DBMS > Amazon Aurora vs. Cassandra vs. EXASOL vs. RocksDB vs. Snowflake

System Properties Comparison Amazon Aurora vs. Cassandra vs. EXASOL vs. RocksDB vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Secondary database models

Document store

Vector DBMS

starting with release V5

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	7.91
Rank	#50	Overall
	#32	Relational DBMS

Score	101.89
Rank	#12	Overall
	#1	Wide column stores

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

aws.amazon.com/rds/aurora

Technical documentation

docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_Aurora.html

cassandra.apache.org/doc/latest

www.exasol.com/resources

github.com/facebook/rocksdb/wiki

docs.snowflake.net/manuals/index.html

Developer

Amazon

Apache Software Foundation

Apache top level project, originally developped by Facebook

Exasol

Facebook, Inc.

Snowflake Computing Inc.

Initial release

2015

2008

2000

2013

2014

Current release

4.1.3, July 2023

8.11.4, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

Open Source

BSD

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

hosted

BSD
Linux
OS X
Windows

Linux

hosted

Data scheme

yes

schema-free

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

restricted

only equality queries, not always the best performing solution

yes

SQL

Support of SQL

yes

SQL-like SELECT, DML and DDL statements (CQL)

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

.Net
JDBC
ODBC
WebSocket

C++ API
Java API

CLI Client
JDBC
ODBC

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

Java
Lua
Python
R

C
C++
Go
Java
Perl
Python
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

no "single point of failure"

Sharding

horizontal partitioning

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

Representation of geographical distribution of servers is possible

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Atomicity and isolation are supported for single operations

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users can be defined per object

Access rights for users, groups and roles according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

More information provided by the system vendor

Specific characteristics

Apache Cassandra is the leading NoSQL, distributed database management system, well...
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Competitive advantages

No single point of failure ensures 100% availability . Operational simplicity for...
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Typical application scenarios

Internet of Things (IOT), fraud detection applications, recommendation engines, product...
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Key customers

Apple, Netflix, Uber, ING,, Intuit,Fidelity, NY Times, Outbrain, BazaarVoice, Best...
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Market metrics

Cassandra is used by 40% of the Fortune 100.
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Licensing and pricing models

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