DBMS > AnzoGraph DB vs. Atos Standard Common Repository vs. BigchainDB vs. Google BigQuery vs. LMDB

System Properties Comparison AnzoGraph DB vs. Atos Standard Common Repository vs. BigchainDB vs. Google BigQuery vs. LMDB

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

Large scale data warehouse service with append-only tables

A high performant, light-weight, embedded key-value database library

Primary database model

Graph DBMS
RDF store

Document store
Key-value store

Document store

Relational DBMS

Key-value store

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

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.99
Rank	#125	Overall
	#21	Key-value stores

Website

cambridgesemantics.com/anzograph

atos.net/en/convergence-creators/portfolio/standard-common-repository

www.bigchaindb.com

cloud.google.com/bigquery

www.symas.com/symas-embedded-database-lmdb

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

bigchaindb.readthedocs.io/en/latest

cloud.google.com/bigquery/docs

www.lmdb.tech/doc

Developer

Cambridge Semantics

Atos Convergence Creators

Google

Symas

Initial release

2018

2016

2010

2011

Current release

2.3, January 2021

1703

0.9.32, January 2024

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

AGPL v3

commercial

Open Source

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

Python

Server operating systems

Linux

hosted

Linux
Unix
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

Schema and schema-less with LDAP views

schema-free

yes

schema-free

Typing

predefined data types such as float or date

optional

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

Secondary indexes

yes

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

yes

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

LDAP

CLI Client
RESTful HTTP API

RESTful HTTP/JSON API

Supported programming languages

C++
Java
Python

All languages with LDAP bindings

Go
Haskell
Java
JavaScript
Python
Ruby

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

cell division

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

yes

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

Transaction concepts

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

ACID

Atomic execution of specific operations

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

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 and roles

LDAP bind authentication

yes

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

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