Most likely you are already aware of what a database is and of its main functions — processing and storing data. AWS provides a variety of data analytics resources that enable you to easily plan, scale, protect and deploy big data tools. The capabilities for capturing, saving, sorting and analyzing big data significantly differ.
Users constantly get confused at the point when they need to choose the database that works best for them and their project. So before jumping right into making your decision, you should understand what AWS databases offer.
In this article, we explain the difference between the two AWS database types: SQL and NoSQL, as well as discuss the capabilities, functions and features of the most popular services to help you choose the database that is a perfect match for your project.
There are two main AWS database types that we traditionally distinguish in the IT environment: relational (SQL) and NoSQL database management systems. Both of these AWS database options are equally useful in their own way and are entirely possible to work with, but you can still find a ton of differences between them.
Basiсally, we can classify AWS databases in the following way:
Let’s take a closer look at each of them.
Relational or SQL is the most common database AWS option. The distinctive feature of an SQL database is storing data in interconnected tables. All information in the database is always associated with other information and is presented in a strictly and logically structured way, including the fields that describe the data, the operations performed on them, their relationships with each other and the most important part — the rules that ensure their integrity.
The table must contain columns (with a data type) and rows (with records). Each row in a table is a record with a key (a unique identifier). Table columns hold data attributes, and each record usually contains a value for each attribute, which helps to easily establish relationships between data items.
Generally speaking, relational databases are databases that are used to store and provide access to related parts of information.
Here is a useful thing to remember: For ordinary projects, in technical terms, there is no difference which database to use, but economically it would be more beneficial to give preference to the most common MySQL, which is used by many content management systems or slightly less common in simple PostgreSQL projects. With a relational database, you have access to more developers, need less support and have lower development costs.
On Amazon, there are a variety of uses for relational databases, which can be:
Let’s consider the most popular Amazon relational databases.
Amazon Aurora has two big advantages: It’s easy to use and offers device efficiency. Aurora’s revolutionary storage infrastructure, which is specifically designed to take advantage of new cloud technology, is one of its most crucial features. Amazon Aurora is five times faster compared to MySQL and three times than PostgreSQL.
The Amazon Aurora RDBMS storage framework also can be configured based on database workloads. It’s a well-known, high-performance and highly scalable cloud RDBMS that works with MySQL and PostgreSQL relational databases. Since Aurora is compatible with MySQL and PostgreSQL, it can use existing code, programs, drivers and software with little to no changes. Aurora is fully managed so you can set up the database quickly.
For storage, Amazon Aurora automatically grows in increments of 10 GB, up to 64 TB. When you run serverless, you’re charged in Aurora capacity units (ACUs), which equal 2GB of memory and the compute and network resources that go with it.
Some of the other Aurora features include:
Amazon Relational Database Service (RDS) permits clients to set up, operate and scale an information base in the AWS database cloud. Amazon RDS gives cost-proficient and resizable limits while robotizing tedious organization assignments (for example, equipment provisioning, arrangement, fixing and reinforcements).
It liberates you to zero in on applications so you can give them the exhibition and security they need. It is perhaps the most basic and lightweight solution accessible in the market with astounding versatility according to the utilization.
Amazon RDS enables users to quickly and easily launch database instances and connect applications. RDS is easier to scale because it is less technical, requiring just a few clicks in the AWS Console to calculate an auto-scale total power. It can be used on-demand or with reserved space.
The engine used affects RDS pricing, but it is normally less expensive than the others. RDS can be purchased as a pay-as-you-go service with a higher tariff, or as a reserved case service with a lower tariff and a commitment to a certain amount of use. Amazon RDS costs less than Aurora, but at the same time, it’s less efficient.
Amazon Redshift is a fast, completely controlled petabyte storage solution that makes analyzing all of your data with your current business intelligence tools simple and cost-effective. Using advanced database optimization, columnar storage on high-performance local disks and massively parallel query execution, the service helps you to execute complex analytic queries against petabytes of structured data.
Most results can be obtained in a matter of seconds. Additional functions, such as concurrency scaling, are compensated under a separate structure. Redshift also is usable on an allocated instance and an on-demand basis.
Unlike most traditional database systems, non-relational databases do not use a tabular row and column schema. They use a storage model that is optimized for the specific requirements of the type of data stored.
These also are so-called NoSQL databases and storages and include MongoDB, CouchDB, Redis, Memcached, Cassandra and Scylla. These are much younger than relational databases and also differ significantly from them in storage structure and mechanics of working with data.
NoSQL DBMSs often are used not for storing all application data, but only for solving specific tasks (logging, caching, distributed data storage) and therefore are less common in simple projects.
While relational databases are not suitable for many use cases, particularly those requiring very high performance or dynamic scalability, NoSQL is there to mainly handle large volumes of unstructured data.
NoSQL does not store any structured and clear tables, but any information that can be presented in the form of a text document, audio file or publication on the Internet.
Since almost any data can be stored in such databases, they are widely used in a variety of applications for smartphones and PCs. They are ideal for all cases where the structure of understandable data is more important than a flexible and easily scalable database, which also is characterized by high-performance parameters.
So we have just covered the main concept. Now let’s review some of the most common AWS database options from the NoSQL group.
Amazon DynamoDB is a text and key-value database that is fully maintained. It has multi-master and multi-region capabilities, as well as built-in encryption, automatic backup and restores, and in-memory caching. Serverless web applications, microservices and mobile backends will all benefit from DynamoDB.
Amazon DynamoDB is a database of key-value pairs and documents that delivers less than 10-millisecond latency at scale. It is a robust, fully managed database for web-wide applications that operates in multiple regions with multiple active servers and has built-in security, backup and recovery, and in-memory caching. DynamoDB possesses the ability to work with over 10 trillion requests per day and can overcome peaks in excess of 20 million requests per second.
Amazon Neptune is a graph storage service that is entirely run by Amazon. It allows you to build and run applications based on large, interconnected data sets. It allows for the storing of large collections of relationship data with low latency access. RDF, SPARQL and Gremlin are among the graph models and languages supported by Neptune. Point-in-time restore, read replicas and continuous backup also is included.
Amazon (QLDB) is a serverless ledger storage provider that is completely run. It can be used to keep track of program data updates in a verifiable manner. You may avoid having to create custom ledger implementations and authentication tools by using QLDB. A SQL-like API can be used to query data in QLDB.
The comparison table below shows the main NoSQL databases services offered by AWS in a more practical way:
| Name of the Database | Type of Service | Use Cases |
| Document DB | Document Document database collects data in JSON or JSON-like documents. Collect documents and quickly access querying on any attribute. |
Cataloging Customer profiles and personalization Content management systems Mobile applications |
| Dynamo DB | Key-value Key-value is the simplest type of data storage that uses a key to access the value within a large hash table. These databases can store various types of data, including simple and compound objects. For instance: storing images, creating specialized file systems, as caches for objects, as well as in scalable big data systems, including gaming and advertising applications. |
Real-time bidding eCommerce shopping carts Customer preferences Product catalogs |
| Neptune | Graph Graph storage is a network database that uses nodes and edges to display and store data. This database form allows you to swiftly navigate relationships between data. Data also can be queried using specific graph languages. |
High security and fraud discovery Social networking Information graphs Data lineage Recommendation engines |
| QLDB (Quantum Ledger Database) | Ledger Data is stored as an eternal, open and cryptographically verifiable log in ledger databases. To ensure provenance, this log is owned by a trusted central authority. |
Financial ledger Manufacturing Asset chain System of record HR and payroll Retail inventories Insurance claims |
As we can see, it is preferable to use a relational DBMS as the main storage. Nonetheless, for ordinary projects, it is easier to use MySQL or PostgreSQL, since the difference between different relational databases is not very noticeable on simple operations. However, if the project provides for a complex logic of data processing, then the choice of the AWS database should be made based on the technical characteristics.
Traditional SQL databases do an excellent job of handling small, strongly typed information. For example, a local ERP system or a cloud CRM. However, in the case of processing a large amount of semi-structured and unstructured data, i.e. Big Data, in a distributed system, you should choose from a variety of NoSQL storages, taking into account the specifics of the task itself.
When choosing among the right AWS database types for a project, it’s always important to study different AWS database options and opinions on them from a couple of trusted sources. In the process of making this important decision, it may turn out that the right choice is not one database, but perhaps even a few of them. Choose the best database for solving a specific problem, and that works for your project best!