Overview

Oracle

Stacks2.4K

Followers1.8K

Votes113

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

Cassandra vs Oracle: What are the differences?

Introduction

Cassandra and Oracle are both popular database management systems used in various industries. While they have similarities and serve the same purpose, there are key differences between the two. This markdown code aims to provide a concise overview of the main distinctions between Cassandra and Oracle.

Data Model: Cassandra is a NoSQL database that follows a wide-column data model, storing data in a distributed manner across multiple nodes. On the other hand, Oracle is a traditional relational database that adheres to a tabular data model, utilizing tables with rows and columns to store data.
Scalability: Cassandra is designed to scale horizontally, meaning it can handle large amounts of data and traffic by adding more commodity hardware. It offers excellent linear scalability and can distribute data across multiple nodes. In contrast, Oracle primarily scales vertically, relying on increasing the resources of a single server to handle more data and users.
Availability and Fault Tolerance: Cassandra is known for its high availability and fault tolerance. It achieves this through its distributed architecture and replication mechanism, allowing the system to continue functioning even in the face of node failures. Oracle, while offering high availability options, typically relies on backups and standby databases for fault tolerance.
Consistency Model: Cassandra follows the eventual consistency model, which means that updates may not immediately propagate across all nodes but will eventually merge and become consistent. Oracle, being a relational database, generally provides strong consistency, ensuring that transactions are processed in an ordered and consistent manner.
Schema Flexibility: Cassandra is schema-free or schema-less, allowing for flexibility in adding or modifying columns without affecting existing data. This makes it suitable for handling evolving or rapidly changing data requirements. In contrast, Oracle follows a rigid schema approach, where the database structure must be defined and adhered to before inserting data.
Cost: Cassandra is open-source and free to use, making it an attractive option for organizations with limited budgets. Oracle, on the other hand, is a commercial database management system and requires licensing, which can be costly depending on the scale and usage requirements.

In summary, Cassandra and Oracle differ in their data models, scalability approaches, availability mechanisms, consistency models, schema flexibility, and cost. These distinctions make each database management system advantageous in specific use cases and environments.

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Advice on Oracle, Cassandra

Micha

CEO & Co-Founder at Dechea

May 27, 2022

Decided

Fauna is a serverless database where you store data as JSON. Also, you have build in a HTTP GraphQL interface with a full authentication & authorization layer. That means you can skip your Backend and call it directly from the Frontend. With the power, that you can write data transformation function within Fauna with her own language called FQL, we're getting a blazing fast application.

Also, Fauna takes care about scaling and backups (All data are sharded on three different locations on the globe). That means we can fully focus on writing business logic and don't have to worry anymore about infrastructure.

93k views93k

Comments

Krishna Chaitanya

Head of Technology at Adonmo

Jun 27, 2021

Review

For such a more realtime-focused, data-centered application like an exchange, it's not the frontend or backend that matter much. In fact for that, they can do away with any of the popular frameworks like React/Vue/Angular for the frontend and Go/Python for the backend. For example uniswap's frontend (although much simpler than binance) is built in React. The main interesting part here would be how they are able to handle updating data so quickly. In my opinion, they might be heavily reliant on realtime processing systems like Kafka+Kafka Streams, Apache Flink or Apache Spark Stream or similar. For more processing heavy but not so real-time processing, they might be relying on OLAP and/or warehousing tools like Cassandra/Redshift. They could have also optimized few high frequent queries using NoSQL stores like mongodb (for persistance) and in-memory cache like Redis (for further perfomance boost to get millisecond latencies).

53.8k views53.8k

Comments

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

Detailed Comparison

Oracle	Cassandra
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.	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.
Statistics
GitHub Stars -	GitHub Stars 9.5K
GitHub Forks -	GitHub Forks 3.8K
Stacks 2.4K	Stacks 3.6K
Followers 1.8K	Followers 3.5K
Votes 113	Votes 507
Pros & Cons
Pros 44 Reliable 33 Enterprise 15 High Availability 5 Hard to maintain 5 Expensive Cons 14 Expensive	Pros 119 Distributed 98 High performance 81 High availability 74 Easy scalability 53 Replication Cons 3 Reliability of replication 1 Updates 1 Size

What are some alternatives to Oracle, Cassandra?

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.

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.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.