Theory | Hands-On Labs | Practice Questions | Downloadable PDF Slides | Pass the certification exam | Latest Syllabus
β±οΈ Length: 55.0 total hours
β 4.29/5 rating
π₯ 48,609 students
π February 2026 update
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- Course Overview
- Embark on a comprehensive journey to master AWS data engineering principles and practices, culminating in preparation for the AWS Certified Data Engineer – Associate certification.
- This program offers a blended learning experience, seamlessly integrating theoretical knowledge with extensive hands-on laboratory exercises, ensuring practical application of concepts.
- The curriculum is meticulously designed to align with the latest AWS Certified Data Engineer – Associate examination syllabus, guaranteeing relevance and up-to-date content.
- With a substantial 55.0 total hours of learning material, this course provides an in-depth exploration of AWS data services and their practical implementation.
- Benefit from a high-quality educational resource, reflected in the impressive 4.29/5 average rating, and join a thriving community of over 48,609 students.
- The course content has been recently updated in February 2026, ensuring you receive the most current information and best practices in AWS data engineering.
- Beyond theoretical understanding, this course emphasizes practical skills through guided labs, allowing you to build and deploy real-world data solutions.
- Prepare for success with dedicated sections featuring practice questions that mirror the format and difficulty of the actual certification exam.
- Access all course materials conveniently with downloadable PDF slides, enabling offline review and personalized study sessions.
- The ultimate objective is to equip you with the confidence and proficiency to successfully pass the AWS Certified Data Engineer – Associate examination.
- Key Learning Areas & Practical Applications
- Gain a foundational understanding of AWS core services essential for data engineering, including S3, EC2, IAM, and VPC.
- Master the design and implementation of robust data lakes on AWS, leveraging services like Amazon S3 for scalable and cost-effective storage.
- Develop proficiency in building data pipelines using AWS Glue, including ETL job development, crawlers, and job scheduling.
- Learn to process and transform large datasets efficiently using AWS EMR, exploring various frameworks like Apache Spark and Hadoop.
- Understand the principles of data warehousing on AWS and gain hands-on experience with Amazon Redshift for analytical workloads.
- Explore real-time data streaming solutions using Amazon Kinesis, covering data ingestion, processing, and analytics.
- Implement secure data access and management strategies using AWS IAM roles, policies, and encryption services.
- Develop skills in data modeling and schema design for various data storage and processing services.
- Learn to monitor and troubleshoot data pipelines and services using AWS CloudWatch and other monitoring tools.
- Understand the importance of data governance and compliance within the AWS ecosystem.
- Practice constructing data pipelines that handle batch and streaming data scenarios effectively.
- Gain experience in optimizing data storage, processing, and query performance for cost and efficiency.
- Learn to deploy and manage data solutions using Infrastructure as Code (IaC) principles, potentially touching upon CloudFormation or Terraform.
- Understand the integration points between various AWS data services to build end-to-end data solutions.
- Requirements / Prerequisites
- A foundational understanding of general cloud computing concepts is beneficial but not strictly mandatory, as the course covers AWS basics.
- Basic programming knowledge, ideally in Python, is recommended for hands-on lab exercises and data manipulation tasks.
- Familiarity with SQL and database concepts will significantly enhance your ability to work with data warehousing and relational databases on AWS.
- A personal AWS account (with free tier eligibility) is required to perform hands-on laboratory exercises.
- A stable internet connection is essential for accessing course materials and performing cloud-based lab work.
- A willingness to learn and experiment with cloud services is paramount for success in this hands-on program.
- Prior exposure to data concepts like data types, data formats, and basic data structures is helpful.
- Skills Covered / Tools Used
- AWS Services: Amazon S3, AWS Glue, Amazon EMR, Amazon Redshift, Amazon Kinesis (Data Streams, Firehose, Analytics), AWS Lambda, AWS IAM, Amazon CloudWatch, AWS Lake Formation, possibly AWS DMS, AWS Step Functions, and AWS Athena.
- Data Processing Frameworks: Apache Spark, Hadoop ecosystem.
- Programming Languages: Primarily Python for scripting and ETL development.
- Data Formats: Parquet, ORC, CSV, JSON.
- Database Concepts: Relational databases, data warehousing principles.
- ETL/ELT Processes: Design, implementation, and optimization.
- Data Modeling: Schema design for various AWS data stores.
- Cloud Architecture: Designing scalable and resilient data solutions.
- Monitoring & Troubleshooting: AWS CloudWatch and log analysis.
- Security: IAM policies, encryption, access control.
- Benefits / Outcomes
- Achieve the prestigious AWS Certified Data Engineer – Associate certification, validating your expertise to employers.
- Become highly employable in the growing field of data engineering, with in-demand skills for cloud-based data solutions.
- Gain the confidence to design, build, and manage complex data pipelines and architectures on AWS.
- Develop a strong portfolio of practical projects through the extensive hands-on lab exercises.
- Enhance your problem-solving abilities in data-related challenges within the AWS environment.
- Become proficient in utilizing a wide array of AWS data services to solve real-world business problems.
- Improve your career prospects and earning potential in cloud and data-focused roles.
- Acquire the skills to optimize data solutions for performance, cost-effectiveness, and security.
- Be prepared to contribute effectively to data initiatives in organizations leveraging AWS.
- Gain a deep understanding of the AWS Well-Architected Framework for Data.
- PROS
- Comprehensive Coverage: Integrates theory, hands-on labs, and exam prep for a well-rounded learning experience.
- Practical Focus: Emphasizes practical application of AWS services through extensive labs.
- Up-to-Date Content: Regularly updated to align with the latest AWS certification syllabus.
- High Student Satisfaction: Excellent rating and large student base indicate quality and effectiveness.
- Valuable Certification: Directly prepares for a highly recognized and in-demand AWS certification.
- Downloadable Resources: PDF slides offer flexible offline study options.
- CONS
- The sheer volume of content (55 hours) may require significant time commitment and dedicated study periods for some learners.
Learning Tracks: English,IT & Software,IT Certifications
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