1. Overview
1.1. Data at Cloud Scale
1.1.1. Database Selection
2. SQL
2.1. Amazon Relational Database Service (RDS)
2.1.1. RELATIONAL
2.1.1.1. Overview
2.1.1.1.1. RDS Engines
2.2. AWS Aurora
2.2.1. Native AWS Datbase Engine
2.2.1.1. Amazon Aurora is an enterprise-class relational database.
2.2.1.2. It is compatible with MySQL and PostgreSQL relational databases.
2.2.1.3. It is up to five times faster than standard MySQL databases and up to three times faster than standard PostgreSQL databases.
2.2.1.4. Consider Amazon Aurora if your workloads require high availability.
2.2.1.5. It replicates six copies of your data across three Availability Zones and continuously backs up your data to Amazon S3.
2.3. Amazon Quantum Ledger Database (QLDB)
2.3.1. Blockchain
2.3.1.1. Used to review a complete history of all the changes that have been made to your application data.
3. NoSQL
3.1. Amazon DocumentDB
3.1.1. DOCUMENT (MongoDB)
3.1.1.1. Amazon DocumentDB is a document database service that supports MongoDB workloads.
3.2. Amazon DynamoDB
3.2.1. Document / KEY VALUE (Redis)
3.2.1.1. Overview
3.2.1.1.1. Under The Hood
3.2.1.2. Amazon DynamoDB is a key-value database service.
3.2.1.2.1. Amazon.com
3.2.1.3. Amazon DynamoDB Accelerator (DAX)
3.2.1.3.1. Amazon DynamoDB Accelerator (DAX) is an in-memory cache for DynamoDB.
3.3. Amazon Neptune
3.3.1. GRAPH (Neo4j)
3.3.1.1. Used for highly connected datasets, such as recommendation engines, fraud detection, and knowledge graphs.
4. Cache
4.1. Amazon ElastiCache
4.1.1. In-Memory CACHE
4.1.1.1. Server-side Managed Caching Service to speed up the RESPONSE time of your application by caching query results
4.2. Elasticache Engines
4.2.1. Amazon ElastiCache for Redis
4.2.1.1. Redis compatible in-memory data store built for the cloud. Power real-time applications with sub-millisecond latency.
4.2.2. Amazon ElastiCache for Memcached
4.2.2.1. Managed, Memcached-compatible, in-memory store. Sub-millisecond latency to power real-time applications.