Data Processing and Orchestration on AWS

Course Modules

Module 1: Introduction

• 1.1 Data Pipelines and Orchestration in AWS
• 1.2 Choosing the Right Service: An Overview of AWS Data Processing Services
  • 1.2.1 Data Warehousing: Redshift vs. Athena
  • 1.2.2 NoSQL Databases: DynamoDB
  • 1.2.3 Streaming Data Ingestion: Kinesis Firehose
  • 1.2.4 Data Lakes: Building and Managing with Lake Formation
• 1.3 Introduction to Multitenancy in Data Management
  • Key AWS Services for Multitenant Data Management
  • Security Considerations in a Multitenant Environment
• 1.4 Benefits of Using Best Practices

Module 2: Data Ingestion

• 2.1 Batch Data Ingestion with Amazon S3
• 2.2 Streaming Data Ingestion with Amazon Kinesis Data Firehose
  • 2.2.1 Delivery Streams and Transformations
  • 2.2.2 Buffering and Error Handling
• 2.3 Real-Time Data Ingestion with AWS Greengrass and IoT Core

Module 3: Data Storage and Management

• 3.1 Data Warehousing for Analytics: Redshift vs. Athena
  • 3.1.1 Redshift: Columnar Storage for Scalable Analytics
  • 3.1.2 Athena: Serverless Analytics on S3
• 3.2 NoSQL Data Storage: DynamoDB for Scalable Applications
• 3.3 Data Lake Management with AWS Lake Formation
  • 3.3.1 Data Lifecycle Management
  • 3.3.2 Access Control and Security

Module 4: Data Processing and Transformation

• 4.1 Serverless Data Processing with AWS Glue
  • 4.1.1 Data Catalog and Schema Management
  • 4.1.2 ETL Jobs with AWS Glue (Extract, Transform, Load)
• 4.2 Orchestrating Data Pipelines with AWS Step Functions
  • 4.2.1 Designing and Implementing Workflows
  • 4.2.2 Integrating AWS Services for Orchestration
• 4.3 Automating Data Processing with AWS Lambda
  • 4.3.1 Event-Driven Data Processing
  • 4.3.2 Scaling and Managing Lambda Functions

Module 5: Data Visualization and Analytics

• 5.1 Data Visualization Techniques and Tools
  • 5.1.1 Using Amazon QuickSight for Business Intelligence
  • 5.1.2 Integrating with Third-Party Visualization Tools
• 5.2 Analyzing Data with AWS Services
  • 5.2.1 Querying Data with Amazon Athena
  • 5.2.2 Processing Streams with Amazon Kinesis Analytics

Module 6: Security and Compliance

• 6.1 Implementing Security Best Practices
  • 6.1.1 Data Encryption at Rest and in Transit
  • 6.1.2 Access Control and Identity Management
• 6.2 Ensuring Compliance with AWS Services
  • 6.2.1 Monitoring and Auditing with AWS CloudTrail
  • 6.2.2 Managing Compliance Programs with AWS Artifact

Module 7: Best Practices and Optimization

• 7.1 Cost Optimization Strategies
  • 7.1.1 Choosing Cost-Effective Services
  • 7.1.2 Monitoring and Managing AWS Costs
• 7.2 Performance Tuning and Optimization
  • 7.2.1 Optimizing Data Storage and Retrieval
  • 7.2.2 Enhancing Data Processing Performance
• 7.3 Disaster Recovery and Business Continuity
  • 7.3.1 Implementing Backup and Recovery Solutions
  • 7.3.2 Designing for High Availability