Azure Cosmos DB vs Cassandra

Overview

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

Azure Cosmos DB

Stacks594

Followers1.1K

Votes130

Azure Cosmos DB vs Cassandra: What are the differences?

Azure Cosmos DB and Apache Cassandra are both popular NoSQL databases that are widely used for building scalable and highly available applications. Let's explore the key differences between them.

Consistency models: Azure Cosmos DB provides five well-defined consistency models, including strong, bounded staleness, session, consistent prefix, and eventual consistency. In contrast, Cassandra offers tunable consistency, where users can customize the level of consistency for each read and write operation.
Data distribution: Azure Cosmos DB uses a partitioning scheme called "container" to distribute data across multiple physical partitions automatically. This enables automatic load balancing and horizontal scalability. On the other hand, Cassandra has a decentralized architecture where data is partitioned and distributed across multiple nodes using a consistent hashing algorithm.
Multi-model capabilities: Azure Cosmos DB is a multi-model database that supports document, key-value, graph, and columnar data models, allowing developers to choose the best model for their application needs. Cassandra, on the other hand, primarily supports the columnar model, although some support for other models can be achieved through customizations and extensions.
Scalability and performance: Azure Cosmos DB offers elastic scalability with automatic partitioning and load balancing, enabling applications to scale seamlessly as the workload grows. It is also designed to deliver low-latency performance even at global scale. Cassandra provides linear scalability by adding more nodes to the cluster. However, scaling Cassandra requires manual configuration and management.
Global distribution: Azure Cosmos DB provides built-in support for global distribution, allowing data to be stored and accessed from multiple regions around the world with low latency. Cassandra, on the other hand, requires manual configuration and replication to achieve global distribution, making it more complex and time-consuming.
Backup and recovery: Azure Cosmos DB offers built-in backup and restore capabilities, allowing users to easily recover their data in case of a disaster or accidental data loss. Cassandra requires users to set up their own backup and recovery mechanisms, which can be more complex and time-consuming.

In summary, Azure Cosmos DB, a fully managed cloud-native database service, provides global distribution, multi-model support, and comprehensive SLAs, making it suitable for building globally distributed applications with low-latency access. Cassandra, an open-source distributed database, offers high availability, fault tolerance, and tunable consistency levels, providing flexibility in deployment and customization options for large-scale distributed systems.

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CLI (Node.js)

Manual

Advice on Cassandra, Azure Cosmos DB

Vinay

Head of Engineering

Sep 19, 2019

Needs advice

The problem I have is - we need to process & change(update/insert) 55M Data every 2 min and this updated data to be available for Rest API for Filtering / Selection. Response time for Rest API should be less than 1 sec.

The most important factors for me are processing and storing time of 2 min. There need to be 2 views of Data One is for Selection & 2. Changed data.

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Comments

Detailed Comparison

Cassandra	Azure Cosmos DB
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.	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
GitHub Stars 9.5K	GitHub Stars -
GitHub Forks 3.8K	GitHub Forks -
Stacks 3.6K	Stacks 594
Followers 3.5K	Followers 1.1K
Votes 507	Votes 130
Pros & Cons
Pros 119 Distributed 98 High performance 81 High availability 74 Easy scalability 53 Replication Cons 3 Reliability of replication 1 Size 1 Updates	Pros 28 Best-of-breed NoSQL features 22 High scalability 15 Globally distributed 14 Automatic indexing over flexible json data model 10 Always on with 99.99% availability sla 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 Cassandra, 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.

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.

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.

Related Comparisons

Azure Cosmos DB vs Cassandra: What are the differences?

Azure Cosmos DB and Apache Cassandra are both popular NoSQL databases that are widely used for building scalable and highly available applications. Let's explore the key differences between them.

Consistency models: Azure Cosmos DB provides five well-defined consistency models, including strong, bounded staleness, session, consistent prefix, and eventual consistency. In contrast, Cassandra offers tunable consistency, where users can customize the level of consistency for each read and write operation.
Data distribution: Azure Cosmos DB uses a partitioning scheme called "container" to distribute data across multiple physical partitions automatically. This enables automatic load balancing and horizontal scalability. On the other hand, Cassandra has a decentralized architecture where data is partitioned and distributed across multiple nodes using a consistent hashing algorithm.
Multi-model capabilities: Azure Cosmos DB is a multi-model database that supports document, key-value, graph, and columnar data models, allowing developers to choose the best model for their application needs. Cassandra, on the other hand, primarily supports the columnar model, although some support for other models can be achieved through customizations and extensions.
Scalability and performance: Azure Cosmos DB offers elastic scalability with automatic partitioning and load balancing, enabling applications to scale seamlessly as the workload grows. It is also designed to deliver low-latency performance even at global scale. Cassandra provides linear scalability by adding more nodes to the cluster. However, scaling Cassandra requires manual configuration and management.
Global distribution: Azure Cosmos DB provides built-in support for global distribution, allowing data to be stored and accessed from multiple regions around the world with low latency. Cassandra, on the other hand, requires manual configuration and replication to achieve global distribution, making it more complex and time-consuming.
Backup and recovery: Azure Cosmos DB offers built-in backup and restore capabilities, allowing users to easily recover their data in case of a disaster or accidental data loss. Cassandra requires users to set up their own backup and recovery mechanisms, which can be more complex and time-consuming.

Azure Cosmos DB vs Cassandra

Overview