DBMS > AnzoGraph DB vs. HarperDB vs. Machbase Neo vs. MaxDB vs. Snowflake

System Properties Comparison AnzoGraph DB vs. HarperDB vs. Machbase Neo vs. MaxDB vs. Snowflake

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

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.

TimeSeries DBMS for AIoT and BigData

A robust and reliable RDBMS optimized to run all major SAP solutions

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

Primary database model

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

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	2.32
Rank	#112	Overall
	#55	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

cambridgesemantics.com/anzograph

Technical documentation

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

docs.harperdb.io/docs

machbase.com/dbms

maxdb.sap.com/documentation

docs.snowflake.net/manuals/index.html

Developer

Cambridge Semantics

HarperDB

Machbase

SAP, acquired from Software AG (Adabas-D) in 1997

Snowflake Computing Inc.

Initial release

2018

2017

2013

1984

2014

Current release

2.3, January 2021

3.1, August 2021

V8.0, August 2023

7.9.10.12, February 2024

License

Commercial or Open Source

commercial

free trial version available

commercial

free community edition available

commercial

free test version available

commercial

Limited community edition free

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

Node.js

C++

Server operating systems

Linux

Linux
OS X

Linux
macOS
Windows

AIX
HP-UX
Linux
Solaris
Windows

hosted

Data scheme

Schema-free and OWL/RDFS-schema support

dynamic schema

yes

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

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

SQL

Support of SQL

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

SQL-like data manipulation statements

SQL-like query language

yes

APIs and other access methods

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

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

ADO.NET
JDBC
ODBC
WebDAV

CLI Client
JDBC
ODBC

Supported programming languages

C++
Java
Python

.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#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

.Net
C#
Java
Perl
PHP
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

Custom Functions

since release 3.1

yes

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

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

Sharding

none

yes

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

selectable replication factor

Source-replica replication

yes

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

Foreign keys

Referential integrity

not needed in graphs

yes

Transaction concepts

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

ACID

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

volatile and lookup table

User concepts

Access control

Access rights for users and roles

simple password-based access control

fine grained access rights according to SQL-standard

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

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.