Databases are an integral part of well-developed applications, so choosing the right database offering for your particular use case is a very crucial part of the development lifecycle for any software application. In this article, we are going to be talking about the various database services offered by AWS and give you ideas on choosing the perfect database that will enable you to create cost-effective and highly performant solutions for your business or customers in AWS. We will look at each database offering and at least one example use case for the offering to put things into perspective for you. Some of the AWS database services we are going to be looking at include the following; Amazon RDS, Aurora, DynamoDB, DocumentDB, Amazon Neptune, Amazon Keyspaces and many more. So without wasting any more of your time, let's get started!
Amazon Relational Database Service (RDS) — It is a fully-managed database service provided by AWS that allows users to create and manage relational databases without the burden of managing the underlying infrastructure. It is designed to simplify the management of relational databases, which are traditional databases with structured data organized in tables. With RDS, AWS handles most of the day-to-day database management tasks such as backups, software patching, and monitoring, allowing users to focus on their applications and data. It supports popular commercial database engines such as Oracle, and Microsoft SQL Server as well as open-source database engines like MySQL, MariaDB and PostgreSQL allowing users to create, scale, and manage instances of these databases in the cloud. The two key benefits of using RDS are its scalability and security. Users can easily scale their database instances up or down depending on the needs of their application. It also provides multiple layers of security to protect user data, including network isolation, encryption of data at rest and in transit, and the ability to control access to the database instance. RDS supports automatic backups, so users can recover their data in case of any disaster. It also supports multi-AZ deployment for high availability and durability and the creation of read replicas, which are copies of the primary database instance. Read replicas allow you to offload read traffic from the primary instance and distribute it across multiple replicas. This can help improve the performance of your application by reducing the load on the primary database instance. RDS integrates well with other AWS services, such as Elastic Compute Cloud (EC2), Elastic Load Balancing (ELB), and Amazon S3 making it easy to build scalable and reliable solutions on AWS using a combination of these services.
Use case: If you require support for multiple database engines such as MySQL, PostgreSQL, Oracle, SQL Server, and MariaDB, RDS provides a single solution for all these databases. This is particularly useful for organizations that run different applications that require different databases. Also, if you need to comply with regulatory requirements such as HIPAA, PCI, or SOC, RDS provides compliance certifications for these and other regulatory standards.
Amazon Aurora — Amazon Aurora is a cloud-native relational database engine that is designed to be highly scalable, durable, and available. It is compatible with MySQL and PostgreSQL, which makes it easy to migrate existing applications to the cloud. Aurora is fundamentally an "AWS-implementation" for PostgreSQL and MySQL. It is a managed database service, which means that AWS takes care of the operational overhead infrastructure management, updates, backups, and security of the database. One of its key benefits is its self-healing capabilities which makes it a highly resilient and reliable database service. It is also able to scale automatically to meet changes in traffic and demand. Aurora uses a distributed, fault-tolerant architecture that separates compute and storage allowing it to scale out as needed. This enables Aurora to deliver up to five times the performance of MySQL and up to three times the performance of PostgreSQL. Automated failovers in Aurora take less than thirty seconds. Aurora offers many features making it a highly scalable, durable, and performant database engine that is well-suited for mission-critical applications.
Use case: If you require a highly scalable, high-performance database with low latency and fast read/write operations, Amazon Aurora is an excellent option. It uses a distributed and fault-tolerant architecture that can scale to support millions of transactions per minute, making it a suitable choice for mission-critical and high-demand applications. In addition, if your application requires a highly available database, Aurora provides the option to deploy in multiple regions, providing greater availability and fault tolerance.
DynamoDB — Amazon DynamoDB is a fully managed NoSQL(non-relational) database service that provides fast performance with seamless scalability. It offers an efficient, flexible, and reliable way to store and retrieve large volumes of unstructured data, and is designed to provide consistent, single-digit millisecond latency at any scale. DynamoDB is a highly available database that automatically replicates data across multiple AZs, making it resilient to infrastructural failures. One of its key features is its ability to scale horizontally without requiring any manual intervention. This is accomplished through the use of partitioning, which allows DynamoDB to automatically distribute data across multiple servers. As a result, it can handle large amounts of data and high traffic volumes without needing to sacrifice performance. DynamoDB is also highly configurable, allowing users to specify attributes such as read and write capacity(RCU and WCU), data partitioning, and data durability. It supports a variety of data types including strings, numbers, binary data, and sets. In addition, DynamoDB provides support for advanced features such as conditional writes, transactions, and complex queries. It supports both document and key-value data models, allowing you to store and query data flexibly and efficiently.
Use case: If you need a fully managed NoSQL database service that can deliver single-digit millisecond latency at any scale, data backup, automatic scaling and multi-region replication, DynamoDB is a good database option to consider. It is designed to handle large amounts of unstructured data, making it a suitable choice for applications that require flexible data models and can't predict their database capacity needs in advance.
Amazon DocumentDB — DocumentDB is a fully managed, highly scalable, serverless, NoSQL document database service that is designed to support MongoDB workloads. It is fundamentally an AWS implementation (cloud native version) of MongoDB. It is compatible with existing MongoDB applications and tools, so you can use it as a drop-in replacement for MongoDB without having to rewrite existing code. It uses a document data model, which means data is stored in flexible JSON-like documents, making it easy to store and retrieve data without having to worry about a predefined schema. One of DocumentDB's key benefits is its ability to scale horizontally, which means you can add more nodes to your database cluster to increase capacity and throughput. It also provides automatic scaling allowing you to focus on your application instead of worrying about managing database capacity. DocumentDB offers security features such as; encryption of data at rest and in transit and allowing you to create database instances within a VPC.
Use case: If you have a large amount of semi-structured or unstructured data, DocumentDB is designed to handle large amounts of semi-structured or unstructured data, making it a good choice for applications that generate large amounts of data with varying data types. If you need the flexibility of a document-based database, you should also consider using DocumentDB. You should also consider DocumentDB if you want to use MongoDB-compatible APIs and tools for your applications.
Amazon Neptune — It is a fully-managed graph database service that enables users to store, query, and analyze highly connected datasets. It is a purpose-built database engine meaning the database is designed and optimized specifically for graph data models. In other words, Neptune is created to efficiently store and query graph data, which is typically made up of nodes and edges that represent relationships between them. The purpose-built nature of Neptune enables it to handle complex graph queries and analyses at scale, making it suitable for use cases such as fraud detection, recommendation engines, and social networking. Neptune is also highly available and durable, with multiple copies of data stored across multiple availability zones. This ensures that data is always available for queries and that it is protected against hardware failures and other types of disruptions.
Use case: If you require a high-performance graph database that is designed for handling complex relationships between data points, Neptune is definitely the right choice for you. It allows you to store, query, and analyze highly connected data sets. It supports popular graph query languages, such as Apache TinkerPop Gremlin and W3C's SPARQL, making it a versatile choice for a wide range of graph-based use cases.
Amazon Keyspaces — Keyspaces is a fully managed, serverless and highly available database service that is built to handle large-scale, mission-critical workloads that require low latency and high throughput. It is a fully managed Apache Cassandra(an open-source NoSQL distributed database) compatible service that allows users to store and retrieve data from Cassandra tables using the same Cassandra Query Language (CQL) API that they use to interact with their existing Cassandra applications. It was formerly known as Amazon Managed Apache Cassandra Service (MCS). Keyspaces offers two capacity modes which are; the on-demand mode and the provisioned mode which offers auto-scaling. It provides several benefits, including automatic scaling of compute and storage, fully managed backups and restores, and built-in encryption at rest and in transit. It also integrates with other AWS services like AWS Identity and Access Management (IAM) and AWS Key Management Service (KMS) for security and access control.
Use case: If you have an existing Apache Cassandra application and you want to migrate to a managed service without changing your application code, Amazon Keyspaces is the perfect solution for you. Also, if your application needs a serverless solution where you only pay for the resources you use, and the service automatically scales up or down based on demand, Keyspaces might be a great option to go with.
Amazon Quantum Ledger Database (QLDB) — Amazon QLDB is a fully managed, serverless ledger database service offered by AWS. It is a purpose-built database service that provides a transparent, immutable(no entry can be removed or modified), and cryptographically verifiable transaction log. QLDB is designed to eliminate the need for organizations to build and maintain their own ledger applications and instead provides a scalable, secure, and highly available solution that can handle millions of transactions per second. It is non-relational and uses an append-only log to record all changes to the database. This means that once a record is written to the database, it can never be modified or deleted. This makes QLDB ideal for applications that require a tamper-evident, audit-friendly, and verifiable history of all data changes. With QLDB, you get two to three times better performance than common ledger blockchain frameworks. QLDB supports a SQL-like query language, called PartiQL, which is used to retrieve data from the database. It integrates with other AWS services, such as AWS Lambda, Amazon CloudWatch, and Amazon Kinesis, which makes it easy to build serverless applications that can process and analyze ledger data in real time.
Use case: QLDB is an ideal choice for industries such as finance, healthcare, government, and any other application that requires the ability to track all changes to the data. For example, QLDB is suitable for financial applications that require an auditable and tamper-proof transaction log or in healthcare for tracking patient data and ensuring that all changes are recorded and cannot be altered.
Amazon Timestream — Amazon Timestream is a fully-managed, fast, scalable, and serverless time-series database service offered by AWS. It is designed to make it easy to store and analyze time-series data. If you wondering what time-series data is, it refers to data that is measured or collected over time, such as stock market prices, sensor data, weather data, or financial transactions. Timestream provides a purpose-built database that stores and processes time-series data at scale. It is designed to be highly available, durable, and scalable, allowing customers to focus on their applications and data rather than the operational overhead of underlying infrastructure management. It provides built-in features such as automated data retention, data compression, and automated tiering to help customers optimize their data storage and processing costs. It integrates with other AWS services such as IoT Core, Kinesis Data Streams, and Lambda, enabling customers to easily take in and analyze their time-series data.
Use case: If you need to manage large volumes of time-series data with high precision and low latency, Timestream is built purposely for that kind of use case.
Conclusion
In this article, we have seen that AWS offers a wide variety of database options, made up of both relational and non-relational databases. Each of these database services has its own strengths and use cases. Depending on your specific needs, you may choose one or more of these services to store and manage your data in the cloud. If you are interested in reading or just glancing through other AWS-related articles I have published, check out the links to the articles below.