DBMS > 4D vs. Cassandra vs. Google Cloud Datastore vs. Kinetica

System Properties Comparison 4D vs. Cassandra vs. Google Cloud Datastore vs. Kinetica

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

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

Fully vectorized database across both GPUs and CPUs

Primary database model

Secondary database models

Vector DBMS

starting with release V5

Spatial DBMS
Time Series DBMS

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	101.89
Rank	#12	Overall
	#1	Wide column stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Website

www.4d.com

cassandra.apache.org

cloud.google.com/datastore

www.kinetica.com

Technical documentation

developer.4d.com

cassandra.apache.org/doc/latest

cloud.google.com/datastore/docs

docs.kinetica.com

Developer

4D, Inc

Apache Software Foundation

Apache top level project, originally developped by Facebook

Google

Kinetica

Initial release

1984

2008

2012

Current release

v20, April 2023

4.1.3, July 2023

7.1, August 2021

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

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, C++

Server operating systems

OS X
Windows

BSD
Linux
OS X
Windows

hosted

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

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

close to SQL 92

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

SQL-like query language (GQL)

SQL-like DML and DDL statements

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

Proprietary protocol

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

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

JDBC
ODBC
RESTful HTTP API

Supported programming languages

4D proprietary IDE
PHP

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

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

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

using Google App Engine

user defined functions

Triggers

yes

Callbacks using the Google Apps Engine

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

no "single point of failure"

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor

Representation of geographical distribution of servers is possible

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

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

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.

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Atomicity and isolation are supported for single operations

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

GPU vRAM or System RAM

User concepts

Access control

Users and groups

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)

Access rights for users and roles on table level

More information provided by the system vendor

Specific characteristics

Apache Cassandra is the leading NoSQL, distributed database management system, well...
» more

Competitive advantages

No single point of failure ensures 100% availability . Operational simplicity for...
» more

Typical application scenarios

Internet of Things (IOT), fraud detection applications, recommendation engines, product...
» more

Key customers

Apple, Netflix, Uber, ING,, Intuit,Fidelity, NY Times, Outbrain, BazaarVoice, Best...
» more

Market metrics

Cassandra is used by 40% of the Fortune 100.
» more

Licensing and pricing models

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

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.