using Kinesis Data Analytics and Apache Flink.

Module 5: Analytical Query Services

Explore Amazon Athena, Redshift, and Redshift Spectrum. Learn when to use which service, how to tune queries, and how to analyze datasets stored in S3 or on-prem.

Module 6: Data Governance and Access Control

Gain expertise in configuring fine-grained access using IAM, Lake Formation, and Glue Data Catalog. Learn how to control data visibility across teams and projects.

Module 7: Security and Encryption

Implement end-to-end encryption using KMS, SSE-S3, and CMKs. Understand secure network configurations using VPC, private endpoints, and cross-account roles.

Module 8: Monitoring and Logging

Use CloudWatch to monitor Glue jobs, Redshift clusters, and Kinesis streams. Set alarms, analyze logs, and track pipeline health proactively.

Module 9: Cost Optimization Strategies

Learn cost-saving techniques like data compression, intelligent storage tiering, choosing serverless options, and turning off idle clusters.

Module 10: Resilient Architecture Design

Design pipelines that are fault-tolerant and auto-recovering. Learn how to retry, replay, and design for exactly-once or at-least-once delivery.

Module 11: Capstone Labs and Projects

You will implement an end-to-end data engineering solution on AWS using multiple services covered in previous modules.

Module 12: Final Exam Preparation

Complete a full-length mock exam under timed conditions. Review questions and explanations. Identify gaps and revisit concepts.

Data Engineering Use Cases

Real-Time Dashboards

Use streaming data to power dashboards using Kinesis, Lambda, Glue, and Athena. Get alerts in near real-time based on data thresholds.

Enterprise Data Lakes

Design centralized storage for all business data using Amazon S3, AWS Glue Catalog, and Athena. Add governance via Lake Formation.

IoT and Sensor Data

Ingest sensor data into AWS from smart devices using IoT Core. Store in S3, transform using Glue, analyze using Timestream or Athena.

Exam-Focused Design

Domain-Based Coverage

Each exam domain is addressed: Data Ingestion, Storage, Processing, Analysis, Security, and Monitoring. All domains are covered deeply.

Sample Questions and Labs

Scenario-based questions at the end of each module help test your understanding. Labs provide practical context to apply the concepts.

Real Exam Practice

Timed mock exams allow you to simulate test conditions. You’ll learn how to manage time, flag confusing questions, and eliminate wrong options.

Architecture Deep Dive

Designing for Scale

Design architectures that handle high throughput and storage with minimal latency. Use partitioning, sharding, and parallel processing.

Durable and Fault-Tolerant Systems

Apply disaster recovery, versioning, cross-region replication, and backup strategies. Ensure availability during failures.

Comparing AWS Services

Understand trade-offs: when to use Kinesis vs MSK, Redshift vs Athena, EMR vs Glue, and RDS vs DynamoDB.

Performance Optimization

Efficient Querying

Tune Redshift and Athena queries with sort keys, distribution keys, and optimized file formats.

Storage and Compute Optimization

Reduce costs and improve speed by compressing files, reducing shuffle in Spark jobs, and using spot instances effectively.

Security and Compliance

Access Controls

Use IAM policies, S3 bucket policies, and Lake Formation permissions to limit access to sensitive datasets.

Encryption and Privacy

Apply client-side and server-side encryption. Use KMS and integrate with compliance frameworks such as HIPAA or GDPR where needed.

Monitoring and Troubleshooting

Observability Best Practices

Visualize and trace data pipelines using CloudWatch and X-Ray. Monitor job runtimes, retries, and errors across your pipeline.

Debugging Failures

Diagnose common issues like schema mismatch, memory overflows, slow transformations, and inconsistent output.

Project-Based Learning

Applied Projects

Implement use cases from various industries like e-commerce, fintech, and healthcare. Integrate services from multiple domains.

Portfolio Development

By completing labs and capstone projects, you will build a portfolio that demonstrates your AWS data engineering skills to potential employers.

Real-World Case Studies

E-Commerce Data Pipelines

Simulate ingestion of orders, users, and session data into S3. Process using Glue and analyze with Redshift and QuickSight.

Financial Analytics

Stream market data using Kinesis, process in real-time, and deliver insights into dashboards or alerting systems.

Healthcare Compliance

Design pipelines that store and process patient data while maintaining HIPAA-level security, auditing, and access control.

Final Preparation

Exam Readiness Tips

Focus on time management, reading questions carefully, and understanding AWS documentation. Practice interpreting architecture diagrams and logs.

Whitepapers and FAQs

Review key AWS resources including Well-Architected Framework, security whitepapers, Glue and Redshift best practices, and data lake guides.

Mindset for Success

Approach the exam with confidence backed by hands-on practice, solid conceptual understanding, and thorough review of key services.

Course Requirements

Foundational AWS Knowledge

To get the most out of this course, learners should already be familiar with the core AWS services. You should understand what Amazon S3 is, how to launch EC2 instances, how IAM policies work, and how VPCs manage networking.

You don’t need to be an AWS expert before starting, but a basic understanding of AWS infrastructure is critical. Familiarity with the AWS Management Console, CLI, and core concepts like regions and availability zones is expected.

Familiarity with Data Engineering Concepts

This course assumes that learners understand the fundamentals of data engineering. This includes concepts like ETL (Extract, Transform, Load), data modeling, schema design, batch vs streaming data, data formats (CSV, JSON, Parquet), and basic principles of data integration.

You should also understand the difference between OLTP and OLAP systems, what a data warehouse is, and the role of data lakes in modern architectures.

Basic Programming Skills

While this is not a software development course, basic scripting or programming knowledge is helpful. Python is commonly used in AWS Glue and Lambda. You should be comfortable writing simple functions, loops, and conditionals.

Experience with SQL is required. You will need to write SQL queries to interact with Athena, Redshift, and data lake queries. If you’re unfamiliar with SQL, it is strongly recommended that you take a basic SQL primer before or during the early part of the course.

Command Line and Shell Basics

Several labs involve using the AWS CLI or working in a Linux shell environment. While full Linux expertise is not required, you should be comfortable navigating directories, executing commands, and reading basic logs from the shell.

CLI knowledge will help you automate tasks and work more efficiently in later modules.

Access to an AWS Account

Hands-on practice is central to this course. You will need access to an AWS account. While many labs are designed to run within the AWS Free Tier, certain exercises may incur minor charges if not managed carefully.

You should understand billing alerts and cost management settings to prevent unintentional expenses. This also provides practical experience managing real AWS environments.

Internet Access and Browser

Because most content, labs, and assessments are delivered online, you will need a stable internet connection and a modern browser. Google Chrome, Firefox, Safari, or Microsoft Edge are all supported.

Commitment and Time

This is a professional-level certification course. You should be prepared to commit at least 8–10 hours per week for content review, labs, quizzes, and project work. Those opting for the fast-track or bootcamp version should be ready to dedicate 20+ hours per week for a shorter time period.

Time management and self-discipline are crucial for successful completion.

Course Description

A Deep Dive into AWS Data Engineering

This course is a complete, end-to-end preparation for the AWS Certified Data Engineer Associate exam (DEA-C01). But more than just preparing for the exam, it is a comprehensive learning experience in building modern data pipelines and data platforms on AWS.

You’ll go far beyond memorizing facts and instead develop real skills you can apply immediately in the workplace.

Designed for Practical Skills

Every section is built with the mindset of “learn by doing.” The course combines instructional video, theory, architecture diagrams, real-world examples, hands-on labs, and assessments. The course covers both managed and serverless services, enabling you to handle any type of AWS data workload.

You will build working solutions using Glue, Redshift, S3, Kinesis, DynamoDB, EMR, Athena, and more. You’ll be comfortable moving between data lakes, data warehouses, and real-time pipelines.

Architectures and Design Patterns

You won’t just learn tools—you’ll learn how to design architectures. Every tool is taught in context. You will analyze trade-offs between AWS services, design scalable workflows, optimize cost and performance, and ensure compliance with security and governance standards.

You’ll be exposed to common architectural patterns in batch and stream processing. You’ll learn how to deal with schema evolution, late-arriving data, transformations at scale, and automation using orchestration services.

Scenarios and Case Studies

The course is full of business scenarios drawn from real industries like finance, e-commerce, healthcare, and streaming media. You’ll build ingestion pipelines for IoT devices, analytics platforms for retailers, and secure healthcare data lakes.

You will be challenged with scenario-based questions similar to the DEA-C01 exam, encouraging you to apply your knowledge to solve problems.

Labs and Projects

Each module includes labs where you’ll implement the concepts hands-on. You will ingest streaming data using Kinesis, transform it with Glue, catalog it with Glue Data Catalog, and query it using Athena or Redshift Spectrum.

Capstone projects bring multiple services together in a single architecture. You’ll deploy your own full-stack data platform that supports ingestion, transformation, storage, analysis, monitoring, and optimization.

Performance and Cost Optimization

AWS gives you many choices. But cost and performance matter. In this course, you’ll learn how to choose the right services and configurations. You’ll understand the difference between Redshift provisioned vs Redshift Serverless. You’ll learn when to use S3 Standard vs Intelligent-Tiering, and how to design storage that’s both fast and affordable.

Performance tuning will include optimizing Spark jobs, Athena queries, Redshift clusters, and Glue resource allocations. You’ll also explore partitioning, file formats, and compression to improve efficiency.

Governance and Security

Data security is at the heart of AWS. This course includes a dedicated module on governance, encryption, access controls, VPC boundaries, and compliance. You’ll work with Lake Formation to implement fine-grained permissions. You’ll apply encryption using KMS and SSE. You’ll enforce IAM policies and cross-account access securely.

Audit logging with CloudTrail and monitoring with CloudWatch are taught in context. You’ll set alerts for job failures, permission issues, or cost anomalies.

DEA-C01 Exam Strategy

Preparing for the exam is different from learning the material. That’s why this course includes a full module on exam strategy. You’ll learn how to read scenario-based questions, identify distractors, and focus on keywords. You’ll complete several full-length practice exams under real test conditions.

You’ll get access to breakdowns of common mistakes, learn how AWS exams are structured, and develop confidence in your test-taking strategy.

What You Will Achieve

By the end of this course, you will be ready to pass the DEA-C01 exam on your first try. More importantly, you will have real, portfolio-worthy experience building cloud-native data pipelines.

Whether you want to land your first data engineering role, level up in your current job, or build advanced data solutions on AWS, this course will provide the skills you need.

Who This Course Is For

Aspiring Data Engineers

If you’re looking to start a career in data engineering, this course provides a structured, comprehensive path. It assumes some basic knowledge of data and AWS, but everything else is taught from the ground up.

You will move from theoretical knowledge to real, hands-on project experience. By the end, you’ll be ready to step into a junior or mid-level data engineering role.

AWS Professionals Seeking Certification

If you already work with AWS but want to deepen your knowledge of data services, this course is for you. It’s designed to help you pass the DEA-C01 exam, while also filling in gaps in architectural understanding and pipeline development.

You’ll move beyond isolated services and learn how to integrate them into end-to-end workflows.

Data Analysts and BI Developers

If you currently work in analytics or BI but want to move into the backend side of data pipelines, this course will bridge the gap. You’ll learn to design and manage the infrastructure that feeds dashboards and reports.

You’ll gain exposure to Glue, Redshift, Athena, and automation tools that make pipelines scalable and reliable.

Developers Transitioning to Data Engineering

If you're a backend developer or software engineer looking to shift into data engineering, this course provides the AWS-specific knowledge you’ll need. You’ll find the programming aspects familiar while learning how to build and manage data-centric workloads.

You’ll also benefit from the architectural content that teaches design tradeoffs and integration across AWS services.

System Administrators and DevOps Engineers

For DevOps professionals supporting data teams, this course will help you understand the workloads you're helping to deploy and secure. You'll gain insight into data processing architectures and monitoring strategies, which can enhance your ability to automate and optimize data infrastructure.

Teams and Organizations

This course is also suitable for technical teams and organizations seeking to upskill employees on AWS data engineering practices. The structure supports team-based learning, with discussion prompts, projects, and assessments that can be adapted to collaborative environments.

Additional Benefits of Taking This Course

Lifetime Access and Updates

AWS evolves quickly. This course is regularly updated to reflect changes in services, best practices, and exam requirements. Once enrolled, learners get lifetime access to all content, including new labs and updated modules.

Access to Community and Support

You’ll join a learning community where you can ask questions, get feedback on your projects, and share solutions. Instructors and mentors are available to support your learning journey.

Career Boost and Certification

The DEA-C01 certification is highly respected. Completing this course and passing the exam signals to employers that you can build data solutions that are scalable, secure, and production-ready. Whether you're job-hunting or aiming for a promotion, the combination of skills and certification can help open new doors.

This course is not just an exam cram session. It's a professional training program built for people who want to master real AWS data engineering. It combines theory, labs, architecture, projects, and test prep into one cohesive learning journey.

Whether you're aiming to break into the field or become an expert in AWS data pipelines, this course gives you the roadmap, the tools, and the confidence to succeed.

Prepaway's AWS Certified Data Engineer - Associate DEA-C01 video training course for passing certification exams is the only solution which you need.