About This Course

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AWS Certified Data Engineer – Associate (DEA-C01) Training

The AWS Data Engineering Professional Certification – Associate Level (DEA-C01) is designed for individuals who aim to advance their expertise in designing, building, and maintaining data processing systems using Amazon Web Services. This course provides comprehensive insights into AWS’s data services and prepares candidates to achieve the AWS Certified Data Engineer – Associate (DEA-C01) credential. By combining theoretical knowledge with practical, hands-on labs, learners will gain the skills needed to manage large-scale data solutions efficiently.

Participants will explore topics such as data ingestion, transformation, storage, and analytics using AWS-native tools and services. The course also emphasizes best practices in security, monitoring, optimization, and cost management for data-driven applications. With a structured approach to learning, students will gain confidence in applying AWS data engineering concepts in real-world scenarios.

Throughout this course, learners will engage with interactive exercises and projects that mirror real industry challenges, ensuring that by the end of the program, they are well-equipped to design scalable and reliable data pipelines on the AWS cloud.

What You Will Learn From This Course

• Understand the fundamentals of cloud computing and AWS architecture, specifically related to data engineering workflows.
  
  

• Gain expertise in AWS data storage solutions such as Amazon S3, Redshift, DynamoDB, and RDS.
  
  

• Learn to design, deploy, and manage efficient ETL (Extract, Transform, Load) pipelines using AWS Glue, Lambda, and other serverless services.
  
  

• Implement data ingestion strategies from multiple sources including streaming and batch data workflows.
  
  

• Develop skills in data transformation, cleaning, and aggregation using AWS-native tools.
  
  

• Explore data analytics frameworks and services such as Amazon Athena, Redshift Spectrum, and QuickSight for business intelligence.
  
  

• Understand monitoring and optimization of data pipelines for cost-efficiency and performance.
  
  

• Learn security best practices including encryption, IAM policies, and access management for data services.
  
  

• Gain the ability to troubleshoot common data engineering challenges in AWS environments.
  
  

• Prepare thoroughly for the AWS Certified Data Engineer – Associate exam (DEA-C01) with hands-on labs and practice questions.

Learning Objectives

By the end of this course, learners will be able to:

• Architect and implement scalable data processing solutions on AWS.
  
  

• Design robust ETL pipelines capable of handling both batch and streaming data.
  
  

• Utilize AWS data storage services to store and retrieve structured and unstructured data efficiently.
  
  

• Perform advanced data analytics using AWS-native querying and visualization tools.
  
  

• Apply security best practices to protect sensitive data across multiple AWS services.
  
  

• Monitor and optimize data workflows for improved performance and reduced operational costs.
  
  

• Solve practical data engineering challenges using AWS services and serverless architecture.
  
  

• Demonstrate readiness for the AWS DEA-C01 certification exam through practice tests and scenario-based exercises.

Requirements

This course is designed for professionals who have a foundational understanding of cloud computing and programming. The requirements include:

• Basic knowledge of cloud computing concepts and architecture.
  
  

• Familiarity with data engineering principles, ETL processes, and relational databases.
  
  

• Understanding of Python or SQL programming for data manipulation.
  
  

• Experience with Linux/Unix commands and scripting is helpful but not mandatory.
  
  

• An AWS account to practice hands-on labs (free tier accounts recommended).
  
  

• Eagerness to learn AWS services for data engineering and analytics.

No prior certification is required; however, individuals with experience in IT infrastructure, software development, or data analytics will benefit most from this course.

Course Description

The AWS Data Engineering Professional Certification – Associate Level (DEA-C01) course is a deep dive into the world of cloud-based data engineering. With a strong focus on practical skills and real-world applications, this course provides participants with the tools and knowledge necessary to become proficient in managing data pipelines and analytics workflows on AWS.

Learners will start by understanding the core AWS services and their role in data engineering. This includes an introduction to S3, Redshift, DynamoDB, RDS, and data lake concepts. The course then moves into more advanced topics, covering ETL pipelines, streaming and batch processing, serverless architecture, and analytics services.

Hands-on labs form a crucial component of the course. Participants will build fully functional pipelines, implement data ingestion from various sources, perform transformations, and load data into appropriate storage solutions. They will also analyze the stored data using AWS analytics tools and create visualizations to derive business insights.

Security, compliance, and best practices are emphasized throughout the course, ensuring learners understand how to safeguard sensitive data while optimizing performance and cost. By combining theory, practical exercises, and exam-focused preparation, this course equips learners with the skills needed to succeed in AWS data engineering roles.

Target Audience

This course is ideal for:

• Data Engineers looking to specialize in AWS services.
  
  

• Cloud professionals seeking to expand their AWS skillset.
  
  

• Database administrators and analysts interested in data engineering workflows.
  
  

• IT professionals transitioning into cloud-based data roles.
  
  

• Software developers who work with large datasets and require scalable cloud solutions.
  
  

• Anyone preparing for the AWS Certified Data Engineer – Associate (DEA-C01) exam.

Professionals in roles such as business intelligence engineers, data architects, and machine learning engineers will also benefit from the insights offered in this course, as it bridges the gap between traditional database management and modern cloud-based data processing.

Prerequisites

Before enrolling in this course, learners should have:

• Familiarity with fundamental cloud computing concepts and services.
  
  

• Basic understanding of relational databases, SQL queries, and data modeling.
  
  

• Exposure to programming or scripting languages such as Python or Java.
  
  

• Awareness of data processing and ETL concepts.
  
  

• An AWS account to practice labs and exercises.

Having prior experience with AWS services is beneficial but not mandatory. This course starts from foundational concepts and progressively builds to advanced skills, making it accessible for beginners while still valuable for intermediate learners.

Course Modules/Sections

The AWS Data Engineering Professional Certification – Associate Level (DEA-C01) course is organized into a series of carefully structured modules to ensure learners gain a thorough understanding of data engineering on AWS. Each module builds upon the previous one, combining theory, practical application, and hands-on exercises to create a comprehensive learning experience.

Module 1: Introduction to AWS Data Engineering
This foundational module introduces the core principles of data engineering and the AWS ecosystem. Learners will understand the significance of cloud-based data pipelines, scalable architecture, and the role of data engineers in modern organizations. Key AWS services such as S3, Redshift, RDS, and DynamoDB are introduced, along with an overview of data lakes, warehouses, and serverless data processing.

Module 2: Data Ingestion and Collection
In this module, students will explore methods to ingest data from various sources. Topics include batch ingestion, real-time streaming, and automated workflows. Hands-on exercises will involve setting up ingestion pipelines using AWS Glue, Kinesis, and Lambda. Learners will also study best practices for error handling, data validation, and scalability.

Module 3: Data Storage Solutions
Data storage is a critical component of any data engineering workflow. This module covers AWS storage services in depth, including Amazon S3, Redshift, DynamoDB, and RDS. Students will learn how to select appropriate storage solutions based on data type, volume, and access patterns. Techniques for organizing, partitioning, and managing data efficiently are also discussed.

Module 4: Data Transformation and ETL
This module focuses on the transformation and processing of raw data into structured, usable formats. Students will design ETL pipelines using AWS Glue, Lambda, and Step Functions. The course emphasizes optimization techniques to ensure pipelines are cost-effective and performant. Practical exercises include data cleansing, aggregation, and enrichment workflows.

Module 5: Data Analytics and Visualization
Once data is stored and processed, analyzing and visualizing it becomes essential. Learners will work with AWS analytics tools such as Athena, Redshift Spectrum, QuickSight, and EMR. Modules include query optimization, integration of multiple data sources, and building dashboards for actionable insights. Students will gain hands-on experience in performing complex analytics tasks and visualizing results for stakeholders.

Module 6: Security and Compliance
Data security and compliance are integral to any AWS deployment. This module covers best practices for securing data at rest and in transit, managing IAM roles and policies, and implementing encryption strategies. Students will learn how to meet regulatory requirements and protect sensitive information in their data pipelines.

Module 7: Monitoring, Optimization, and Cost Management
Monitoring and optimizing data workflows is crucial for maintaining performance and reducing costs. This module explores CloudWatch, CloudTrail, and AWS Cost Explorer. Students will learn to track performance metrics, identify bottlenecks, and implement cost-saving strategies. Real-world scenarios and exercises teach learners how to maintain efficient and reliable data pipelines.

Module 8: Exam Preparation and Practice Labs
The final module focuses on preparing for the AWS Certified Data Engineer – Associate exam (DEA-C01). It includes practice questions, exam strategies, and scenario-based exercises to simulate the certification test environment. Learners will also review key concepts and workflows from previous modules to reinforce knowledge.

Each module is designed to be self-contained yet interconnected, ensuring learners develop both foundational understanding and practical skills. By the end of these modules, students will have completed fully functional AWS data engineering projects, reinforcing their readiness for certification and professional practice.

Key Topics Covered

The course covers a wide range of topics to provide a holistic understanding of AWS data engineering. Topics include:

• Cloud Computing Fundamentals: Understanding the cloud, AWS architecture, and its advantages for data engineering.
  
  

• AWS Core Data Services: In-depth coverage of Amazon S3, Redshift, DynamoDB, RDS, and AWS Data Lakes.
  
  

• Data Ingestion Techniques: Batch processing, streaming data ingestion, and real-time data pipelines.
  
  

• ETL Process Design: Extracting, transforming, and loading data efficiently using AWS Glue, Lambda, and Step Functions.
  
  

• Data Transformation: Data cleansing, enrichment, aggregation, and preparation for analytics.
  
  

• Data Analytics: Querying large datasets using Athena, Redshift Spectrum, and other analytics services.
  
  

• Visualization and Reporting: Building dashboards and reports using AWS QuickSight and integrating insights into business workflows.
  
  

• Security Best Practices: IAM roles and policies, encryption, and compliance standards.
  
  

• Monitoring and Logging: Using CloudWatch, CloudTrail, and logging strategies to maintain pipeline health.
  
  

• Cost Optimization: Techniques for efficient resource usage and budget management.
  
  

• Serverless Architecture: Leveraging AWS Lambda and other serverless tools for automated data workflows.
  
  

• Real-world Scenarios: Hands-on labs replicating industry challenges in data engineering.
  
  

• Exam Readiness: Practice questions, scenario-based exercises, and review sessions to prepare for DEA-C01 certification.

Each topic is reinforced with practical examples, case studies, and lab exercises. Learners not only understand the theoretical underpinnings but also gain experience applying AWS services in real-world contexts.

Teaching Methodology

The teaching methodology for this course combines theoretical instruction with hands-on practical exercises to create an engaging and effective learning experience. Each module begins with conceptual explanations to ensure learners understand the fundamental principles behind AWS services and data engineering workflows. Detailed demonstrations are provided, showing step-by-step implementation of pipelines, storage solutions, and analytics workflows.

Hands-on labs are a core component of the course, allowing learners to apply concepts in a real AWS environment. These labs include creating data ingestion pipelines, performing ETL transformations, implementing security measures, and conducting analytics using AWS tools. Learners also engage in mini-projects that simulate challenges faced by data engineers in real-world scenarios.

Interactive exercises, quizzes, and group discussions are used to reinforce learning and encourage critical thinking. The course also includes scenario-based problem-solving sessions, where learners must design and troubleshoot data solutions using AWS services. This approach ensures that students are not only exam-ready but also equipped to handle practical challenges in professional settings.

Supplemental resources, including reading materials, documentation references, and video tutorials, are provided to support self-paced learning. Regular assessments and feedback sessions help learners track progress and identify areas for improvement. By combining theory, practice, and interactive learning, the course ensures a comprehensive understanding of AWS data engineering principles.

Assessment & Evaluation

Assessment and evaluation are integral to the learning process in this course, designed to ensure that participants achieve mastery of AWS data engineering concepts. Students are evaluated through a combination of hands-on lab exercises, quizzes, assignments, and practice exams. Each assessment is structured to test both conceptual understanding and practical application of skills.

Hands-on labs serve as a primary evaluation tool, requiring learners to implement data pipelines, perform transformations, and execute analytics tasks using AWS services. These labs are reviewed for accuracy, efficiency, and adherence to best practices. Quizzes and assignments at the end of each module provide additional checkpoints, testing knowledge retention and problem-solving abilities.

Scenario-based exercises simulate real-world challenges, requiring learners to analyze requirements, design solutions, and optimize workflows. These exercises assess critical thinking, decision-making, and the ability to apply AWS services effectively. Feedback is provided for each exercise to guide improvement and reinforce learning outcomes.

Practice exams are included to prepare learners for the AWS Certified Data Engineer – Associate (DEA-C01) certification. These exams mirror the format and difficulty level of the actual test, helping students identify strengths and weaknesses. Comprehensive review sessions follow practice exams, allowing learners to revisit key concepts and refine their understanding.

Overall, the assessment methodology ensures a balance between theoretical knowledge and practical application, preparing participants to confidently implement AWS data engineering solutions and succeed in certification exams.

Benefits of the Course

Enrolling in the AWS Data Engineering Professional Certification – Associate Level (DEA-C01) course offers numerous benefits for both aspiring and experienced data engineers. The program is designed to provide a combination of theoretical knowledge, practical skills, and exam readiness, enabling learners to excel in cloud-based data engineering roles.

One of the primary benefits of the course is gaining expertise in AWS data services. Participants will develop a deep understanding of Amazon S3, Redshift, DynamoDB, RDS, AWS Glue, Athena, Kinesis, and other essential tools used in modern data engineering. This knowledge empowers learners to design and implement scalable, reliable, and cost-efficient data pipelines, enabling businesses to leverage data for strategic decision-making.

The course also emphasizes hands-on experience, which is invaluable for career growth. Through lab exercises, mini-projects, and scenario-based problem-solving sessions, learners gain practical skills that can be directly applied to professional environments. By working on real-world datasets and building functional pipelines, participants acquire confidence in their ability to manage end-to-end data engineering workflows.

Another key benefit is the focus on security, compliance, and best practices. Understanding how to protect sensitive data, manage access controls, and implement encryption techniques is essential in today’s regulatory landscape. This course equips learners with the knowledge to ensure data integrity and compliance, making them highly valuable to organizations that handle critical data.

Preparing for the AWS Certified Data Engineer – Associate exam is another significant advantage. The course includes exam-focused modules, practice questions, and scenario-based assessments, ensuring that learners are thoroughly prepared for the certification. Earning this credential demonstrates proficiency in AWS data engineering, enhancing employability and opening doors to roles such as data engineer, data architect, cloud analyst, and business intelligence engineer.

Additionally, the course fosters problem-solving and analytical thinking. By tackling complex data engineering challenges, learners develop the ability to optimize pipelines, troubleshoot issues, and implement performance improvements. These skills are transferable to any cloud-based or hybrid data environment, making the course applicable beyond AWS alone.

Finally, participants benefit from networking and collaboration opportunities. Engaging with instructors, peers, and industry experts helps learners exchange ideas, discuss best practices, and gain insights into current trends in cloud-based data engineering. This exposure enhances professional growth, encourages knowledge sharing, and positions learners as competitive candidates in the job market.

Course Duration

The AWS Data Engineering Professional Certification – Associate Level (DEA-C01) course is designed to be completed over a comprehensive schedule that balances in-depth learning with practical experience. The total duration of the course varies depending on the learner’s pace, prior knowledge, and commitment, but it is structured to provide a complete mastery of AWS data engineering concepts.

On average, the course spans 12 to 16 weeks for learners dedicating 8 to 12 hours per week. This includes time for lecture sessions, lab exercises, assignments, quizzes, and practice exams. Learners can also choose an accelerated schedule for faster completion, typically covering the entire syllabus in 6 to 8 weeks by engaging in more intensive daily sessions.

Each module is carefully timed to ensure thorough understanding. Introductory modules, such as cloud computing fundamentals and AWS core services, are generally completed within the first two weeks. Modules covering data ingestion, storage, and transformation may take three to four weeks, as these topics involve extensive hands-on exercises. Analytics, visualization, security, and monitoring modules are designed to take approximately three weeks, providing sufficient time for both theory and practice.

Exam preparation is an integral part of the schedule and typically requires 1 to 2 weeks of focused review. During this period, learners engage with practice tests, scenario-based assessments, and review sessions that consolidate knowledge from all modules. This structured timeline ensures that participants are fully prepared for the AWS Certified Data Engineer – Associate (DEA-C01) exam by the end of the course.

The course also allows for self-paced learning, giving learners the flexibility to adjust the schedule based on professional or personal commitments. This flexibility ensures that students can balance learning with real-world responsibilities while still achieving comprehensive knowledge and practical experience.

Tools & Resources Required

To successfully complete the AWS Data Engineering Professional Certification – Associate Level (DEA-C01) course, learners require a combination of software tools, cloud resources, and educational materials. These tools and resources are essential for hands-on labs, practical exercises, and exam preparation.

AWS Account:
A primary requirement is an active AWS account. A free-tier account is sufficient for most lab exercises, though certain modules may require access to additional AWS services. An account allows learners to practice data ingestion, storage, ETL, analytics, security configurations, and monitoring tasks in a live environment.

Programming Tools:
Familiarity with programming languages such as Python, SQL, or Java is necessary for ETL, data transformation, and analytics tasks. Learners should have a code editor such as VS Code, PyCharm, or Sublime Text to write scripts and develop data processing workflows. Knowledge of basic scripting and SQL queries is essential for manipulating datasets and performing analysis.

AWS CLI and SDKs:
Participants should install and configure the AWS Command Line Interface (CLI) to interact with AWS services from their local machines. Additionally, using AWS SDKs (such as Boto3 for Python) is recommended for building programmatic solutions and automating workflows.

Data Visualization Tools:
For analytics and visualization exercises, learners will use AWS-native tools such as QuickSight. Familiarity with dashboard creation, charting, and reporting is beneficial. Students may also use open-source tools like Tableau or Power BI for supplementary practice in visualizing data.

Learning Resources:
Comprehensive course materials, including video lectures, PDF documentation, and reading resources, are provided. Learners are encouraged to use AWS official documentation, whitepapers, and best-practice guides to deepen their understanding. Additional reference books on cloud architecture, data engineering, and big data analytics may enhance learning.

Project and Lab Environment:
A modern computer with internet access is required to run virtual labs and perform cloud exercises efficiently. Each module includes step-by-step lab guides to set up data pipelines, perform transformations, and deploy analytics workflows. Students should have access to sufficient storage and computing resources to handle large datasets during lab exercises.

Collaboration and Discussion Tools:
Engaging with instructors, mentors, and peers is recommended to enhance learning. Platforms for discussion, group projects, and collaborative problem-solving may include course forums, Slack channels, or dedicated learning management systems. Interaction with others helps reinforce concepts, clarify doubts, and gain practical insights from shared experiences.

Practice Exams and Assessment Tools:
To prepare for the AWS Certified Data Engineer – Associate exam, learners need access to mock exams, practice questions, and scenario-based assessments. These resources help evaluate readiness, identify knowledge gaps, and build confidence before attempting the certification.

Optional Tools:
For advanced learners, additional tools such as Apache Spark, Hadoop, or third-party ETL platforms may be explored. These tools provide supplementary experience and prepare students for hybrid environments where AWS services integrate with broader data engineering ecosystems.

By leveraging these tools and resources, learners gain both theoretical and practical experience, ensuring they are fully equipped to implement AWS data engineering solutions and succeed in certification assessments.

Career Opportunities

Completing the AWS Data Engineering Professional Certification – Associate Level (DEA-C01) course opens a wide array of career opportunities for individuals seeking to advance their professional journey in the field of data engineering. Organizations across industries are increasingly relying on cloud-based data solutions to manage, analyze, and leverage data efficiently. As a result, certified AWS data engineers are in high demand, and this course equips learners with the skills and knowledge required to fill these roles confidently. Data engineers play a pivotal role in designing, building, and maintaining data pipelines, ensuring that raw data is transformed into actionable insights. This course prepares participants to take on responsibilities such as data ingestion, transformation, storage, analytics, and monitoring, all within AWS environments. Graduates can pursue positions such as cloud data engineer, data architect, business intelligence engineer, analytics engineer, and data operations specialist. These roles often involve collaborating with data scientists, software engineers, and business analysts to optimize workflows, maintain data integrity, and implement cost-efficient solutions. Professionals certified in AWS data engineering are also well-positioned to contribute to data governance initiatives, implement best practices for security and compliance, and streamline large-scale data processing tasks. Organizations ranging from startups to multinational corporations seek AWS-certified professionals to manage cloud-based data ecosystems and ensure business intelligence processes are accurate, reliable, and scalable. In addition to technical roles, certification enhances opportunities for leadership and project management positions in data teams. With strong analytical, problem-solving, and cloud engineering skills, graduates can transition into senior roles, such as lead data engineer, cloud solutions architect, or head of data operations. Beyond corporate opportunities, individuals may explore consulting, freelance, or entrepreneurial roles in the cloud and data analytics space, leveraging expertise to design and implement AWS-based solutions for multiple clients. Furthermore, this course provides a foundation for continuous professional development, as learners gain skills applicable to advanced certifications, emerging AWS technologies, and specialized areas such as machine learning integration, big data analytics, and IoT data processing. The combination of practical experience, theoretical knowledge, and certification readiness ensures that participants are highly competitive in the job market and prepared for long-term career growth in the rapidly evolving cloud data engineering landscape.

Enroll Today

Enrolling in the AWS Data Engineering Professional Certification – Associate Level (DEA-C01) course is the first step toward a rewarding career in cloud-based data engineering. The course offers a structured, comprehensive curriculum designed to equip learners with both foundational and advanced skills in AWS data services, ETL pipelines, data storage, analytics, security, and monitoring. By enrolling, participants gain access to expert-led instruction, hands-on labs, practical projects, and exam-focused preparation materials that ensure readiness for the AWS Certified Data Engineer – Associate exam. The course provides flexibility for self-paced learning or guided instruction, allowing learners to balance professional commitments while achieving comprehensive mastery of data engineering concepts. Participants benefit from interactive exercises, real-world case studies, and scenario-based problem-solving sessions, which help develop practical skills applicable to professional environments. Enrolling also grants access to a wide range of resources, including AWS documentation, practice exams, lab guides, and collaborative learning platforms to interact with instructors and peers. This immersive learning experience ensures that graduates can confidently design, implement, and manage scalable data solutions on AWS, making them highly employable and ready to take on diverse roles in the cloud data ecosystem. By enrolling today, learners take a proactive step toward advancing their expertise, achieving certification, and opening doors to lucrative career opportunities in the fast-growing field of AWS data engineering.

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