★★★★★
"The structure, the practice exams, the instructor — all top tier. Passed first try."
AS
Aarti SharmaSenior Project Manager · TCS
— The journey
Course Outline
What the programme covers, module by module.
Module 1: Introduction to AWS Data Analytics
- Cloud analytics concepts
- AWS analytics ecosystem
- Data workloads
- Batch analytics
- Streaming analytics
- Modern data architectures
Module 2: AWS Analytics Architecture
- Data sources
- Data ingestion
- Storage layers
- Processing layers
- Analytics layers
- Visualization layers
Module 3: AWS Identity & Access for Analytics
- IAM fundamentals
- Users and roles
- Policies
- Service permissions
- Least privilege
- Access management
Module 4: Amazon S3 for Data Analytics
- S3 fundamentals
- Buckets and objects
- Storage classes
- Data organization
- Partitioning concepts
- Analytics storage
Module 5: Building Data Lakes on AWS
- Data lake architecture
- Raw data zones
- Processed data zones
- Curated datasets
- Data organization
- Data lake workflows
Module 6: AWS Glue Fundamentals
- AWS Glue architecture
- Data Catalog
- Crawlers
- ETL jobs
- Data discovery
- Metadata management
Module 7: Data Transformation with AWS Glue
- ETL workflows
- Data cleansing
- Data transformation
- Job configuration
- Job monitoring
- Pipeline optimization
Module 8: Amazon Athena
- Serverless querying
- SQL analytics
- External tables
- Data partitioning
- Query optimization
- Cost considerations
Module 9: Amazon Redshift Fundamentals
- Data warehousing concepts
- Redshift architecture
- Databases and schemas
- Tables
- Data loading
- Query processing
Module 10: Amazon Redshift Advanced Analytics
- Distribution strategies
- Sort strategies
- Workload management
- Query optimization
- Redshift Spectrum
- Performance monitoring
Module 11: Data Modelling for Analytics
- Dimensional modelling
- Fact tables
- Dimension tables
- Star schemas
- Data relationships
- Analytics model design
Module 12: Amazon EMR
- Big data processing
- EMR architecture
- Cluster concepts
- Apache Spark
- Distributed processing
- Data transformation
Module 13: Apache Spark on AWS
- Spark fundamentals
- DataFrames
- Transformations
- Actions
- Distributed analytics
- Performance considerations
Module 14: Streaming Data Fundamentals
- Streaming concepts
- Event-driven data
- Real-time analytics
- Producers and consumers
- Streaming architecture
- Processing patterns
Module 15: Amazon Kinesis
- Kinesis Data Streams
- Data ingestion
- Stream processing
- Shards
- Consumers
- Real-time pipelines
Module 16: Amazon Managed Streaming for Apache Kafka
- Kafka fundamentals
- Amazon MSK
- Topics
- Partitions
- Producers
- Consumers
Module 17: AWS Lambda for Data Processing
- Serverless computing
- Lambda functions
- Event triggers
- Data processing
- Workflow automation
- Analytics use cases
Module 18: Data Pipeline Orchestration
- Workflow orchestration
- AWS Step Functions
- Event-driven workflows
- Pipeline dependencies
- Error handling
- Scheduling concepts
Module 19: Amazon QuickSight
- Business intelligence concepts
- Data sources
- Datasets
- Visualizations
- Dashboards
- Interactive analytics
Module 20: Data Governance on AWS
- Data governance principles
- Metadata management
- Data ownership
- Data classification
- AWS Lake Formation
- Governance workflows
Module 21: Data Security
- Encryption concepts
- AWS KMS
- Data access controls
- Network security
- Secure storage
- Security best practices
Module 22: Data Quality & Validation
- Data profiling
- Quality checks
- Missing data
- Duplicate detection
- Validation rules
- Data reliability
Module 23: Monitoring AWS Analytics Workloads
- Amazon CloudWatch
- Logs
- Metrics
- Alerts
- Pipeline monitoring
- Troubleshooting
Module 24: Analytics Performance Optimization
- Query performance
- Data partitioning
- File formats
- Compression
- Resource optimization
- Workload tuning
Module 25: Cost Optimization for Analytics
- AWS pricing concepts
- Storage optimization
- Compute optimization
- Query cost management
- Resource utilization
- Cost monitoring
Module 26: Machine Learning Integration
- Analytics and machine learning
- Amazon SageMaker concepts
- Feature preparation
- Data pipelines
- Model integration
- Analytics use cases
Module 27: Generative AI for Data Analytics
- Generative AI concepts
- Amazon Bedrock overview
- Natural-language analytics
- Data summarization
- Analytics assistance
- Responsible AI usage
Module 28: Designing Scalable Analytics Solutions
- Architecture selection
- Scalability
- Availability
- Reliability
- Performance
- Cost efficiency
Module 29: AWS Analytics Best Practices
- Architecture reviews
- Security practices
- Data governance
- Performance optimization
- Operational excellence
- Maintainable solutions
Module 30: Practical AWS Data Analytics Project
- Data ingestion
- Data lake configuration
- ETL pipeline development
- Analytics queries
- Dashboard development
- Solution architecture
— 01.2 · Is it right for you?
Who it's for & what's included
Pick a delivery method to see exactly who it suits and everything you receive.
Who it's for
Classroom
Best for learners who want face-to-face tuition and to network with peers in person.
What's included
Everything you get
- ✓ Live instructor on-site
- ✓ Printed workbook & materials
- ✓ Group exercises & case studies
Who it's for
Online Instructor-Led
Best for learners who want a live instructor and a fixed schedule, without the travel.
What's included
Everything you get
- ✓ Live instructor via video call
- ✓ Digital workbook & resources
- ✓ Session recordings
Who it's for
Self-Paced
Best for self-motivated learners who need maximum flexibility around work and life.
What's included
Everything you get
- ✓ On-demand video lessons
- ✓ Interactive quizzes
- ✓ 24/7 access on any device