DBMS > BoltDB vs. Microsoft Azure Cosmos DB vs. OrientDB vs. SQream DB vs. Teradata Aster

System Properties Comparison BoltDB vs. Microsoft Azure Cosmos DB vs. OrientDB vs. SQream DB vs. Teradata Aster

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

An embedded key-value store for Go.

Globally distributed, horizontally scalable, multi-model database service

Multi-model DBMS (Document, Graph, Key/Value)

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

Platform for big data analytics on multistructured data sources and types

Primary database model

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Document store
Graph DBMS
Key-value store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

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

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

Website

github.com/boltdb/bolt

azure.microsoft.com/services/cosmos-db

orientdb.org

sqream.com

Technical documentation

learn.microsoft.com/azure/cosmos-db

www.orientdb.com/docs/last/index.html

docs.sqream.com

Developer

Microsoft

OrientDB LTD; CallidusCloud; SAP

SQream Technologies

Teradata

Initial release

2013

2014

2010

2017

2005

Current release

3.2.29, March 2024

2022.1.6, December 2022

License

Commercial or Open Source

Open Source

MIT License

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.

Implementation language

Java

C++, CUDA, Haskell, Java, Scala

Server operating systems

BSD
Linux
OS X
Solaris
Windows

hosted

All OS with a Java JDK (>= JDK 6)

Linux

Data scheme

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

Typing

predefined data types such as float or date

yes

JSON types

yes

yes, ANSI Standard SQL Types

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

in Aster File Store

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language

SQL-like query language, no joins

yes

APIs and other access methods

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

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

.Net
JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

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

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

C++
Java
JavaScript (Node.js)
Python

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

JavaScript

Java, Javascript

user defined functions in Python

R packages

Triggers

JavaScript

Hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

horizontal and vertical partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Implicit feature of the cloud service

Multi-source replication

none

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

could be achieved with distributed queries

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

none

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

relationship in graphs

Transaction concepts

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

yes

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

Access rights can be defined down to the item level

Access rights for users and roles; record level security configurable

fine grained access rights according to SQL-standard

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