Amazon Aurora vs. Amazon DocumentDB vs. Mongoose vs. SQL Server Data Access Components Comparison Chart

Amazon Aurora is a MySQL and PostgreSQL-compatible relational database built for the cloud, that combines the performance and availability of traditional enterprise databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora is up to five times faster than standard MySQL databases and three times faster than standard PostgreSQL databases. It provides the security, availability, and reliability of commercial databases at 1/10th the cost. Amazon Aurora is fully managed by Amazon Relational Database Service (RDS), which automates time-consuming administration tasks like hardware provisioning, database setup, patching, and backups. Amazon Aurora features a distributed, fault-tolerant, self-healing storage system that auto-scales up to 64TB per database instance. It delivers high performance and availability with up to 15 low-latency read replicas, point-in-time recovery, continuous backup to Amazon S3, and replication across three Availability Zones.

Let's face it, writing MongoDB validation, casting and business logic boilerplate is a drag. That's why we wrote Mongoose. Now say we like fuzzy kittens and want to record every kitten we ever meet in MongoDB. The first thing we need to do is include mongoose in our project and open a connection to the test database on our locally running instance of MongoDB. We have a pending connection to the test database running on localhost. We now need to get notified if we connect successfully or if a connection error occurs. Mongoose documents represent a one-to-one mapping to documents as stored in MongoDB. Each document is an instance of its Model. Subdocuments are documents embedded in other documents. In Mongoose, this means you can nest schemas in other schemas. Mongoose has two distinct notions of subdocuments: arrays of subdocuments and single nested subdocuments.