AWS Database Services Comparison

Open Dash Tutorial

Comparing RDS vs DynamoDB vs Redshift

Hello and welcome back to Open Dash! In this tutorial, we'll compare AWS database services and clarify where and how to use each for your cloud-native architectures.

Amazon RDS

SQL / Relational

OLTP Workloads

Amazon DynamoDB

NoSQL / Key-Value

Real-time Applications

Amazon Redshift

Data Warehouse

OLAP Workloads

AWS Database Services Tutorial | Page 1 of 18

AWS Database Services Overview

Understanding the AWS Database Ecosystem

Choosing the Right Database Service

Many developers get confused when choosing between AWS database services, especially when starting with cloud-native architectures. Let's break down the primary options:

Amazon RDS

Type: SQL / Relational

Traditional relational database service with full SQL support

Best For:

OLTP workloads
Structured data with relationships
Applications requiring ACID transactions

Amazon DynamoDB

Type: NoSQL / Key-Value & Document

Serverless, fully managed NoSQL database service

Best For:

Real-time, low-latency applications
Serverless architectures
High-throughput workloads

Amazon Redshift

Type: Data Warehouse / Columnar

Fully managed, petabyte-scale data warehouse service

Best For:

OLAP workloads
Business intelligence & analytics
Complex queries across large datasets

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Amazon RDS (Relational Database Service)

SQL / Relational Database Solution

What is Amazon RDS?

Amazon RDS is a managed relational database service that makes it easy to set up, operate, and scale a relational database in the cloud. It provides cost-efficient and resizable capacity while automating time-consuming administration tasks.

Ideal Use Cases:

Online Transaction Processing (OLTP), booking systems, point-of-sale systems, finance applications

Key Features

Managed Relational Databases

Supports MySQL, PostgreSQL, SQL Server, Oracle, MariaDB, and Aurora

High Availability Options

Single-AZ (cost-effective) or Multi-AZ (auto failover, high availability)

Read Replicas

For read-heavy workloads, analytics, or reporting (same/cross-region)

Security & Backups

VPC integration, security groups, automated backups, and snapshots

RDS Architecture

AWS Region

Availability Zone 1

Primary DB

Availability Zone 2

Standby DB

Automatic failover in Multi-AZ deployments

Synchronous data replication for high durability

Supported Database Engines

MySQL

PostgreSQL

SQL Server

Oracle

MariaDB

Aurora

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Amazon RDS Deep Dive

Deployment Options & High Availability

RDS Deployment Architecture

Single-AZ

Single-AZ Deployment

Cost-effective option with a single database instance in one Availability Zone.

Primary DB Instance

Availability Zone A

Consideration: Potential downtime during maintenance or failures

Multi-AZ

Multi-AZ Deployment

High availability option with automatic failover to standby instance.

Primary DB

Availability Zone A

Sync
Replication

Standby DB

Availability Zone B

Benefit: Automatic failover, enhanced availability, better durability

Read Replicas

Improve read performance by offloading read queries to replicas. Can be created in the same region or cross-region.

Primary DB

Writes

Async
Replication

Read Replica

Reads

Read Replica

Reads

Read Replica

Reads

Security

Lives inside a VPC with network isolation
Security Groups control traffic access
IAM authentication for database access
Encryption at rest and in transit
Database-level access controls (e.g., scott/tiger in Oracle)

Backups & Snapshots

Automated backups with configurable retention (1-35 days)
Manual snapshots (retained until explicitly deleted)
Point-in-time recovery (PITR)
Incremental backups stored in S3 (cost-efficient)
Snapshot sharing across AWS accounts

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Amazon DynamoDB

NoSQL Database Service

What is Amazon DynamoDB?

Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. It's a key-value and document database that can handle any scale of application with consistent, single-digit millisecond latency.

Ideal Use Cases:

Real-time applications, serverless workloads, IoT systems, gaming leaderboards, session management

Key Features

Fully Managed Service

No servers to provision, patch, or manage; AWS handles all of this for you

High Performance at Scale

Single-digit millisecond latency at any scale, handling millions of requests per second

Multi-AZ by Default

Data automatically replicated across multiple AZs for high durability

Zero Storage Administration

No need to define disk size; storage automatically scales with your data

Data Model & Architecture

Primary Key Types

Simple Primary Key

Partition Key (Hash Key) only

Composite Primary Key

Partition Key + Sort Key

Example DynamoDB Table

UserId (Partition Key)	GameId (Sort Key)	Attributes
user_123	game_1	Score: 94, Level: 5
user_123	game_2	Score: 82, Level: 3
user_456	game_1	Score: 77, Level: 4

Partitioning Architecture

Data distributed across partitions based on the Partition Key

Partition 1

user_123

Partition 2

user_456

Partition 3

user_789

Capacity Modes

Provisioned Capacity

You specify read and write capacity units in advance. Best for predictable workloads.

On-Demand Capacity

Pay-per-request model with no capacity planning. Best for variable or unpredictable workloads.

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DynamoDB Advanced Features

Amazon DynamoDB Advanced Features

High Availability & Global Distribution

Global Tables

DynamoDB Global Tables provide a fully managed, multi-region, multi-active database solution for building globally distributed applications with low-latency data access.

US-EAST (N. Virginia)

DynamoDB Table

Multi-AZ Replicated

Multi-Region
Replication

EU-WEST (Ireland)

DynamoDB Table

Multi-AZ Replicated

Benefits: Global read/write access with low latency, built-in conflict resolution, and regional fault tolerance

Time-to-Live (TTL)

Automatically expire and delete items based on a timestamp attribute. Perfect for session data, temporary data, or logs that should be automatically removed after a certain time.

DynamoDB Streams

Capture item-level changes in your tables and send change records in near real-time to Lambda for event-driven processing, or for replication, analytics, and more.

DAX (DynamoDB Accelerator)

In-memory cache designed specifically for DynamoDB that delivers microsecond response times for read-heavy workloads. Access DynamoDB through the DAX client for up to 10x performance improvement.

Transactions

Coordinate all-or-nothing changes across multiple items within and across tables. Provides atomicity, consistency, isolation, and durability (ACID) capabilities.

Popular DynamoDB Use Cases

Gaming

Leaderboards, player data, game state, and session management with low-latency at any scale

IoT Applications

Device metadata, sensor data storage, and real-time event processing

Serverless Apps

Perfect complement to Lambda functions for truly serverless, scalable applications

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RDS vs DynamoDB Comparison

RDS vs DynamoDB: Side-by-Side Comparison

Choosing the Right Database for Your Workload

Key Differences at a Glance

Feature	Amazon RDS	Amazon DynamoDB
Database Type	Relational (SQL)	NoSQL (Key-Value & Document)
Performance	Good for complex transactional workloads	Single-digit millisecond latency at any scale
Scalability	Vertical scaling (instance size) with read replicas	Horizontal scaling with unlimited throughput capacity
Administration	Managed, but requires some administration (DB parameters, patching schedules)	Fully managed serverless experience with minimal administration
Data Model	Structured schema with tables, columns, and relationships	Schema-less with flexible attributes (requires partition key)
Query Capabilities	Complex joins, aggregations, subqueries	No joins, limited to key-based access patterns
Serverless	No (except Aurora Serverless)	Yes
Global Distribution	Read replicas across regions	Global Tables with multi-region replication

When to Choose What?

Choose RDS when:

You need SQL features (joins, transactions, complex queries)
You're migrating a legacy or existing relational app
Your data has complex relationships that benefit from a normalized schema
You need ACID compliance for financial or transactional integrity

Choose DynamoDB when:

You want serverless, auto-scaled architecture
Your app is high-traffic, real-time, and latency-sensitive
You need high availability without managing infrastructure
Your access patterns are simple and known in advance

Common Use Case Scenarios

RDS Ideal For:

E-commerce platforms, content management systems, financial applications, ERP systems

DynamoDB Ideal For:

Mobile backends, gaming leaderboards, IoT data, session stores, real-time analytics

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Amazon Redshift: Data Warehousing on AWS

Amazon Redshift

Data Warehousing on AWS

What is Amazon Redshift?

Amazon Redshift is a fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to analyze all your data using standard SQL and your existing business intelligence tools. It is optimized for Online Analytical Processing (OLAP) workloads and is designed to handle large-scale data analysis and complex queries.

Ideal Use Cases:

Business intelligence, reporting and dashboarding, complex aggregations and joins across large volumes of data

Columnar Architecture

Redshift Cluster

Leader Node

Query Planning & Aggregation

Compute Nodes

Compute Node 1

Compute Node 2

Compute Node 3

Column 1

Column 2

Column 3

Column 4

Columnar Storage Format

Key Features

Columnar Storage

Stores data by columns instead of rows, making aggregations and scanning operations faster for analytics

Massively Parallel Processing (MPP)

Distributes query execution across multiple nodes, reducing query time drastically

Data Compression

Automatically applies compression algorithms column-wise, reducing storage costs and improving I/O performance

Concurrency Scaling

Automatically adds extra capacity to handle bursts of queries without performance degradation

Data Loading & Integration

Amazon S3

via COPY command

AWS Glue

ETL/ELT jobs

Amazon Kinesis

Real-time data

AWS DMS

Database migration

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Amazon Redshift Advanced Features

Performance Optimization & Integrations

Performance Optimization

Sort Keys

Define how data is sorted on disk. Improves query performance by minimizing data scanned and enabling efficient range filtering.

Distribution Keys

Controls how data is distributed across nodes. Aligning with join keys minimizes data movement between nodes during query execution.

Materialized Views

Pre-compute and store complex query results for faster access. Redshift automatically maintains and refreshes these views as underlying data changes.

Concurrency Scaling

Automatically adds cluster capacity to handle increases in concurrent queries, maintaining consistent performance during peak usage periods.

Redshift Spectrum

Redshift Tables

Internal data

JOIN

Amazon S3

External data

How It Works: Query data directly in S3 without loading it into Redshift.

Run federated queries across Redshift and S3
Support for formats like Parquet, ORC, JSON, CSV
Apply schema-on-read to semi-structured data

Security & Access

IAM roles for secure data access from S3 or Glue
VPC isolation with security groups
Encryption at rest and in transit
KMS integration for key management
Fine-grained access control via SQL-level permissions
Column-level security for sensitive data

Backup & Recovery

Automated snapshots taken every 8 hours by default
Manual snapshots for planned changes
Configurable retention periods
Cross-region snapshot copy for disaster recovery
Point-in-time recovery capabilities
Automated snapshot management through AWS Backup

Regional Scope & Node Types

Region-specific: Redshift clusters operate within a single AWS region

RA3 Nodes: Scale compute and storage independently for flexibility

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Comprehensive AWS Database Comparison

Comprehensive Database Service Comparison

RDS vs DynamoDB vs Redshift: Making the Right Choice

Comparing AWS Database Services

Choose the right database service based on your specific workload requirements and use cases

Features	Amazon RDS	Amazon DynamoDB	Amazon Redshift
Database Type	Relational (SQL)	NoSQL (Key-Value & Document)	Data Warehouse (Columnar)
Primary Workload	OLTP (Online Transaction Processing)	Real-time applications, high throughput	OLAP (Online Analytical Processing)
Scalability	Vertical scaling with read replicas	Unlimited horizontal scaling (serverless)	MPP architecture, scale by adding nodes
Performance	Millisecond response times	Single-digit millisecond latency	Optimized for complex queries over large datasets
Query Complexity	Complex SQL, joins, subqueries	Limited to key-based access	Complex analytical queries
Serverless Option	Aurora Serverless only	Fully serverless	No
Administration	Managed service (some administration)	Zero administration	Managed service (requires tuning)
Capacity Planning	Instance size & storage planning	On-demand or provisioned capacity	Node type & count planning
Cost Model	Hourly instance charges + storage	Pay per request or provisioned capacity	Hourly node charges + storage
Global Distribution	Cross-region read replicas	Global Tables (multi-active)	Cross-region snapshots only

When to use RDS:

Traditional web applications
ERP, CRM, e-commerce systems
Applications requiring ACID compliance

When to use DynamoDB:

Mobile/web backends requiring high scale
Serverless applications with Lambda
IoT, gaming leaderboards, session stores

When to use Redshift:

Business intelligence & reporting
Log analysis at scale
Historical data aggregation & trends

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AWS Database Services: Real-World Architectures

Common Patterns & Implementation Examples

Common Architectural Patterns

Explore real-world architectures leveraging AWS database services for specific use cases

Multi-Tier Web Application

Traditional web application with separated presentation, business logic, and data layers

Architecture Components:

EC2 Web Tier → EC2 App Tier → RDS Multi-AZ

Key Benefits:

Full SQL capability for complex business rules
Automatic failover with Multi-AZ deployment
Read replicas for read-heavy workloads
Familiar development model for existing teams

Example Use Case:

E-commerce platform with product catalogs, customer accounts, and order processing

Serverless Microservices

Event-driven, scalable architecture with no server management

Architecture Components:

API Gateway → Lambda Functions → DynamoDB

Key Benefits:

Auto-scaling to match any traffic pattern
Pay-per-request pricing (no idle costs)
Single-digit millisecond response times
Global tables for multi-region availability

Example Use Case:

Mobile app backend for a social media platform with user profiles, activity feeds, and notifications

Analytics Pipeline

Data warehouse solution for business intelligence and reporting

Architecture Components:

S3 Data Lake → Glue ETL → Redshift

Key Benefits:

Columnar storage optimized for analytics
Massively parallel processing architecture
SQL interface compatible with existing BI tools
Redshift Spectrum for querying data in S3

Example Use Case:

Retail company analyzing sales trends, customer behavior, and inventory optimization across thousands of stores

Multi-Database Architectures

Modern applications often combine multiple database services for different workloads:

CQRS Pattern

Use RDS for write operations (commands) and DynamoDB for read operations (queries) to optimize for different access patterns

Data Pipeline Pattern

Operational data in RDS/DynamoDB with periodic ETL into Redshift for analytics and reporting

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Database Migration and Integration Strategies

Moving to AWS Database Services Efficiently

Migration Approaches for Different Databases

Strategies to efficiently migrate your current databases to AWS services

To Amazon RDS

Schema migration with AWS SCT
Data migration using AWS DMS
Validate data and cut over

AWS DMS

AWS SCT

Native Tools

Pros:

Minimal code changes
Familiar SQL interface
Zero downtime options

Cons:

Schema dependencies
Storage planning

To DynamoDB

Design access patterns first
Model NoSQL schema (denormalize)
Batch import with custom ETL

Lambda

DMS

Data Pipeline

Pros:

Infinite scaling
Simplified operations
Performance at scale

Cons:

Data modeling changes
Query pattern limitations

To Redshift

Export data to S3 in chunks
Define schema with sort/dist keys
Load with COPY commands

Glue

COPY Command

Pros:

Analytical performance
SQL compatibility
Parallel processing

Cons:

Requires optimization
Not for OLTP workloads

Common Integration Patterns

Event-Driven Integration

Use DynamoDB Streams or RDS with AWS DMS to trigger Lambda functions for real-time processing and cross-service updates.

ETL Integration

AWS Glue jobs to transform and move data between services, such as from operational databases to Redshift for analysis.

API-Based Integration

Create microservices with API Gateway and Lambda to provide unified access to different database services.

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Cost Optimization and Performance Best Practices

Getting the Most from Your AWS Database Services

Database Cost Optimization Strategies

Implement these practices to optimize costs while maintaining performance

Amazon RDS

Right-size your instances

Monitor CloudWatch metrics and adjust instance sizes to match actual workload needs

Potential savings: High
Reserved Instances

Purchase Reserved Instances for predictable workloads to receive significant discounts

Potential savings: Very High
Storage optimization

Use gp3 volumes when possible and regularly clean up unused snapshots

Amazon DynamoDB

Choose the right capacity mode

Use on-demand for variable traffic and provisioned with auto-scaling for predictable traffic

Potential savings: High
Reserved Capacity

Purchase reserved capacity for stable, predictable workloads
Optimize TTL & item size

Configure TTL for temporary data and keep item sizes small

Amazon Redshift

Use RA3 nodes with managed storage

Scale compute and storage independently to optimize costs
Leverage pause and resume

Pause clusters during idle periods to reduce costs

Potential savings: Very High
Optimize storage with compression

Use appropriate compression encodings for columns

Performance Best Practices

RDS Performance

Optimize your queries with proper indexing
Use read replicas for read-heavy workloads
Tune instance parameters specific to workload

DynamoDB Performance

Design for uniform key distribution
Use DAX for caching frequent reads
Consider sparse indexes for selective queries

Redshift Performance

Define optimal sort and distribution keys
Use materialized views for common queries
Run VACUUM and ANALYZE regularly

Pro Tip: Use AWS Cost Explorer & Trusted Advisor

Monitor database costs, identify savings opportunities, and receive recommendations to optimize resource utilization and costs

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Security Best Practices for AWS Database Services

Protecting Your Data in the Cloud

Database Security Fundamentals

Implement these foundational security practices across all AWS database services

Encryption

Enable encryption at rest and in transit for all database services

Access Controls

Implement least privilege IAM permissions and service-specific access controls

Network Security

Use VPC, security groups, and network ACLs to restrict network access

Monitoring & Auditing

Enable CloudTrail, AWS Config, and service-specific logging

Amazon RDS Security

Use Security Groups

Restrict inbound access to specific CIDR ranges and security groups
Enable SSL/TLS for connections

Force all database connections to use encryption in transit
Implement IAM Database Authentication

Use IAM roles and users for database authentication
Enable Automated Backups

Configure backups with appropriate retention periods

Security Note: Rotate database credentials regularly and avoid hardcoding them in application code

DynamoDB Security

Fine-grained access control

Use IAM policies with conditions to restrict access to specific items and attributes
VPC Endpoints

Use VPC endpoints to access DynamoDB without traversing the public internet
Enable Point-in-time Recovery

Protect against accidental writes or deletes with continuous backups
Use CMKs for enhanced encryption

Utilize customer-managed KMS keys for more control over encryption

Security Note: When using Global Tables, ensure IAM policies account for multi-region resources

Redshift Security

Configure cluster encryption

Enable encryption at rest for the entire cluster and specify KMS key
Enhanced VPC Routing

Enable to ensure traffic between your cluster and data repositories flows through your VPC
Column-level access controls

Restrict access to sensitive columns using view-based access control
Audit logging

Enable audit logging to track connection attempts, queries, and changes

Security Note: Regularly audit user permissions and rotate admin credentials

Good vs. Bad Security Practices

Recommended Security Practices

Enforce least privilege access at all times
Use parameter/secret stores for credentials
Encrypt data both at rest and in transit
Implement VPC endpoints for private communications
Monitor and alert on suspicious activities

Security Anti-Patterns to Avoid

Using public access for database instances
Hardcoding credentials in application code
Overly permissive IAM policies and security groups
Disabling encryption features to save costs
Neglecting regular security audits and updates

Security is Everyone's Responsibility

Remember that AWS provides the tools but security implementation follows the shared responsibility model. You are responsible for security in the cloud while AWS is responsible for security of the cloud.

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Decision Framework & Best Practices

How to Choose the Right Database Service for Your Workload

Database Selection Framework

1. Define Your Workload Type

Transactional (OLTP)

Analytical (OLAP)

Real-time/High-throughput

2. Assess Data Structure Requirements

Relational (tables with joins)

Non-relational (document, key-value)

3. Consider Operational Requirements

Scalability needs

Global distribution

Managed vs. serverless

4. Match to Service

RDS DynamoDB Redshift

Common Challenges & Solutions

Cost Management Surprises

Unexpected costs from over-provisioning or inefficient usage

Solutions:

Use AWS Cost Explorer to monitor and analyze costs
Implement auto-scaling for dynamic workloads
Consider reserved capacity for predictable workloads

Performance Bottlenecks

Slow queries, throttling, or inefficient data access patterns

Solutions:

Optimize schema design and indexing strategy
Implement caching (DAX for DynamoDB, ElastiCache)
Monitor performance metrics and adjust accordingly

Data Migration Complexity

Challenges in moving data between database types

Solutions:

Use AWS Database Migration Service (DMS)
Implement staged migration strategies
Design for future data growth and access patterns

Best Practice: Purpose-Built Database Strategy

Modern applications often benefit from using multiple database services together, each optimized for specific workloads:

RDS for Transactional Data

Primary records, financial transactions, normalized data models

DynamoDB for High-Velocity Data

User sessions, real-time data, high-throughput access patterns

Redshift for Analytics

Historical aggregations, reporting, data warehousing needs

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Future Trends & Advanced Database Patterns

Emerging Technologies and Best Practices in AWS Database Services

Emerging Trends in AWS Database Services

The future of AWS databases is evolving rapidly with these key developments

Serverless Expansion

Beyond Aurora Serverless and DynamoDB, expect more serverless database options with automatic scaling and pay-per-use models across AWS database portfolio

Coming soon: Enhanced serverless analytics and more granular resource control

ML Integration

Machine learning capabilities embedded directly within database services for intelligent query optimization, anomaly detection, and predictive scaling

Watch for: Enhanced integration between Amazon SageMaker and database services

Quantum-Ready Databases

Preparation for quantum computing's impact on cryptography and database processing with quantum-resistant encryption and optimized algorithms

Research focus: Amazon Braket integration with data processing workflows

Advanced Database Design Patterns

Sophisticated architectural approaches leveraging AWS database services

Polyglot Persistence

Using multiple database types for different data needs within a single application

Example Implementation:

RDS for transactional data
DynamoDB for session management
Redshift for analytics
ElastiCache for real-time data

Event-Driven Architecture

Using database change events to trigger downstream processes and maintain data consistency

Example Implementation:

DynamoDB Streams with Lambda
RDS with CDC to Kinesis
Event Bridge integrations
Asynchronous microservices communication

Multi-Model Databases

Leveraging services that support multiple data models to simplify architecture

Example Implementation:

Amazon DocumentDB for document and graph
Amazon Neptune for graph and RDF
DynamoDB for key-value and document
Aurora with multi-model extensions

Stay Current with AWS Database Innovation

Follow AWS Database Blog, attend re:Invent sessions, and join AWS database webinars to learn about the latest features and best practices

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Summary & Next Steps

Key Takeaways and Implementation Guidance

Key Takeaways

Amazon RDS

Choose for relational data needs with SQL compatibility
Best for apps requiring ACID compliance & complex queries
Consider Multi-AZ for high availability production workloads

Amazon DynamoDB

Select for serverless, scalable NoSQL requirements
Perfect for high-throughput, low-latency workloads
Design access patterns carefully before implementation

Amazon Redshift

Ideal for data warehousing and complex analytics
Optimize with proper sort/distribution keys
Consider cost optimization through pausing idle clusters

Implementation Roadmap

Next Steps

Assess your workload requirements

Determine your data structure needs, access patterns, scalability requirements, and performance SLAs

Create proof-of-concept deployments

Test your specific use cases with sample data in each relevant database service

Implement monitoring and cost controls

Set up CloudWatch alerts, performance monitoring, and budget thresholds before scaling

Establish backup and disaster recovery

Configure appropriate backup schedules, retention policies, and cross-region strategies

Avoid These Common Pitfalls

Over-provisioning resources

Start small and scale as needed, especially for DynamoDB provisioned capacity
Ignoring data access patterns

DynamoDB and Redshift performance depend heavily on understanding your access patterns
Neglecting security best practices

Always implement encryption, least-privilege access, and proper VPC controls
Forcing a single database for all workloads

Consider purpose-built database strategy for complex applications

Recommended Resources

Official Documentation

AWS Database Documentation
Database Blog & Whitepapers

Training & Workshops

AWS Database Workshops
Database Specialty Certification

Tools & Utilities

AWS Database Migration Service
Database Cost Explorer

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Thank You & Next Steps

Thank You for Watching!

AWS Database Services Comparison: RDS vs DynamoDB vs Redshift

We've Covered A Lot Today!

Thank you for joining us on this journey through AWS database services. We hope this comparison helps you make the right choices for your cloud architecture.

Multi-AZ RDS Setup

Learn how to configure high-availability RDS databases

DynamoDB Global Tables

Create multi-region, low-latency database setups

Real-time Leaderboards

Implement gaming leaderboards with DynamoDB

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