Cloud Data Engineering with AWS
Master the AWS ecosystem for modern data platforms. Work with S3, Redshift, Glue, and Kinesis to build production-ready cloud architectures with serverless data processing and infrastructure as code.
Comprehensive AWS Cloud Data Platform Training
Professional Development and Career Impact
Technical Expertise Areas
- AWS data lake architecture design and optimization
- Serverless data processing with Lambda and Step Functions
- Real-time analytics implementation using Kinesis
- Infrastructure as code with Terraform and CloudFormation
Industry Applications
- Design enterprise-grade cloud data platforms
- Implement cost-effective data processing solutions
- Build scalable analytics and machine learning pipelines
- Lead cloud migration and modernization projects
AWS Services and Professional Tools
Storage & Warehousing
- Amazon S3 data lake implementation
- Redshift data warehouse design
- DynamoDB NoSQL database
- RDS managed relational databases
Processing & Analytics
- AWS Glue ETL service
- EMR big data processing
- Kinesis streaming analytics
- Athena serverless queries
Infrastructure & Deployment
- Lambda serverless computing
- ECS containerized workloads
- CloudFormation infrastructure
- Terraform automation
Cloud Security and Compliance Standards
Security Framework Implementation
Application of AWS security best practices including IAM policies, VPC configurations, encryption at rest and in transit, and security monitoring using CloudTrail and GuardDuty services.
Cost Optimization Strategies
Implementation of cost-effective architectures using spot instances, reserved capacity, lifecycle policies, and automated resource scaling to optimize cloud spending while maintaining performance.
High Availability Design
Architecture patterns for fault-tolerant systems using multi-region deployment, automated backup strategies, and disaster recovery planning following AWS Well-Architected Framework principles.
Performance Monitoring
Comprehensive observability implementation using CloudWatch metrics, X-Ray tracing, and custom monitoring solutions for proactive performance management and troubleshooting.
Target Audience and Technical Prerequisites
Ideal Candidates
-
Cloud Infrastructure Engineers
Professionals transitioning to specialized data platform engineering roles
-
Software Developers
Developers seeking expertise in cloud-native data architecture and processing
-
Data Platform Engineers
Engineers looking to specialize in AWS ecosystem for data engineering
Required Background
Technical Prerequisites
- Basic cloud computing concepts and AWS fundamentals
- Programming experience with Python or Java
- Understanding of database systems and SQL
- Familiarity with JSON, REST APIs, and web services
Assessment Methods and Progress Evaluation
Cloud Architecture Projects
Design and deploy complete data platforms on AWS with cost analysis, security reviews, and performance benchmarking.
Infrastructure as Code
Implementation of Terraform modules and CloudFormation templates with version control and automated deployment pipelines.
AWS Certification Prep
Structured preparation for AWS Certified Data Analytics and Solutions Architect Professional certification exams.
Master AWS Cloud Data Engineering
Join professionals building the future of data infrastructure on AWS. Develop expertise in cloud-native data platforms and advance your career with in-demand skills.
This comprehensive course provides in-depth training on Amazon Web Services ecosystem for building modern, scalable data platforms. Students learn to architect and implement cloud-native data solutions using AWS services including S3 data lakes, Redshift data warehouses, Glue ETL services, and Kinesis streaming analytics.
The curriculum covers cost-effective architecture design, security best practices, and performance optimization for large-scale data workloads. Participants gain hands-on experience with serverless data processing using Lambda functions, container-based solutions with ECS, and infrastructure as code implementation using Terraform and CloudFormation.
Through practical projects, students build production-ready data platforms incorporating comprehensive monitoring, automated scaling, and disaster recovery strategies that meet enterprise requirements for reliability and compliance.