DBMS > AnzoGraph DB vs. Bangdb vs. Brytlyt vs. MaxDB vs. Snowflake

System Properties Comparison AnzoGraph DB vs. Bangdb vs. Brytlyt 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

Converged and high performance database for device data, events, time series, document and graph

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

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

Graph DBMS
RDF store

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	0.29
Rank	#288	Overall
	#131	Relational 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.bangdb.com

docs.brytlyt.io

maxdb.sap.com/documentation

docs.snowflake.net/manuals/index.html

Developer

Cambridge Semantics

Sachin Sinha, BangDB

Brytlyt

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

Snowflake Computing Inc.

Initial release

2018

2012

2016

1984

2014

Current release

2.3, January 2021

BangDB 2.0, October 2021

5.0, August 2023

7.9.10.12, February 2024

License

Commercial or Open Source

commercial

free trial version available

Open Source

BSD 3

commercial

Limited community edition free

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

C, C++ and CUDA

C++

Server operating systems

Linux

Linux
OS X
Windows

AIX
HP-UX
Linux
Solaris
Windows

hosted

Data scheme

Schema-free and OWL/RDFS-schema support

schema-free

yes

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

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

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

specific XML-type available, but no XML query functionality.

yes

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

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

SQL like support with command line tool

yes

APIs and other access methods

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

Proprietary protocol
RESTful HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

ADO.NET
JDBC
ODBC
WebDAV

CLI Client
JDBC
ODBC

Supported programming languages

C++
Java
Python

C
C#
C++
Java
Python

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

.Net
C#
Java
Perl
PHP
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

in PL/pgSQL

yes

user defined functions

Triggers

yes, Notifications (with Streaming only)

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

selectable replication factor, Knob for CAP (enterprise version only)

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

Tunable consistency, set CAP knob accordingly

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

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

yes, run db with in-memory only mode

User concepts

Access control

Access rights for users and roles

yes (enterprise version only)

fine grained access rights according to SQL-standard

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

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