Course Overview:

The Data Engineering on AWS course is designed for data professionals who want to learn how to design, build, secure, and optimize data pipelines on AWS. Through a combination of lectures, demonstrations, and hands-on labs, participants will gain practical experience in ingesting, transforming, storing, and analysing data using AWS data services.

This course covers essential data engineering topics such as data lakes, batch and streaming data processing, orchestration, and automation, enabling learners to create scalable, efficient, and secure data pipelines aligned with AWS best practices.

By the end of this course, learners will have the skills to design and implement data workflows for real-world analytics and machine learning applications.

After completing this course, participants will be able to:

  • Explain the role and key responsibilities of a data engineer in AWS environments.
  • Design, build, and manage data lakes using Amazon S3, Glue, and Lake Formation.
  • Implement batch and streaming data pipelines using AWS Glue, Kinesis, and Apache Flink.
  • Use AWS orchestration and automation services such as Step Functions, Lambda, and Code Pipeline.
  • Apply data security and access control using AWS IAM, KMS, and Lake Formation policies.
  • Optimize performance and cost of data processing pipelines.
  • Design, implement, and manage data lakes on AWS.
  • Build scalable batch and streaming pipelines.
  • Secure and optimize data workflows using AWS services.
  • Automate orchestration and CI/CD for data engineering projects.
  • Apply AWS best practices to improve performance, governance, and compliance.
  • Deploy, monitor, and troubleshoot data workflows using AWS monitoring tools.

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Key Features:

  • Comprehensive Data Engineering Coverage

    • Covers core AWS data services including Glue, Kinesis, Redshift, Lake Formation, and S3.
    • Explores both batch and streaming architectures.

  • Hands-On Learning Experience

    • Includes multiple hands-on labs using AWS Console, Glue Studio, and Step Functions.
    • Real-world scenarios to practice data ingestion, transformation, and orchestration.

  • End-to-End Data Pipeline Implementation

    • Design, deploy, and optimize complete data engineering solutions from source to analytics.

  • Security and Compliance Integration

    • Learn to manage access, encryption, and compliance controls across AWS data workflows.

  • Practical Project Work

    • “A Day in the Life of a Data Engineer” capstone lab brings together all learning modules.

  • AWS-Certified Trainer Delivery

    • Delivered by AWS Authorized Instructors with real-world implementation experience.

Who should Attend?

  • Data Engineers and Data Architects
  • Data Scientists working with AWS data tools
  • Software Developers managing data pipelines
  • Cloud Engineers and Analytics Professionals
  • Technical Leads and Solution Architects building data ecosystems

Prerequisites:

We recommend that attendees of this course have:
  • Familiarity with basic machine learning concepts, such as supervised and unsupervised learning, regression, classification, and clustering algorithms.
  • Working knowledge of Python programming language and common data science libraries like NumPy, Pandas, and Scikit-learn.
  • Basic understanding of cloud computing concepts and familiarity with the AWS platform.
  • Familiarity with SQL and relational databases is recommended but not mandatory.
  • Experience with version control systems like Git is beneficial but not required.

    • Why choose CloudThat as your training partner?

    • Specialized AWS Focus: CloudThat specializes in cloud technologies, offering focused and specialized training programs. We are Authorized Trainers for Amazon. This specialization ensures in-depth coverage of AWS services, use cases, best practices, and hands-on experience tailored specifically for AWS.
    • Industry-Recognized Trainers: CloudThat has a strong pool of industry-recognized trainers certified by AWS. These trainers bring real-world experience and practical insights into the training sessions, comprehensively understanding how AWS is applied in different industries and scenarios.
    • Hands-On Learning Approach: CloudThat emphasizes a hands-on learning approach. Learners can access practical labs, real-world projects, and case studies that simulate actual AWS environments. This approach allows learners to apply theoretical knowledge in practical scenarios, enhancing their understanding and skill set.
    • Customized Learning Paths: CloudThat understands that learners have different levels of expertise and varied learning objectives. We offer customized learning paths, catering to beginners, intermediate learners, and professionals seeking advanced AWS skills.
    • Interactive Learning Experience: CloudThat's training programs are designed to be interactive and engaging. We utilize various teaching methodologies like live sessions, group discussions, quizzes, and mentorship to keep learners engaged and motivated throughout the course.
    • Placement Assistance and Career Support: CloudThat often provides placement assistance and career support services. This includes resume building, interview preparation, and connecting learners with job opportunities through our network of industry partners and companies looking for AWS Data Engineering Associates.
    • Continuous Learning and Updates: CloudThat ensures that our course content is regularly updated to reflect the latest trends, updates, and best practices within the AWS ecosystem. This commitment to keeping the content current enables learners to stay ahead in their AWS knowledge.
    • Positive Reviews and Testimonials: Reviews and testimonials from past learners can strongly indicate the quality of training provided. You can Check feedback and reviews about our AWS courses that can provide potential learners with insights into the effectiveness and value of the training.

    Course Outline: Download Course Outline

    • Role of a Data Engineer
    • Key functions of a Data Engineer
    • Data Personas
    • Data Discovery
    • AWS Data Services

    • Orchestration and Automation
    • Data Engineering Security
    • Monitoring
    • Continuous Integration and Continuous Delivery
    • Infrastructure as Code
    • Serverless Application Model
    • Networking Considerations
    • Cost Optimization Tools

    • Data lake introduction
    • Data lake storage
    • Ingest data into a data lake
    • Catalog data
    • Transform data
    • Server data for consumption

    Hands-on lab: Setting up a Data Lake on AWS

    • Open Table Formats
    • Security using AWS Lake Formation
    • Setting permissions with Lake Formation
    • Security and governance
    • Troubleshooting

    Hand-on lab: Automating Data Lake Creation using AWS Lake Formation Blueprints

    • Introduction to data warehouses
    • Amazon Redshift Overview
    • Ingesting data into Redshift
    • Processing data
    • Serving data for consumption

    Hands-on Lab: Setting up a Data Warehouse using Amazon Redshift Serverless

    • Monitoring and optimization options
    • Data optimization in Amazon Redshift
    • Query optimization in Amazon Redshift
    • Orchestration options

    • Authentication and access control in Amazon Redshift
    • Data security in Amazon Redshift
    • Auditing and compliance in Amazon Redshift

    Hands-on lab: Managing Access Control in Redshift

    • Introduction to batch data pipelines
    • Designing a batch data pipeline
    • AWS services for batch data processing

    • Elements of a batch data pipeline
    • Processing and transforming data
    • Integrating and cataloging your data
    • Serving data for consumption

    Hands-on lab: A Day in the Life of a Data Engineer

    • Optimizing the batch data pipeline
    • Orchestrating the batch data pipeline
    • Securing the batch data pipeline

    Hands-on lab: Orchestrating Data Processing in Spark using AWS Step Functions

    • Introduction to streaming data pipelines
    • Ingesting data from stream sources
    • Streaming data ingestion services
    • Storing streaming data
    • Processing Streaming Data
    • Analyzing Streaming Data with AWS Services

    Hands-on lab: Streaming Analytics with Amazon Managed Service for Apache Flink

    • Optimizing a streaming data solution
    • Securing a streaming data pipeline
    • Compliance considerations

    • Hands-on lab: Access Control with Amazon Managed Streaming for Apache Kafka

    Certification Details:

    • Participants will receive a CloudThat Course Completion Certificate for Data Engineering on AWS.
    • This training also prepares participants for the AWS Certified Data Engineer – Associate certification exam.

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    Course ID: 26264

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    FAQs:

    This course is designed for data engineers, architects, and developers working with AWS data services.

    You’ll gain hands-on experience with S3, Glue, Redshift, Kinesis, Lake Formation, Step Functions, and Apache Flink.

    Basic knowledge of AWS, Python, SQL, and data warehousing concepts is recommended.

    This is a 3-day instructor-led course with guided labs and real-world projects.

    Yes, multiple labs will cover building, optimizing, and securing AWS data pipelines.

    Data engineers with AWS expertise are in high demand, with average salaries ranging from ₹18–28 LPA (INR) depending on experience.

    Yes, you’ll earn a CloudThat Course Completion Certificate.

    AWS Data Engineer, Data Pipeline Developer, Cloud Data Architect, or Analytics Engineer.

    It’s an official AWS-authorized, lab-driven course aligned with real industry data workloads.

    Yes, you can advance to AWS Certified Data Engineer – Associate or AWS Machine Learning Specialty certifications.

    Enquire Now