DBMS > Blazegraph vs. Databend vs. Microsoft Azure Cosmos DB vs. NSDb

System Properties Comparison Blazegraph vs. Databend vs. Microsoft Azure Cosmos DB vs. NSDb

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Editorial information provided by DB-Engines

Name

Amazon has acquired Blazegraph's domain and (probably) product. It is said that Amazon Neptune is based on Blazegraph.

Description

High-performance graph database supporting Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop3, blueprints, vertex-centric) APIs with scale-out and High Availability.

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

Globally distributed, horizontally scalable, multi-model database service

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Primary database model

Graph DBMS
RDF store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	0.74
Rank	#217	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	0.28
Rank	#287	Overall
	#131	Relational DBMS

Score	24.97
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Website

blazegraph.com

github.com/datafuselabs/databend
www.databend.com

azure.microsoft.com/services/cosmos-db

nsdb.io

Technical documentation

wiki.blazegraph.com

docs.databend.com

learn.microsoft.com/azure/cosmos-db

nsdb.io/Architecture

Developer

Blazegraph

Databend Labs

Microsoft

Initial release

2006

2021

2014

2017

Current release

2.1.5, March 2019

1.0.59, April 2023

License

Commercial or Open Source

Open Source

extended commercial license available

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Rust

Java, Scala

Server operating systems

Linux
OS X
Windows

hosted
Linux
macOS

hosted

Linux
macOS

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

RDF literal types

yes

JSON types

yes: int, bigint, decimal, string

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

all fields are automatically indexed

SQL

Support of SQL

SPARQL is used as query language

yes

SQL-like query language

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

CLI Client
JDBC
RESTful HTTP API

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

gRPC
HTTP REST
WebSocket

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

Go
Java
JavaScript (Node.js)
Python
Rust

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

Java
Scala

Server-side scripts

Stored procedures

yes

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in Graphs

Transaction concepts

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

ACID

yes

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

User concepts

Access control

Security and Authentication via Web Application Container (Tomcat, Jetty)

Users with fine-grained authorization concept, user roles

Access rights can be defined down to the item level

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