Learn when and why to use DynamoDB, S3, ElastiCache, and OpenSearch. Explore data access patterns, consistency
models, lifecycle management, and caching strategies to make informed storage decisions for your applications.

Introduction to Data Stores in AWS

Match data stores to use cases

Explore AWS data services in Console

Compare data store characteristics

Data access patterns and consistency models

Options

Strongly Consistent Reads

Eventually Consistent Reads

Match consistency models to scenarios

Configure DynamoDB read consistency

Analyze access patterns

Data lifecycle management and caching strategies

Match lifecycle strategies to scenarios

Keep in Standard

Standard-IA after 30 days

Glacier after 90 days

Choosing lifecycle policies for data types

Identify caching opportunities

Choosing the Right AWS Data Store

Go hands-on with AWS's two most essential data services. Design scalable DynamoDB tables, master CRUD operations and indexes, and optimize S3 with storage classes, versioning, lifecycle policies, and replication

Introduction to Amazon DynamoDB

Identify partition key cardinality

Design DynamoDB table with partition key

Design table with composite primary key

DynamoDB CRUD operations and Query vs Scan

Choosing between Query and Scan operations

Understanding PutItem operations in DynamoDB

Preventing race conditions with conditions

DynamoDB indexes, streams, and optimization

Understanding differences between GSI and LSI

Choosing the right GSI design for email lookups

Designing tables with Local Secondary Indexes

S3 deep dive: storage, versioning, and replication

Selecting optimal S3 storage classes

Implementing automatic storage class transitions

Protecting data with S3 versioning

Building with DynamoDB and S3

Protect application data end-to-end. Classify PII and PHI, encrypt with KMS, manage secrets with Secrets
Manager and Parameter Store, sanitize logs, and implement multi-tenant isolation using IAM condition keys.

Data classification, encryption, and AWS KMS

Identifying PII and PHI in application data

AWS vs customer security responsibilities

When to use different encryption types

AWS Secrets Manager and Parameter Store

Selecting the right service for secret management

Understanding how applications access secrets

Understanding automatic credential rotation

Data sanitization and masking

Classifying data by masking technique

Understanding partial masking implementation

Preventing PII exposure in application logs

Multi-tenant data access patterns and IAM policies

Choosing isolation patterns for scenarios

Partition key design for tenant isolation

Understanding IAM condition keys for DynamoDB

Securing and Isolating Sensitive Data

Master the essential AWS data services that power modern applications. In this hands-on course, you'll learn when and why to use DynamoDB, S3, ElastiCache, and OpenSearch by matching each service to real-world access patterns and consistency requirements. You'll design scalable DynamoDB tables with high-cardinality partition keys, perform CRUD operations, and compare Query vs Scan to optimize performance and cost. On the S3 side, you'll implement lifecycle policies to automatically transition data through storage classes and use versioning to protect against accidental deletion. The course wraps up with securing sensitive data end-to-end — classifying PII and PHI, managing secrets with Secrets Manager and Parameter Store, applying data masking techniques, and implementing multi-tenant isolation using IAM condition keys. By the end, you'll be able to choose, build with, and secure AWS data stores for production applications.

Intermediate Python

Introduction to AWS Boto in Python

Introduction to Shell

Learn to choose, build with, and secure AWS data stores including DynamoDB and S3 through hands-on console exercises and real-world scenarios.

Using Data Stores in AWS

Website product images - accessed thousands of times daily by customers

Monthly financial reports - accessed 2-3 times per month by finance team

Marketing campaign videos - high traffic for 30 days, then minimal access

Application logs - accessed frequently for 7 days, then rarely for debugging

Employee records - accessed occasionally, must be kept for 7 years for compliance

Database backups - accessed only for disaster recovery (rarely)

Choosing lifecycle policies for data types

Using Data Stores in AWS

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