---
title: CTP Topic 51 Architecting with AWS purpose-built databases
type: cloud-learning
source-type: video
category: DevOps & SRE/01_AWS-Landing-Zone
tags:
  - AWS
  - Database
  - Purpose-Built
  - CTP
date-added: 2026-04-14
video-source: nas:///volume2/work/Public Cloud Learning Sessions/CTP _ Topic 51_ Architecting with AWS purpose-built databases.mp4
audio-source: ""
status: summarized (Gemini 摘要)
---

# CTP Topic 51 Architecting with AWS purpose-built databases

**Source:** NAS `/volume2/work/Public Cloud Learning Sessions/CTP _ Topic 51_ Architecting with AWS purpose-built databases.mp4`

**Type:** VIDEO | **Category:** 01_AWS-Landing-Zone

**Status:** 🟡 Awaiting Whisper transcription → Summary

---

## 摘要

> ## Architecting with AWS Purpose-Built Databases

Femi George, a database sales specialist from AWS, discussed purpose-built databases for modern applications, covering modern applications, the rationale for purpose-built databases, key AWS databases, and the evolving role of DBAs/developers in the cloud.

Modern applications have evolved from client-server models due to changing customer requirements, new devices, diverse data types, and economic considerations. Key questions include scalability, global delivery with low latency, and developer access. The approach involves starting with the use case and selecting the best tool for the job, avoiding a one-size-fits-all approach. *We need to start thinking of the right purpose built database for the right application.*

Considerations for purpose-built databases include application scale, user numbers, access patterns, usage spikes, and performance requirements like latency and availability. Duolingo uses DynamoDB for personalized data, ElastiCache for common words/phrases, and Aurora for transactional data. AWS offers a range of purpose-built databases, including relational (e.g., RDS, Aurora) and NoSQL (key-value, document, in-memory, graph) options, along with time series, ledger, and wide-column databases.

Relational databases are suitable for fixed schemas and maintaining referential integrity. Amazon RDS provides fully managed traditional and open-source databases, handling backups and patching. Data endpoints in RDS facilitate easy application access. Amazon Aurora, a cloud-native database, offers MySQL and PostgreSQL compatibility with enhanced performance, scalability, and security. *Amazon Aurora has two flavors, MySQL and PostgreSQL.* Aurora separates storage and compute, improving IO and availability.

Key-value data is popular among developers and forms the basis of NoSQL databases. Amazon DynamoDB is a key-value and document database with single-digit millisecond performance at any scale, supporting trillions of requests per day. Netflix uses DynamoDB for resilience and low-latency access to JSON documents. Document databases extend key-value stores by enabling deeper querying within JSON files. Amazon DocumentDB is compatible with MongoDB and offers flexible schemas.

Apache Cassandra, a wide-column database, is used for large-scale applications with unstructured schemas. Amazon Keyspaces is a managed service for Cassandra-compatible databases, offering serverless options. In-memory databases, like Amazon ElastiCache (Redis, Memcached), are used for caching, media streaming, session stores, and real-time analytics. Peloton uses ElastiCache Redis for immediate feedback to customers.

Graph databases (e.g., Amazon Neptune) are suitable for fraud detection, social networking, and recommendations. They help uncover correlations that relational databases struggle with. Time series databases (e.g., Amazon Timestream) are designed for high-volume, time-based data analysis, such as data from IoT devices.

The role of the DBA is evolving in the cloud. While AWS manages much of the platform, DBAs still handle tasks like restoring databases, managing access, and optimizing queries. The focus shifts from platform management to application innovation.


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*最后更新: 2026-04-14*
