Azure Cosmos DB vs Oracle

Overview

Oracle

Stacks2.6K

Followers1.8K

Votes113

Azure Cosmos DB

Stacks594

Followers1.1K

Votes130

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.

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Advice on Oracle, Azure Cosmos DB

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

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

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

Oracle	Azure Cosmos DB
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.	Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development.
-	Fully managed with 99.99% Availability SLA;Elastically and highly scalable (both throughput and storage);Predictable low latency: <10ms @ P99 reads and <15ms @ P99 fully-indexed writes;Globally distributed with multi-region replication;Rich SQL queries over schema-agnostic automatic indexing;JavaScript language integrated multi-record ACID transactions with snapshot isolation;Well-defined tunable consistency models: Strong, Bounded Staleness, Session, and Eventual
Statistics
Stacks 2.6K	Stacks 594
Followers 1.8K	Followers 1.1K
Votes 113	Votes 130
Pros & Cons
Pros 44 Reliable 33 Enterprise 15 High Availability 5 Hard to maintain 5 Expensive Cons 14 Expensive	Pros 28 Best-of-breed NoSQL features 22 High scalability 15 Globally distributed 14 Automatic indexing over flexible json data model 10 Tunable consistency Cons 18 Pricing 4 Poor No SQL query support
Integrations
No integrations available	Azure Machine Learning MongoDB Hadoop Java Azure Functions Azure Container Service Azure Storage Azure Websites Apache Spark Python

What are some alternatives to Oracle, Azure Cosmos DB?

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

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

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

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 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

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 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.

Amazon DynamoDB

With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

Related Comparisons

Azure Cosmos DB vs Oracle: What are the differences?

Introduction

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.

Azure Cosmos DB vs Oracle

Overview