DBMS > Blueflood vs. Datastax Enterprise vs. Google Cloud Bigtable vs. MonetDB

System Properties Comparison Blueflood vs. Datastax Enterprise vs. Google Cloud Bigtable vs. MonetDB

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

DataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

A relational database management system that stores data in columns

Primary database model

Time Series DBMS

Wide column store

Key-value store
Wide column store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

Document store
Spatial DBMS

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

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	5.80
Rank	#60	Overall
	#4	Wide column stores

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Website

blueflood.io

www.datastax.com/products/datastax-enterprise

cloud.google.com/bigtable

www.monetdb.org

Technical documentation

github.com/rax-maas/blueflood/wiki

docs.datastax.com

cloud.google.com/bigtable/docs

www.monetdb.org/Documentation

Developer

Rackspace

DataStax

Google

MonetDB BV

Initial release

2013

2011

2015

2004

Current release

6.8, April 2020

Dec2023 (11.49), December 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Mozilla Public License 2.0

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.

Datastax Astra DB: Astra DB simplifies cloud-native Cassandra application development for your apps, microservices and functions. Deploy in minutes on AWS, Google Cloud, Azure, and have it managed for you by the experts, with serverless, pay-as-you-go pricing.

Implementation language

Java

Server operating systems

Linux
OS X

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements (CQL); Spark SQL

yes

SQL 2003 with some extensions

APIs and other access methods

HTTP REST

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Supported programming languages

C
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

Server-side scripts

Stored procedures

yes, in SQL, C, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

no "single point of failure"

Sharding

Sharding via remote tables

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

configurable replication factor, datacenter aware, advanced replication for edge computing

Internal replication in Colossus, and regional replication between two clusters in different zones

none

Source-replica replication available in experimental status

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomicity and isolation are supported for single operations

Atomic single-row operations

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

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)

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

DataStax Enterprise is scale-out data infrastructure for enterprises that need to...
» more

Competitive advantages

Supporting the following application requirements: Zero downtime - Built on Apache...
» more

Typical application scenarios

Applications that must be massively and linearly scalable with 100% uptime and able...
» more

Key customers

Capital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
» more

Market metrics

Among the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
» more

Licensing and pricing models

Annual subscription
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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