DBMS > AnzoGraph DB vs. Hive vs. Netezza vs. RocksDB

System Properties Comparison AnzoGraph DB vs. Hive vs. Netezza vs. RocksDB

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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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Data warehouse and analytics appliance part of IBM PureSystems

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Graph DBMS
RDF store

Relational DBMS

Key-value store

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	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	8.59
Rank	#45	Overall
	#29	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

cambridgesemantics.com/anzograph

hive.apache.org

www.ibm.com/products/netezza

rocksdb.org

Technical documentation

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

cwiki.apache.org/confluence/display/Hive/Home

github.com/facebook/rocksdb/wiki

Developer

Cambridge Semantics

Apache Software Foundation

initially developed by Facebook

IBM

Facebook, Inc.

Initial release

2018

2012

2000

2013

Current release

2.3, January 2021

3.1.3, April 2022

9.2.1, May 2024

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache Version 2

commercial

Open Source

BSD

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

Linux

All OS with a Java VM

Linux

included in appliance

Linux

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-free

Typing

predefined data types such as float or date

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 DML and DDL statements

yes

APIs and other access methods

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

JDBC
ODBC
Thrift

JDBC
ODBC
OLE DB

C++ API
Java API

Supported programming languages

C++
Java
Python

C++
Java
PHP
Python

C
C++
Fortran
Java
Lua
Perl
Python
R

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

user defined functions and integration of map-reduce

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

selectable replication factor

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Eventual Consistency

Foreign keys

Referential integrity

not needed in graphs

Transaction concepts

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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 rights for users, groups and roles

Users with fine-grained authorization concept

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