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Azure SQL Database vs Oracle: What are the differences?
Introduction
In this article, we will discuss the key differences between Azure SQL Database and Oracle. Azure SQL Database is a managed cloud service provided by Microsoft, while Oracle is a widely used relational database management system. Below are the key differences between these two platforms.
Scalability: One major difference between Azure SQL Database and Oracle is scalability. Azure SQL Database offers elastic scalability, allowing you to easily adjust the resources allocated to your database based on demand. With a few clicks or automated scaling rules, you can dynamically increase or decrease the database resources. On the other hand, Oracle requires manual intervention for scaling and requires additional hardware resources.
Pricing model: Azure SQL Database follows a fully managed, pay-as-you-go pricing model. You are charged based on the resources consumed by your database and have the flexibility to choose between different pricing tiers based on your needs. Oracle, on the other hand, follows a traditional licensing model where you need to purchase licenses based on the number of cores or users, which can be more complex and costly.
High availability: Azure SQL Database offers built-in high availability, ensuring that your database is always accessible. It utilizes redundant copies of your data across different fault domains, providing automatic failover in case of any failures. Oracle also offers high availability features but requires additional configuration and setup, which can be more complex.
Backup and restore: Azure SQL Database provides automated backups and point-in-time restore capabilities. It takes regular backups of your database and allows you to restore it to any point within a specific time period. Oracle also provides backup and restore features but requires manual setup and management of backups.
Security: Azure SQL Database offers various security features, including built-in threat detection, firewall rules, and encryption at rest and in transit. It also has advanced security capabilities like dynamic data masking and row-level security. Oracle also provides similar security features but may require additional configuration and setup.
Development and deployment: Azure SQL Database provides seamless integration with other Azure services and supports modern development practices like DevOps and CI/CD pipelines. It also supports platform as a service (PaaS) deployment model. On the other hand, Oracle has been traditionally used in on-premises environments and may require more effort for integration with other cloud services and modern deployment practices.
In summary, Azure SQL Database and Oracle differ in terms of scalability, pricing model, high availability, backup and restore capabilities, security features, and development and deployment options. Each platform has its strengths and considerations, and the choice depends on specific requirements and preferences.
We have chosen Tibero over Oracle because we want to offer a PL/SQL-as-a-Service that the users can deploy in any Cloud without concerns from our website at some standard cost. With Oracle Database, developers would have to worry about what they implement and the related costs of each feature but the licensing model from Tibero is just 1 price and we have all features included, so we don't have to worry and developers using our SQLaaS neither. PostgreSQL would be open source. We have chosen Tibero over Oracle because we want to offer a PL/SQL that you can deploy in any Cloud without concerns. PostgreSQL would be the open source option but we need to offer an SQLaaS with encryption and more enterprise features in the background and best value option we have found, it was Tibero Database for PL/SQL-based applications.
We wanted a JSON datastore that could save the state of our bioinformatics visualizations without destructive normalization. As a leading NoSQL data storage technology, MongoDB has been a perfect fit for our needs. Plus it's open source, and has an enterprise SLA scale-out path, with support of hosted solutions like Atlas. Mongo has been an absolute champ. So much so that SQL and Oracle have begun shipping JSON column types as a new feature for their databases. And when Fast Healthcare Interoperability Resources (FHIR) announced support for JSON, we basically had our FHIR datalake technology.
In the field of bioinformatics, we regularly work with hierarchical and unstructured document data. Unstructured text data from PDFs, image data from radiographs, phylogenetic trees and cladograms, network graphs, streaming ECG data... none of it fits into a traditional SQL database particularly well. As such, we prefer to use document oriented databases.
MongoDB is probably the oldest component in our stack besides Javascript, having been in it for over 5 years. At the time, we were looking for a technology that could simply cache our data visualization state (stored in JSON) in a database as-is without any destructive normalization. MongoDB was the perfect tool; and has been exceeding expectations ever since.
Trivia fact: some of the earliest electronic medical records (EMRs) used a document oriented database called MUMPS as early as the 1960s, prior to the invention of SQL. MUMPS is still in use today in systems like Epic and VistA, and stores upwards of 40% of all medical records at hospitals. So, we saw MongoDB as something as a 21st century version of the MUMPS database.
Pros of Azure SQL Database
- Managed6
- Secure4
- Scalable3
Pros of Oracle
- Reliable44
- Enterprise33
- High Availability15
- Expensive5
- Hard to maintain5
- Maintainable4
- Hard to use4
- High complexity3
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Cons of Azure SQL Database
Cons of Oracle
- Expensive14
