DBMS > BaseX vs. Google Cloud Bigtable vs. Microsoft Azure Cosmos DB vs. SQream DB

System Properties Comparison BaseX vs. Google Cloud Bigtable vs. Microsoft Azure Cosmos DB vs. SQream DB

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Editorial information provided by DB-Engines

Name

Description

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

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

Globally distributed, horizontally scalable, multi-model database service

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Native XML DBMS

Key-value store
Wide column store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	1.73
Rank	#142	Overall
	#4	Native XML DBMS

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

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

Website

basex.org

cloud.google.com/bigtable

azure.microsoft.com/services/cosmos-db

sqream.com

Technical documentation

docs.basex.org

cloud.google.com/bigtable/docs

learn.microsoft.com/azure/cosmos-db

docs.sqream.com

Developer

BaseX GmbH

Google

Microsoft

SQream Technologies

Initial release

2007

2015

2014

2017

Current release

10.7, August 2023

2022.1.6, December 2022

License

Commercial or Open Source

Open Source

BSD license

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.

Implementation language

Java

C++, CUDA, Haskell, Java, Scala

Server operating systems

Linux
OS X
Windows

hosted

Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

XQuery supports types

yes

JSON types

yes, ANSI Standard SQL Types

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

All properties auto-indexed by default

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

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

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

.Net
JDBC
ODBC

Supported programming languages

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

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

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

JavaScript

user defined functions in Python

Triggers

yes

via events

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

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

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

multiple readers, single writer

Atomic single-row operations

Multi-item ACID transactions with snapshot isolation within a partition

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.

User concepts

Access control

Users with fine-grained authorization concept on 4 levels

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

Access rights can be defined down to the item level

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