DBMS > AnzoGraph DB vs. MaxDB vs. Oracle Berkeley DB vs. Snowflake vs. Spark SQL

System Properties Comparison AnzoGraph DB vs. MaxDB vs. Oracle Berkeley DB vs. Snowflake vs. Spark SQL

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

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

Widely used in-process key-value store

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Graph DBMS
RDF store

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational 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	2.32
Rank	#112	Overall
	#55	Relational DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

cambridgesemantics.com/anzograph

maxdb.sap.com

www.oracle.com/database/technologies/related/berkeleydb.html

www.snowflake.com

spark.apache.org/sql

Technical documentation

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

maxdb.sap.com/documentation

docs.oracle.com/cd/E17076_05/html/index.html

docs.snowflake.net/manuals/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Cambridge Semantics

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

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2018

1984

1994

2014

Current release

2.3, January 2021

7.9.10.12, February 2024

18.1.40, May 2020

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

free trial version available

commercial

Limited community edition free

Open Source

commercial license available

commercial

Open Source

Apache 2.0

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

C++

C, Java, C++ (depending on the Berkeley DB edition)

Scala

Server operating systems

Linux

AIX
HP-UX
Linux
Solaris
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

hosted

Linux
OS X
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-free

yes

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

yes

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.

yes

only with the Berkeley DB XML edition

yes

Secondary indexes

yes

SQL

Support of SQL

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

yes

SQL interfaced based on SQLite is available

yes

SQL-like DML and DDL statements

APIs and other access methods

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

ADO.NET
JDBC
ODBC
WebDAV

CLI Client
JDBC
ODBC

JDBC
ODBC

Supported programming languages

C++
Java
Python

.Net
C#
Java
Perl
PHP
Python

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

JavaScript (Node.js)
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

user defined functions

Triggers

yes

only for the SQL API

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Source-replica replication

yes

none

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

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

fine grained access rights according to SQL-standard

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

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