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Amazon RDS vs Oracle: What are the differences?
Introduction
Amazon RDS and Oracle are two popular database management systems that offer different features and functionalities. Understanding the key differences between Amazon RDS and Oracle can help organizations make informed decisions about their database management needs.
Scalability: One key difference between Amazon RDS and Oracle is their scalability options. Amazon RDS provides automatic scalability where the database can be easily scaled up or down based on the workload. On the other hand, Oracle offers manual scalability options, requiring administrators to manually adjust resources as needed.
Database Management: Another difference lies in the database management responsibilities. With Amazon RDS, much of the database management tasks such as backups, patching, and monitoring are taken care of by Amazon Web Services. Oracle, on the other hand, requires more manual administration and maintenance tasks to be performed by the database administrators.
Pricing Model: The pricing model is also different between Amazon RDS and Oracle. Amazon RDS offers a pay-as-you-go pricing model, where users are charged based on the resources they use. Oracle, on the other hand, follows a traditional licensing model with upfront costs and ongoing maintenance fees.
Ease of Deployment: When it comes to deployment, Amazon RDS offers a more seamless and automated experience. It allows users to easily provision a new database instance within minutes, with all the necessary configurations already set up. Oracle requires more manual effort and configuration to set up a new database instance.
Supported Databases: Amazon RDS supports a variety of databases apart from Oracle, such as MySQL, PostgreSQL, and Microsoft SQL Server, providing users with flexibility and choice. Oracle, on the other hand, is specifically designed for Oracle databases, restricting the choice of database platforms.
Backup and Recovery: The backup and recovery mechanisms also differ between Amazon RDS and Oracle. Amazon RDS offers automatic backups and point-in-time recovery features, making it easier to restore data in case of an issue. Oracle offers backup and recovery options but requires more manual intervention from the administrators.
In Summary, Amazon RDS provides automatic scalability, takes care of database management tasks, follows a pay-as-you-go pricing model, offers seamless deployment, supports multiple databases, and provides automated backup and recovery features. Oracle, on the other hand, offers manual scalability, requires more manual administration, follows a traditional licensing model, requires manual deployment configuration, supports only Oracle databases, and offers backup and recovery options with more manual intervention.
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 Amazon RDS
- Reliable failovers165
- Automated backups156
- Backed by amazon130
- Db snapshots92
- Multi-availability87
- Control iops, fast restore to point of time30
- Security28
- Elastic24
- Push-button scaling20
- Automatic software patching20
- Replication4
- Reliable3
- Isolation2
Pros of Oracle
- Reliable44
- Enterprise33
- High Availability15
- Hard to maintain5
- Expensive5
- Maintainable4
- Hard to use4
- High complexity3
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Cons of Amazon RDS
Cons of Oracle
- Expensive14
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What is Amazon RDS?
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