Overview

Oracle

Stacks2.4K

Followers1.8K

Votes113

WatermelonDB

Stacks12

Followers123

Votes1

GitHub Stars11.3K

Forks626

Oracle vs WatermelonDB: What are the differences?

Data Model: Oracle uses a relational database management system (RDBMS) with tables, rows, and columns, while WatermelonDB is based on NoSQL database with object-oriented data model and document-based storage.
Offline Support: WatermelonDB provides built-in offline-first support, allowing for seamless data synchronization and offline access, whereas Oracle requires additional setup and configurations for offline capabilities.
Performance: WatermelonDB is optimized for mobile applications, offering faster data retrieval and storage for better user experience on mobile devices, unlike Oracle which may have performance issues when used in mobile applications.
Size and Footprint: WatermelonDB has a smaller footprint and is designed for offline-first mobile apps, making it more lightweight and suitable for mobile devices with limited storage capacity compared to Oracle which may have a larger size and more features best suited for traditional server-based applications.
Query Language: Oracle uses SQL for data manipulation and retrieval, while WatermelonDB utilizes its own query language optimized for client-side data fetching and syncing, providing a more efficient way to work with data in mobile applications.
Community Support and Documentation: Oracle has a larger community support and extensive documentation available, making it easier for developers to find resources and solutions to common issues, whereas WatermelonDB may have a smaller community and limited resources for troubleshooting and development assistance.

In Summary, Oracle and WatermelonDB differ in their data model, offline support, performance, size and footprint, query language, and community support, catering to different needs based on the type of application and platform being used.

Advice on Oracle, WatermelonDB

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.

495k views495k

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	WatermelonDB
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.	WatermelonDB is a new way of dealing with user data in React Native and React web apps. It's optimized for building complex applications in React Native, and the number one goal is real-world performance. In simple words, your app must launch fast.
Statistics
GitHub Stars -	GitHub Stars 11.3K
GitHub Forks -	GitHub Forks 626
Stacks 2.4K	Stacks 12
Followers 1.8K	Followers 123
Votes 113	Votes 1
Pros & Cons
Pros 44 Reliable 33 Enterprise 15 High Availability 5 Expensive 5 Hard to maintain Cons 14 Expensive	Pros 1 Undefined is not an object (evaluating 'columnSchema.ty
Integrations
No integrations available	RxJS React SQLite React Native

What are some alternatives to Oracle, WatermelonDB?

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.

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.