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AWS Database Choices

Source: https://docs.aws.amazon.com/decision-guides/latest/databases-on-aws-how-to-choose/databases-on-aws-how-to-choose.html

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Vagelis Hristidis

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Introduction

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  • AWS offers over 15 purpose-built database services for OLTP.
  • For OLAP, AWS offers Redshift.
  • Supports diverse data models: relational, key-value, document, etc.
  • Selection depends on specific application requirements.

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Choices

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AWS Database Services Overview

  • Amazon RDS: Managed relational databases (e.g., MySQL, PostgreSQL).
  • Amazon Aurora: High-performance relational database compatible with MySQL and PostgreSQL.
  • Amazon DynamoDB: Fully managed NoSQL key-value and document database.
  • Amazon ElastiCache: In-memory data store for caching (Redis, Memcached).
  • Amazon Neptune: Graph database for connected data.
  • Amazon Timestream: Time series database for IoT and operational applications.
  • Amazon DocumentDB: Document database with MongoDB compatibility.
  • Amazon Keyspaces: Managed Cassandra-compatible database.
  • Amazon MemoryDB: Redis-compatible, durable in-memory database.​

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  • Redshift: OLAP

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Summary 1/3

Data model

When would you use it?

What is it optimized for?

Related database engines or services

Relational

Use when you're migrating or modernizing an on- premises relational workload, or if your workload has less predictable query patterns.

Optimized for structured data that is stored in tables, rows, and columns. Relational databases support complex queries through joins.

Key-value

Use for workloads such as session stores or shopping carts. Key-value databases can scale to large amounts of data and extremely high throughput of requests, while servicing millions of simultaneous users through distributed processing and storage.

Optimized to provide a serverless, NoSQL, fully managed database with single-digit millisecond performance at any scale.

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Summary 2/3

In-memory

Use Amazon ElastiCache when you need a caching layer to improve read performance. Use Amazon MemoryDB when you need full data persistence, but still need sub-millisecond read latencies.

Optimized to support microsecond reads and sub-millisecond writes. MemoryDB supports microsecond reads and single-digit millisecond writes. ElastiCache is an ephemeral cache, while MemoryDB is an in-memory database.

Document

Use when you want to store JSON-like documents with rich querying abilities across the fields of the documents.

Optimized for storing semi-structured data as documents with multilayered attributes.

Wide-column

Use when you need to migrate your on-premises Cassandra workloads, or when you need to process data at high speeds for applications that require single-digit millisecond latency.

Optimized for workloads that require heavy reads/writes and high throughput, coupled with low latency and linear scalability.

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Summary 3/3

Graph

Use when you have to model complex networks of objects, such as social networks, fraud detection, and recommendation engine use cases.

Optimized for traversing and evaluating large numbers of relationships, and identifying patterns with minimal latency.

Time series

Use when you have a large amount of time series data, potentially from a number of sources, such as Internet of Things (IoT) data, application metrics, and asset tracking.

Optimized for storing and querying data that is associated with timestamps and trend lines.

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Conclusion

  • AWS offers databases for varied workloads.
  • Proper selection ensures performance and cost-efficiency.
  • Migration of data is a consideration
  • Other cloud providers like Azure and GCP offer similar products

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Vector Databases for AI

  • AWS databases now support vector capabilities for AI applications.
  • Facilitates semantic search and context retrieval in generative AI workflows.
  • Integrates with existing data sources for enhanced AI model grounding.