DBMS > Cassandra vs. DuckDB vs. Google Cloud Datastore vs. Sequoiadb

System Properties Comparison Cassandra vs. DuckDB vs. Google Cloud Datastore vs. Sequoiadb

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Editorial information provided by DB-Engines

Name

Description

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

An embeddable, in-process, column-oriented SQL OLAP RDBMS

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

NewSQL database with distributed OLTP and SQL

Primary database model

Wide column store

Relational DBMS

Document store

Document store
Relational DBMS

Secondary database models

Vector DBMS

starting with release V5

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

Score	101.89
Rank	#12	Overall
	#1	Wide column stores

Score	4.57
Rank	#74	Overall
	#40	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.45
Rank	#261	Overall
	#41	Document stores
	#122	Relational DBMS

Website

cassandra.apache.org

duckdb.org

cloud.google.com/datastore

www.sequoiadb.com

Technical documentation

cassandra.apache.org/doc/latest

duckdb.org/docs

cloud.google.com/datastore/docs

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Apache Software Foundation

Apache top level project, originally developped by Facebook

Google

Sequoiadb Ltd.

Initial release

2008

2018

2008

2013

Current release

4.1.3, July 2023

0.10, February 2024

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

MIT License

commercial

Open Source

Server: AGPL; Client: Apache V2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Implementation language

Java

C++

Server operating systems

BSD
Linux
OS X
Windows

server-less

hosted

Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

yes

oid, date, timestamp, binary, regex

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.

Secondary indexes

restricted

only equality queries, not always the best performing solution

yes

SQL

Support of SQL

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

yes

SQL-like query language (GQL)

SQL-like query language

APIs and other access methods

Proprietary protocol

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

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

proprietary protocol using JSON

Supported programming languages

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

using Google App Engine

JavaScript

Triggers

yes

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Representation of geographical distribution of servers is possible

none

Multi-source replication using Paxos

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Eventual Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

Atomicity and isolation are supported for single operations

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

Access rights for users can be defined per object

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

simple password-based access control

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

Apache licenseÂ Pricing for commercial distributions provided by DataStax and available...
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