DBMS > 4D vs. Blueflood vs. Cassandra vs. Hazelcast vs. Teradata

System Properties Comparison 4D vs. Blueflood vs. Cassandra vs. Hazelcast vs. Teradata

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Scalable TimeSeries DBMS based on Cassandra

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

A widely adopted in-memory data grid

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Secondary database models

Vector DBMS

starting with release V5

Document store

JSON support with IMDG 3.12

Document store
Graph DBMS
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	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	101.89
Rank	#12	Overall
	#1	Wide column stores

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

Technical documentation

developer.4d.com

github.com/rax-maas/blueflood/wiki

cassandra.apache.org/doc/latest

hazelcast.org/imdg/docs

docs.teradata.com

Developer

4D, Inc

Rackspace

Apache Software Foundation

Apache top level project, originally developped by Facebook

Hazelcast

Teradata

Initial release

1984

2013

2008

1984

Current release

v20, April 2023

4.1.3, July 2023

5.3.6, November 2023

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache version 2

Open Source

Apache Version 2; commercial licenses available

commercial

Cloud-based only

Only available as a cloud service

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

Server operating systems

OS X
Windows

Linux
OS X

BSD
Linux
OS X
Windows

All OS with a Java VM

hosted
Linux

Data scheme

yes

predefined scheme

schema-free

yes

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

restricted

only equality queries, not always the best performing solution

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

yes

close to SQL 92

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

SQL-like query language

yes

SQL 2016 + extensions

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

HTTP REST

Proprietary protocol

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

JCache
JPA
Memcached protocol
RESTful HTTP API

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

4D proprietary IDE
PHP

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding

no "single point of failure"

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor

based on Cassandra

selectable replication factor

Representation of geographical distribution of servers is possible

yes

Replicated Map

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

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

one or two-phase-commit; repeatable reads; read commited

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

Users and groups

Access rights for users can be defined per object

Role-based access control

fine grained access rights according to SQL-standard

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.