Diving into AWS Databases: Amazon RDS and DynamoDB Explained
The increasing demand for data processing and manipulation has led organizations to prioritize efficiency, security, scalability and availability in their data management strategies. Companies that leverage top tools in the market enjoy a higher chance of success, which is why Amazon Web Services (AWS) should be a top contender when considering database storage solutions.
AWS offers an extensive range of database services for various use cases. In this article, we’ll explore the primary differences between two popular options:
- Relational Database Service (RDS)
Before we delve into the specifics of each service, let’s first look at the distinctions between relational and non-relational databases.
Relational (SQL) vs. Nonrelational (NoSQL) Databases
Relational databases use predefined schemas and store data in rows and columns, resembling a spreadsheet. In contrast, nonrelational databases like DynamoDB feature dynamic schemas, are document-oriented and scale horizontally.
Relational (SQL) Databases
Relational databases employ Structured Query Language (SQL) for interaction and use predefined schemas. SQL is a widely recognized query language familiar to most database administrators (DBAs).
SQL databases contain tables with columns (attributes) and rows (records) and use keys with constrained logical relationships to maintain consistency and integrity. These databases can be scaled vertically by enhancing processing hardware power (for instance increasing RAM, CPU or solid-state disks).
Advantages of relational (SQL) databases:
- Use of SQL language
- Atomicity of database operations
- Flexible query capabilities
Disadvantages of relational (SQL) databases:
- Requires careful upfront design
- Schema changes may cause downtime
- Limited horizontal scalability
Nonrelational (NoSQL) Databases
NoSQL databases are nonrelational database management systems with dynamic schemas for unstructured data. These databases can be categorized based on their data models:
- Key value
- Wide column
NoSQL databases are suitable for large volumes of data or frequently changing data sets. Document databases are particularly useful for handling vast amounts of unstructured data. Unlike SQL databases, which scale vertically, NoSQL databases scale horizontally, making it easier to expand capacity by adding more servers or nodes.
Advantages of nonrelational (NoSQL) databases:
- Easy scalability and high availability
- Flexible database models
- High performance
Disadvantages of nonrelational (NoSQL) databases:
- Some databases lack atomicity and data integrity
- Absence of standardization
Now that we’ve covered the basics of relational and nonrelational databases, let’s examine the database options provided by AWS.
Amazon RDS (SQL) vs. DynamoDB (NoSQL)
Both RDS and DynamoDB are fully managed by AWS, meaning the company handles the underlying operating system and core components. AWS automates routine tasks such as provisioning, patching, backup, recovery, failure detection and repair, reducing administrative overhead.
Let’s examine the details of each service.
Amazon RDS enables users to set up, operate and scale relational (SQL) databases on AWS. It simplifies replication to improve availability and reliability for production workloads. AWS offers six SQL-based database engine options:
- Amazon Aurora
- Microsoft SQL server
AWS provides various instance types with differing combinations of CPU, memory, storage and networking capacity to suit workload requirements.
Amazon RDS features:
- Multi-availability zone deployment for high availability
- Read replicas for read-heavy workloads
- Automatic backups and patching
Both AWS RDS and DynamoDB provide businesses with fully managed cloud-service options. AWS or a managed cloud-service company, such as Mission, takes care of routine tasks like provisioning, patching, backup, recovery, failure detection and repair. Ultimately, the choice between these two services depends on your specific needs and preferences.
RDS is often favored for enterprise resource planning (ERP), customer relationship management (CRM), financial data and transactional applications. It enables you to establish, operate and scale relational (SQL) databases on AWS, offering a variety of instance types to choose from.
On the other hand, AWS DynamoDB is a serverless solution that automatically adjusts table capacity to accommodate demand, requiring no administrative effort from you. Typical use cases for DynamoDB include real-time bidding, shopping carts, mobile applications and high I/O requirements.
When you need help deciding what database to migrate to, an AWS Premier Tier Partner like Mission Cloud can advise you in determining the right database for your needs. Contact Mission Cloud to set up your complimentary one-hour session with a Mission solutions architect to discuss your machine learning questions.