Summary and Quiz

We’ve studied different databases offered by AWS and their important features, use cases, and limitations. The concepts covered throughout the chapter are very important and require industry practice to master them. Therefore, reading the official documentation on these concepts and services from the certification’s perspective is always recommended to keep your toolkit updated before appearing in the exam.

Let’s summarize the services we’ve covered:

Relational databases

• Amazon RDS: A fully managed, highly available, Multi-AZ database that supports seven popular and widely used DB engines. We discussed the DB instances, their classes, types, and storage options. We also discussed the RDS connection with VPC and AZs, and finally, we learned about the deployment options of the RDS.

• Aurora and Aurora Serverless: We studied the Aurora engines, replicas, and global databases and how they help in the high availability and durability of the data. We also studied cross-region replication and backups in Amazon Aurora. We reviewed the Aurora Serverless, its use cases, and its advantages. 

NoSQL database

Amazon Web Services offers several solutions for NoSQL databases to meet our business needs. The concepts we studied are briefly described below within the relevant category:

Key-value database

• Amazon DynamoDB: We studied the core components of DynamoDB and how they store data. We studied the consistency models and the concepts of global tables and multi-region replication. We also reviewed the backup and recovery options in DynamoDB.

Graph database

• Amazon Neptune: A fast, reliable, fully managed, and purpose-built database to perform the graph or connected data. We studied the functionality of the Amazon Neptune and how it improves performance and scalability. We also studied scaling and backup options and how to restore the database from backup.

Document database

• Amazon DocumentDB: We studied the functionality and core components of DocumentDB and how these components work together. We studied the global cluster and its usage in data locality, recovery, and scalability. We also studied high availability, backing up, and restoring databases.

Wide-column database

• Amazon Keyspaces: We studied some important features of Amazon Keyspaces like capacity modes, Multi-Region Replication, high availability, backup, and recovery. 

Ledger database

• Amazon Quantum Ledger Database (QLDB): A purpose-built database to facilitate businesses where the history of all the changes made to the data is required, and the original data never gets deleted. We studied the core components of Amazon QLDB and how they work with each other. We also studied the verification types of data in Amazon QLDB.

In-memory databases

• Amazon MemoryDB for Redis: A purpose-built, fast, fully managed, and Redis-compatible primary database that delivers ultra-fast performance. We studied its main components and how they work together and manage data. We discussed Multi-AZ replication, failover, and scaling features. We also discussed backup and restore options in Amazon MemoryDB.

• Amazon ElastiCache: A fully managed and scalable cache solution for applications where the data resides in some other databases. It enhances the performance by caching the data in its nodes. Amazon ElastiCache supports Redis and Memcached engines. We studied ElastiCache for Redis in detail and discussed some points compared with ElastiCache for Memcached. We also discussed deployment, backup, and recovery options in ElastiCache for Redis.