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Azure Cosmos DB vs Oracle: What are the differences?
Introduction
Azure Cosmos DB and Oracle are both popular database management systems that offer a wide range of features and capabilities. However, there are several key differences between the two that set them apart. In this article, we will explore and compare these differences in detail.
Data Models: Azure Cosmos DB is a NoSQL database that supports multiple data models, including document, key-value, graph, and column-family. On the other hand, Oracle is a relational database that follows the traditional table-based data model.
Scalability: Azure Cosmos DB is designed to scale horizontally across multiple regions, allowing for global distribution and high availability. It offers automatic and transparent scaling of throughput and storage based on application demand. In contrast, Oracle relies on vertical scaling, where additional resources are added to a single server to handle increased load.
Multi-Model Support: Azure Cosmos DB supports multiple data models, allowing developers to use different models for different parts of their application. This flexibility enables efficient data modeling and eliminates the need for separate databases for each data model. Oracle, on the other hand, primarily supports the relational data model.
Global Distribution: Azure Cosmos DB provides built-in global distribution, allowing data to be replicated across multiple regions for seamless data access and lower latency. This global distribution enables developers to build highly available and low-latency applications that can serve users around the world. Oracle, on the other hand, requires additional configuration and setup to achieve global distribution.
Consistency Models: Azure Cosmos DB offers a range of consistency models, including strong, bounded staleness, session, and eventual consistency. These consistency models provide developers with fine-grained control over trade-offs between consistency, availability, and performance. Oracle, on the other hand, primarily offers strong consistency as the default model.
Pricing: Azure Cosmos DB offers a consumption-based pricing model, where customers only pay for the actual usage of the resources. This pay-as-you-go pricing allows for cost optimization and scalability. Oracle, on the other hand, has a more traditional licensing model, which may include fixed costs, maintenance fees, and additional charges for specific features.
In summary, Azure Cosmos DB and Oracle differ in their data models, scalability approaches, support for multiple data models, global distribution capabilities, consistency models, and pricing models. These differences highlight the unique strengths and use cases of each database management system, enabling developers to choose the one that best fits their specific requirements.
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 Cosmos DB
- Best-of-breed NoSQL features28
- High scalability22
- Globally distributed15
- Automatic indexing over flexible json data model14
- Tunable consistency10
- Always on with 99.99% availability sla10
- Javascript language integrated transactions and queries7
- Predictable performance6
- High performance5
- Analytics Store5
- Rapid Development2
- No Sql2
- Auto Indexing2
- Ease of use2
Pros of Oracle
- Reliable44
- Enterprise33
- High Availability15
- Hard to maintain5
- Expensive5
- Maintainable4
- Hard to use4
- High complexity3
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Cons of Azure Cosmos DB
- Pricing18
- Poor No SQL query support4
Cons of Oracle
- Expensive14
