AWS DynamoDB

Course Description

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