Duration 2 days – 14 hrs
Overview
A practical, hands-on course that teaches you how to design, build, secure, and operate high-performance applications on Amazon DynamoDB. You’ll master data modeling for NoSQL workloads, choose the right capacity and indexing strategies, implement serverless patterns with Lambda, and optimize for cost, scale, and latency—backed by labs using the AWS Console, CLI, and SDKs.
Objectives
- Explain DynamoDB core concepts: tables, items, attributes, partitions, and consistency.
- Design single-table and multi-table schemas for real-world access patterns.
- Select and tune capacity modes (Provisioned vs On-Demand), auto scaling, and WCU/RCU sizing.
- Use GSIs/LSIs, PartiQL, TTL, and conditional writes effectively.
- Build serverless patterns with DynamoDB Streams, AWS Lambda, and EventBridge.
- Implement security with IAM, KMS encryption, VPC endpoints, and fine-grained access control.
- Apply Global Tables, backups/point-in-time recovery, and transactions for resilience.
- Monitor and troubleshoot with CloudWatch, CloudTrail, and performance diagnostics.
- Migrate and integrate using AWS DMS, data import/export, and SDKs.
- Optimize cost and performance using best practices and anti-patterns.
Audience
- Backend/Full-Stack Developers, Solution Architects, Data/Platform Engineers
- DevOps/Cloud Engineers building serverless or high-throughput apps
- Technical Leads and System Designers moving from relational to NoSQL
Pre- requisites
- Basic understanding of AWS services (EC2, IAM, Lambda helpful)
- Familiarity with JSON and at least one programming language (e.g., JavaScript/TypeScript, Python, or Java)
- Optional: prior exposure to relational databases to contrast modeling approaches
Course Content
Day 1 Foundations & Data Modeling
Introduction to DynamoDB
- NoSQL vs SQL: when to choose DynamoDB
- Tables, items, attributes; partitions & sort keys
- Consistency models, durability, latency expectations
Capacity & Throughput
- Provisioned vs On-Demand, WCUs/RCUs, burst capacity
- Auto scaling, throttling, hot partitions—diagnosis and mitigation
Indexing & Query Patterns
- Local Secondary Index (LSI) vs Global Secondary Index (GSI)
- Designing efficient queries and avoiding scans
- Composite keys, adjacency lists, and sparse indexes
Data Modeling Deep Dive
- Single-table design principles and trade-offs
- Access pattern discovery; entity-relationship to access-pattern mapping
- Common patterns: shopping carts, user profiles, time-series, leaderboards, session stores
Hands-On Labs (D1)
- Create tables; switch capacity modes; set auto scaling
- Define GSIs/LSIs; write/query using Console, CLI, and SDK
- Model and implement 2–3 real access patterns
Day 2 Operations, Serverless, Security & Optimization
Advanced Features
- PartiQL for SQL-like operations
- Conditional writes, optimistic locking, batch ops, TTL
- Transactions (ACID) and use cases
Streams & Event-Driven Architectures
- DynamoDB Streams internals and guarantees
- Integrations: AWS Lambda, EventBridge, Kinesis
- CQRS and materialized views with GSIs and Streams
Global Scale & Resilience
- Global Tables (multi-Region replication), conflict handling
- Backup & restore, PITR, disaster recovery strategies
Security & Governance
- IAM policies, least privilege, fine-grained access control
- Encryption at rest (KMS) and in transit; VPC endpoints; compliance considerations
- Auditing with CloudTrail
Monitoring, Troubleshooting & Cost Optimization
- CloudWatch metrics/alarms, contributor insights
- Hot partition analysis, latency tuning, adaptive capacity
- Cost levers: item size, indexes, patterns to avoid scans; architectural trade-offs
Integration & Migration
- AWS DMS, CSV imports/exports, data loaders
- Coexistence with RDS, Redshift, OpenSearch; analytics offloading
Hands-On Labs (D2) & Capstone
- Implement Streams → Lambda workflow
- Add transactions and conditional logic
- Monitoring dashboard & alarms
