DBMS > AnzoGraph DB vs. Google BigQuery vs. H2 vs. Netezza vs. XTDB

System Properties Comparison AnzoGraph DB vs. Google BigQuery vs. H2 vs. Netezza vs. XTDB

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

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Data warehouse and analytics appliance part of IBM PureSystems

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Graph DBMS
RDF store

Relational DBMS

Document store

Secondary database models

Spatial 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	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	8.59
Rank	#45	Overall
	#29	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

cambridgesemantics.com/anzograph

cloud.google.com/bigquery

www.h2database.com

www.ibm.com/products/netezza

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

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

cloud.google.com/bigquery/docs

www.h2database.com/html/main.html

www.xtdb.com/docs

Developer

Cambridge Semantics

Google

Thomas Mueller

IBM

Juxt Ltd.

Initial release

2018

2010

2005

2000

2019

Current release

2.3, January 2021

2.2.220, July 2023

1.19, September 2021

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

commercial

Open Source

MIT License

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

Clojure

Server operating systems

Linux

hosted

All OS with a Java VM

Linux

included in appliance

All OS with a Java 8 (and higher) VM
Linux

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

limited SQL, making use of Apache Calcite

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

JDBC
ODBC
OLE DB

HTTP REST
JDBC

Supported programming languages

C++
Java
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java

C
C++
Fortran
Java
Lua
Perl
Python
R

Clojure
Java

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

in JavaScript

Java Stored Procedures and User-Defined Functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

With clustering: 2 database servers on different computers operate on identical copies of a database

Source-replica replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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)

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

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