RDS: AWS's relational database system which is basically hosting MySQL, PostGres, MSSQL, Oracle, Amazon's own AuroraDB and MariaDB, an open-source clone of MySQL. Applications that are on application servers at data centers or hosted in the cloud can both use RDS as a data source and customize the DB instance (the basic unit of RDS) with the hardware and memory that they want. The databases can all be administered using the respective clients. AWS Networking and Backups are integrated with RDS.
DynamoDB: AWS's NoSQL database which stores data in JSON key-value ("a" : "test") format. Instead of writing SQL queries like with a relational database, you write NoSQL queries that query JSON. It integrates with AutoScaling that changes the read and write capacity of the database, depending on request volume. It also integrates with KMS allowing you to encrypt data at rest on the fly. It claims to scale really well horizontally (throw more computers at the problem). DynamoDB also has a HTTP API that you can use to directly query it. As usual, the devil is in the details and it is probably not for everyone. There's a nice blog which has a cool flowchart about when one should and should not use DynamoDB.
Elasticache: This is an in-memory database that supports Redis and Memcached. The point of an in-memory DB is to increase the speed of resolution, so users do not have to wait longer to use services. In other words it is a layer of abstraction before the database. If a user's request can be served from Redis cache, it will be done - and done faster than a round trip to the database. Here is a link to a comparison between Redis and Memcached.
Neptune: This is a graphing database. It is largely useful when there are large sets of data that are related to each other. The inter-related data is stored in the database and users can query it using languages built specifically for graphing (Apache Tinkerpop Gremlin and Sparql). Its interesting that the smallest DB instance that you can provision from inside Neptune is db.r4.large (~16 GB RAM) - which by itself shows that this is a product used for very large data sets.
Redshift: This is AWS's enterprise data warehousing solution. In other words it means that it helps analyze petabytes (if you want) of data from a variety of sources such as S3, Glacier, Aurora and RDS. There's a lot of database design that's needed, so I'm guessing (do not know for sure) that things can get pretty complex, pretty soon. Once the data is inside a RedShift cluster (for example: copied from S3), you can run SQL queries against it and make complex queries against the cluster. If you don't have huge amounts of data you probably do not want RedShift.
DocumentDB: This is basically there so you can migrate all your MongoDB content to the cloud while continuing to use all the Mongo relevant clients and tools. All you then do is change the DB endpoint to point to the DocumentDB endpoint in the cloud. The cool bit here is you can autoscale the storage your DB needs and the read capacity (how many queries can you make) so large applications are easily served. This too has the smallest instance as a db.r5.large instance with 16 GB RAM. So that feels like this too is a production service and might be expensive for smaller loads. I don't know that for a fact though - so please do your testing :)
DynamoDB: AWS's NoSQL database which stores data in JSON key-value ("a" : "test") format. Instead of writing SQL queries like with a relational database, you write NoSQL queries that query JSON. It integrates with AutoScaling that changes the read and write capacity of the database, depending on request volume. It also integrates with KMS allowing you to encrypt data at rest on the fly. It claims to scale really well horizontally (throw more computers at the problem). DynamoDB also has a HTTP API that you can use to directly query it. As usual, the devil is in the details and it is probably not for everyone. There's a nice blog which has a cool flowchart about when one should and should not use DynamoDB.
Elasticache: This is an in-memory database that supports Redis and Memcached. The point of an in-memory DB is to increase the speed of resolution, so users do not have to wait longer to use services. In other words it is a layer of abstraction before the database. If a user's request can be served from Redis cache, it will be done - and done faster than a round trip to the database. Here is a link to a comparison between Redis and Memcached.
Neptune: This is a graphing database. It is largely useful when there are large sets of data that are related to each other. The inter-related data is stored in the database and users can query it using languages built specifically for graphing (Apache Tinkerpop Gremlin and Sparql). Its interesting that the smallest DB instance that you can provision from inside Neptune is db.r4.large (~16 GB RAM) - which by itself shows that this is a product used for very large data sets.
Redshift: This is AWS's enterprise data warehousing solution. In other words it means that it helps analyze petabytes (if you want) of data from a variety of sources such as S3, Glacier, Aurora and RDS. There's a lot of database design that's needed, so I'm guessing (do not know for sure) that things can get pretty complex, pretty soon. Once the data is inside a RedShift cluster (for example: copied from S3), you can run SQL queries against it and make complex queries against the cluster. If you don't have huge amounts of data you probably do not want RedShift.
DocumentDB: This is basically there so you can migrate all your MongoDB content to the cloud while continuing to use all the Mongo relevant clients and tools. All you then do is change the DB endpoint to point to the DocumentDB endpoint in the cloud. The cool bit here is you can autoscale the storage your DB needs and the read capacity (how many queries can you make) so large applications are easily served. This too has the smallest instance as a db.r5.large instance with 16 GB RAM. So that feels like this too is a production service and might be expensive for smaller loads. I don't know that for a fact though - so please do your testing :)
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