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  1. Stackups
  2. Application & Data
  3. Relational Databases
  4. SQL Database As A Service
  5. Amazon RDS vs Oracle

Amazon RDS vs Oracle

OverviewDecisionsComparisonAlternatives

Overview

Amazon RDS
Amazon RDS
Stacks16.2K
Followers10.8K
Votes761
Oracle
Oracle
Stacks2.6K
Followers1.8K
Votes113

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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

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Advice on Amazon RDS, Oracle

Daniel
Daniel

Data Engineer at Dimensigon

Jul 18, 2020

Decided

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.

496k views496k
Comments
Abigail
Abigail

Dec 6, 2019

Decided

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.

540k views540k
Comments
Abigail
Abigail

Dec 10, 2019

Decided

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.

558k views558k
Comments

Detailed Comparison

Amazon RDS
Amazon RDS
Oracle
Oracle

Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.

Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.

Pre-configured Parameters;Monitoring and Metrics;Automatic Software Patching;Automated Backups;DB Snapshots;DB Event Notifications;Multi-Availability Zone (Multi-AZ) Deployments;Provisioned IOPS;Push-Button Scaling;Automatic Host Replacement;Replication;Isolation and Security
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Statistics
Stacks
16.2K
Stacks
2.6K
Followers
10.8K
Followers
1.8K
Votes
761
Votes
113
Pros & Cons
Pros
  • 165
    Reliable failovers
  • 156
    Automated backups
  • 130
    Backed by amazon
  • 92
    Db snapshots
  • 87
    Multi-availability
Pros
  • 44
    Reliable
  • 33
    Enterprise
  • 15
    High Availability
  • 5
    Expensive
  • 5
    Hard to maintain
Cons
  • 14
    Expensive

What are some alternatives to Amazon RDS, Oracle?

MongoDB

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

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