DBMS > AnzoGraph DB vs. Axibase vs. Snowflake vs. Vertica

System Properties Comparison AnzoGraph DB vs. Axibase vs. Snowflake vs. Vertica

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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

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

Cloud-based data warehousing service for structured and semi-structured data

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Graph DBMS
RDF store

Time Series DBMS

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.22
Rank	#302	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	0.21
Rank	#308	Overall
	#25	Time Series DBMS

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

cambridgesemantics.com/anzograph

axibase.com/docs/atsd/finance

www.snowflake.com

www.vertica.com

Technical documentation

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

docs.snowflake.net/manuals/index.html

vertica.com/documentation

Developer

Cambridge Semantics

Axibase Corporation

Snowflake Computing Inc.

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2018

2013

2014

2005

Current release

2.3, January 2021

15585

12.0.3, January 2023

License

Commercial or Open Source

commercial

free trial version available

commercial

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

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Server operating systems

Linux

hosted

Linux

Data scheme

Schema-free and OWL/RDFS-schema support

yes

support of semi-structured data formats (JSON, XML, Avro)

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes

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

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

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

SQL-like query language

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

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

CLI Client
JDBC
ODBC

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

C++
Java
Python

Go
Java
PHP
Python
R
Ruby

JavaScript (Node.js)
Python

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

user defined functions

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

similar concept for controling cloud resources

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

yes

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Source-replica replication

yes

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

Bi-directional Spark integration

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

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

Users with fine-grained authorization concept, user roles and pluggable authentication

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

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