AWS Certified Data Analytics Specialty (DAS-C01) Training – Transitioned to AWS Data Engineer Associate

What you’ll learn

AWS Data Analytics Ecosystem: Gain an in-depth understanding of the diverse range of AWS services designed for data analytics and their ideal scenarios of use, exploring how each service contributes to modern data pipelines.
Hands-on Experience: Work through practical examples and projects that demonstrate AWS analytics services in real-world environments.
Exam Readiness: Acquire the confidence and mastery to take and pass the AWS Data Engineer Associate certification exam, which has replaced the Data Analytics Specialty exam.
Best Practices: Understand and apply leading industry methodologies for managing, securing, and analyzing data on AWS platforms.

This course includes

16 hours of on-demand instructional video that thoroughly covers the required domains
1 practice test to simulate the real exam environment and reinforce your preparation
2 articles designed to supplement your learning with additional reading material
5 downloadable resources that provide reference material and templates
Access on both mobile and TV, allowing you to learn flexibly at your own pace
Closed captions for better accessibility
Audio descriptions embedded in the existing audio content
Certificate of completion to validate your skills and knowledge

Requirements

A passion to learn AWS services and data analytics concepts, coupled with dedication and curiosity, is all that is needed. While prior knowledge of AWS basics can help, this course is structured in a way that both beginners and experienced professionals can follow and benefit from.

Description

The AWS Certified Data Analytics Specialty exam was officially retired in April 2024. As AWS certifications continue to evolve to match the needs of modern data-driven organizations, the new AWS Data Engineer Associate certification has taken its place as the key credential for professionals aiming to specialize in building and managing data pipelines on AWS. To ensure learners stay aligned with the latest certification requirements and industry best practices, this course has been completely restructured and updated. It now reflects the AWS Data Engineer Associate certification guidelines and incorporates the newest services, features, and workflows that are relevant in today’s cloud ecosystem. The course is designed not only as exam preparation but also as a comprehensive training program for professionals who want to build real-world expertise in AWS data analytics.

This program is built to immerse you in the core concepts, services, and practical applications of AWS data analytics. From data collection and ingestion through storage, processing, analysis, and visualization, you will learn how to design and implement robust, scalable solutions that meet enterprise-level requirements. Unlike short crash courses that only skim the surface, this training has been carefully designed to give you both conceptual clarity and hands-on capability. The aim is to make you confident not only in passing the exam but also in applying these skills in your job or projects.

By the time you complete this course, you will have a detailed understanding of how AWS services interact to form complete data solutions. You will explore Amazon Kinesis and Kafka for real-time data ingestion, Amazon S3 and Lake Formation for building scalable data lakes, Redshift and DynamoDB for structured and unstructured data management, and EMR and Glue for large-scale processing. The program also covers tools like Athena and QuickSight to help you analyze and visualize data, while services such as IAM, KMS, and GuardDuty are introduced to ensure you know how to protect and secure sensitive information. Every step of the way, you will see how these services fit together into end-to-end data pipelines that can be applied in industries ranging from e-commerce and finance to healthcare and manufacturing.

The teaching approach combines in-depth video lectures, practical demonstrations in the AWS Management Console, and real-world examples that illustrate how organizations leverage these tools in production. You will not just watch theory—you will practice building solutions. With over 16 hours of video, extensive slide resources, and guided labs, the course is designed to cater to both visual and hands-on learners. Each domain of the certification is broken down into manageable sections, ensuring that even complex topics such as streaming data pipelines or advanced database migrations become understandable and actionable.

This course is ideal for aspiring data engineers, data scientists, developers, consultants, or IT professionals who want to specialize in AWS analytics. Whether you are new to AWS or have prior cloud experience, the structured flow of the lessons allows you to build your knowledge progressively. By the end, you will not only be exam-ready but also job-ready, capable of building secure, scalable, and efficient data solutions that organizations demand.

Inside the Course you will find

Inside this course you will find a carefully designed blend of learning materials and practical exercises, all built to help you master AWS data analytics and prepare confidently for the AWS Data Engineer Associate certification. Every component has been created with learners in mind, ensuring you get a balance of theory, hands-on practice, and exam readiness. The resources are not only meant to help you pass an exam but also to develop real-world skills that can be applied in professional data engineering roles.

One of the core elements of the course is over 600 pages of detailed presentation slides. These slides are not just text-heavy documents; they have been developed to explain AWS services and concepts in a way that is visual, structured, and easy to follow. Each slide breaks down complex topics into manageable parts, using diagrams, flowcharts, and real-world analogies to make the learning experience more engaging. Instead of trying to memorize long lists of features, you will see how these services fit together in practical workflows. Learners often find these slides to be valuable as reference material they can revisit long after completing the video lessons.

Alongside the slides, you will have access to more than 10 hours of in-depth video lectures. These sessions go beyond simply reading content from slides. Each video is designed to walk you through the why, how, and when of using AWS services for data engineering. The demonstrations use the AWS Management Console so you can see exactly how to set up, configure, and run different services. For example, you will watch how to create data pipelines with Amazon Kinesis, configure storage solutions with Amazon S3 and Lake Formation, and run analytics queries with Athena. By following along, you will not only hear the explanation but also see the process in action, making the concepts much easier to grasp.

The curriculum has been carefully updated to align with the latest AWS services and certification domains. This means that you are not studying outdated content or features that have been retired. Instead, you will be learning the exact tools and practices that AWS recommends today. This includes coverage of services like Amazon Managed Workflows for Apache Airflow (MWAA), the expanded capabilities of AWS Glue for ETL, and new approaches to cost management and security. By staying up to date, you will feel confident that your knowledge matches what is expected on the exam and in real-world scenarios.

A major highlight of the course is the inclusion of step-by-step demonstrations and practical labs. These exercises are built so that you can follow along in your own AWS account. They provide the opportunity to practice building data lakes, creating ETL jobs, deploying Redshift clusters, or setting up dashboards in QuickSight. The hands-on experience is what transforms theoretical knowledge into practical skill.

Finally, to ensure you are fully ready for the exam, the course includes practice tests. These tests are designed to simulate the difficulty and style of questions you will encounter in the certification. They allow you to test your understanding, identify weak areas, and revisit the relevant course material to strengthen your skills. With this combination of resources, you will be well prepared not only to pass the certification but also to confidently apply AWS data analytics in your career.

Course Structure and Domains

The course is organized into five main domains that map directly to the areas you must master for the AWS Data Engineer Associate exam. In addition to these, supplementary content has been included to give you a broader foundation.

Domain 1: Collection

This section focuses on how AWS services ingest and collect data from different sources. You will study Amazon Kinesis for real-time data streams, Amazon Data Firehose for managed delivery of streaming data, and Amazon Managed Service for Apache Kafka for working with large-scale distributed event streaming. Other services include Amazon SQS and Amazon MQ for queue-based messaging, the AWS Snow Family for physical data transfer at scale, AWS Transfer Family for secure file transfers, AWS Database Migration Service for moving data between databases, Amazon AppFlow for integration with SaaS platforms, and AWS Data Exchange for accessing third-party datasets. Practical labs are included where you will simulate streaming analytics pipelines and data ingestion from multiple environments.

Domain 2: Storage and Data Management

This section introduces you to the services that form the backbone of storage and management on AWS. You will work with Amazon S3 as the primary object storage solution, AWS Lake Formation for setting up data lakes, Amazon EFS for elastic file systems, and Amazon EBS for block storage attached to EC2. Relational and non-relational databases such as Amazon RDS, Aurora, Redshift, DynamoDB, MemoryDB for Redis, DocumentDB, and Neptune are covered in detail. The section emphasizes schema design, partitioning, scaling, and how these services interconnect to build high-performing data storage solutions. You will practice setting up Redshift clusters, designing DynamoDB tables, and building graph queries in Neptune.

Domain 3: Processing

This domain emphasizes transforming and processing data. AWS Glue is introduced as the central ETL service for preparing data. AWS Lambda and Step Functions are explored for event-driven and orchestrated workflows. You will learn how Amazon EMR allows you to run big data frameworks like Apache Spark and Hadoop on AWS, while Amazon Managed Workflows for Apache Airflow (MWAA) is covered as a way to schedule and manage complex pipelines. Exercises include building Glue jobs to cleanse data, deploying Spark jobs on EMR, and creating Lambda-based transformations.

Domain 4: Analysis and Visualization

In this domain, you will learn how to analyze data and create insights. Amazon Athena is covered as a serverless SQL engine to query S3 data. Amazon OpenSearch is demonstrated for search and log analytics. Machine learning analysis is introduced through Amazon SageMaker, where you can build, train, and deploy models. Visualization is handled by Amazon QuickSight, which allows for interactive dashboards. The exercises in this domain include querying logs with Athena, creating visualization dashboards with QuickSight, and deploying a basic SageMaker model.

Domain 5: Security

This domain focuses on protecting data and ensuring compliance. You will study AWS Identity and Access Management for user and permission control, AWS STS for temporary credentials, and AWS Key Management Service for encryption. AWS Secrets Manager is used for secure handling of sensitive information. Monitoring services such as Amazon Macie, GuardDuty, Detective, CloudWatch, and CloudTrail are explained, alongside network security practices. Practical labs demonstrate how to configure IAM policies, enable CloudTrail logging, and identify anomalies with GuardDuty.

Additional Resources

Beyond the main domains, the course includes sections on AWS Developer Tools and Cost Management. These lessons show how to integrate CI/CD pipelines into data workflows and how to estimate and manage costs across analytics projects. Students will learn to leverage AWS Budgets and Cost Explorer to keep expenses under control while scaling their workloads efficiently.

Instructor Profile

The course is taught by Maruchin Tech, also known as Maruchin “Marty” Shun. Based in Japan, he has published over 30 courses and practice tests on Udemy, with more than 50,000 students enrolled. His expertise spans AWS, data analytics, and IT system development, with previous professional experience in automotive manufacturing and consulting. His mission is to provide learners with both theoretical depth and hands-on skill, enabling them to build strong careers in data engineering and analytics. His teaching approach blends technical accuracy with practical demonstrations that make learning engaging and applicable.

Who this Course is for

This course is designed for a wide variety of learners who are eager to develop skills in AWS data analytics and data engineering. Whether you are completely new to AWS or already have some experience with cloud technologies, the course is structured to meet you at your level and guide you toward mastery. It provides a mix of theory and practice that ensures all types of learners—from beginners to professionals—can take away valuable skills that are relevant to their goals.

For aspiring data engineers, this course provides the essential foundation for building, managing, and optimizing data pipelines in the AWS ecosystem. Data engineering is one of the fastest-growing career paths in technology today, and employers are increasingly looking for candidates who not only understand concepts but also know how to apply them using tools like AWS Glue, Redshift, DynamoDB, and EMR. By working through the lessons and hands-on labs, aspiring data engineers will gain the knowledge and experience needed to secure jobs or advance in their current positions.

For data scientists, the course offers an opportunity to learn how to integrate their models and analysis into scalable AWS workflows. Data scientists often focus on statistics, modeling, and algorithms, but in many organizations they are also expected to prepare and process data or deploy solutions into production environments. This course bridges that gap by showing data scientists how to use services such as Amazon SageMaker for machine learning, Athena for querying large datasets, and QuickSight for visualization. It enables them to go beyond experimentation and contribute to full data solutions.

For IT professionals, this course is a way to expand existing technical skills into the domain of data analytics. Many system administrators, DevOps engineers, or database administrators already have strong technical backgrounds but need to understand how data systems are evolving in the cloud. By studying this material, IT professionals can upskill and prepare themselves for roles where cloud data architecture, security, and integration are critical. The course helps them transition into hybrid roles that combine operations with data analytics expertise.

Developers who want to specialize in data workflows will also benefit from this course. Many developers have experience writing application code but may not know how to design data pipelines, work with distributed systems, or build data models that scale. This course introduces them to services like Kinesis, Lambda, and Step Functions so they can begin integrating data processing into their applications. With these skills, developers can expand their career opportunities into data-focused roles that are in high demand.

Consultants and solution architects who advise clients on AWS adoption will find this course valuable as well. Understanding how to design end-to-end data solutions is critical when advising businesses on migrating to the cloud or building modern analytics environments. This course gives consultants the practical knowledge they need to propose solutions that are efficient, secure, and cost-effective.

Finally, students and learners who are just starting their careers will benefit from the structured and comprehensive nature of this program. Cloud computing and data analytics are among the most in-demand skills worldwide, and this course provides a clear path to acquiring them. By completing the course and earning the AWS Data Engineer Associate certification, students can differentiate themselves in the job market and demonstrate they are ready for professional opportunities.

In summary, this course is for anyone who wants to work with data in AWS—whether your goal is to advance in your career, transition into a new role, or prepare for the AWS certification exam. With its balance of theoretical knowledge, hands-on labs, and exam readiness material, the course ensures that all learners leave with the depth and breadth of skills required to succeed.