DBMS > AnzoGraph DB vs. Google BigQuery vs. HarperDB vs. Sequoiadb

System Properties Comparison AnzoGraph DB vs. Google BigQuery vs. HarperDB vs. Sequoiadb

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Editorial information provided by DB-Engines

Name

Description

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

Large scale data warehouse service with append-only tables

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

NewSQL database with distributed OLTP and SQL

Primary database model

Graph DBMS
RDF store

Relational DBMS

Document store

Document store
Relational DBMS

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

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Website

cambridgesemantics.com/anzograph

cloud.google.com/bigquery

www.harperdb.io

www.sequoiadb.com

Technical documentation

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

cloud.google.com/bigquery/docs

docs.harperdb.io/docs

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Cambridge Semantics

Google

HarperDB

Sequoiadb Ltd.

Initial release

2018

2010

2017

2013

Current release

2.3, January 2021

3.1, August 2021

License

Commercial or Open Source

commercial

free trial version available

commercial

free community edition available

Open Source

Server: AGPL; Client: Apache V2

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

Node.js

C++

Server operating systems

Linux

hosted

Linux
OS X

Linux

Data scheme

Schema-free and OWL/RDFS-schema support

yes

dynamic schema

schema-free

Typing

predefined data types such as float or date

yes

JSON data types

yes

oid, date, timestamp, binary, regex

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

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

yes

SQL-like data manipulation statements

SQL-like query language

APIs and other access methods

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

RESTful HTTP/JSON API

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

proprietary protocol using JSON

Supported programming languages

C++
Java
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

in JavaScript

Custom Functions

since release 3.1

JavaScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

A table resides as a whole on one (or more) nodes in a cluster

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

yes

the nodes on which a table resides can be defined

Source-replica replication

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

Eventual Consistency

Foreign keys

Referential integrity

not needed in graphs

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

Atomic execution of specific operations

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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

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

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles

simple password-based access control

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