DBMS > AnzoGraph DB vs. Axibase vs. Google BigQuery vs. HarperDB vs. Netezza

System Properties Comparison AnzoGraph DB vs. Axibase vs. Google BigQuery vs. HarperDB vs. Netezza

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

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

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.

Data warehouse and analytics appliance part of IBM PureSystems

Primary database model

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	0.35
Rank	#282	Overall
	#25	Time Series DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	8.59
Rank	#45	Overall
	#29	Relational DBMS

Website

cambridgesemantics.com/anzograph

axibase.com/docs/atsd/finance

cloud.google.com/bigquery

www.harperdb.io

www.ibm.com/products/netezza

Technical documentation

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

cloud.google.com/bigquery/docs

docs.harperdb.io/docs

Developer

Cambridge Semantics

Axibase Corporation

Google

HarperDB

IBM

Initial release

2018

2013

2010

2017

2000

Current release

2.3, January 2021

15585

3.1, August 2021

License

Commercial or Open Source

commercial

free trial version available

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

commercial

free community edition available

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

Node.js

Server operating systems

Linux

hosted

Linux
OS X

Linux

included in appliance

Data scheme

Schema-free and OWL/RDFS-schema support

yes

dynamic schema

yes

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

yes

JSON data 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.

Secondary indexes

yes

SQL

Support of SQL

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

SQL-like query language

yes

SQL-like data manipulation statements

yes

APIs and other access methods

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

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

RESTful HTTP/JSON API

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

JDBC
ODBC
OLE DB

Supported programming languages

C++
Java
Python

Go
Java
PHP
Python
R
Ruby

.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

C
C++
Fortran
Java
Lua
Perl
Python
R

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

user defined functions

in JavaScript

Custom Functions

since release 3.1

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

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

Source-replica replication

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

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

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

ACID

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

Users with fine-grained authorization concept

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