DBMS > AnzoGraph DB vs. Apache Phoenix vs. EJDB vs. Microsoft SQL Server vs. SAP HANA

System Properties Comparison AnzoGraph DB vs. Apache Phoenix vs. EJDB vs. Microsoft SQL Server vs. SAP HANA

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 scale-out RDBMS with evolutionary schema built on Apache HBase

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Microsofts flagship relational DBMS

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

cambridgesemantics.com/anzograph

phoenix.apache.org

github.com/Softmotions/ejdb

www.microsoft.com/en-us/sql-server

www.sap.com/products/hana.html

Technical documentation

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

phoenix.apache.org

github.com/Softmotions/ejdb/blob/master/README.md

learn.microsoft.com/en-US/sql/sql-server

help.sap.com/hana

Developer

Cambridge Semantics

Apache Software Foundation

Softmotions

Microsoft

SAP

Initial release

2018

2014

2012

1989

2010

Current release

2.3, January 2021

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

SQL Server 2022, November 2022

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache Version 2.0

Open Source

GPLv2

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

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Implementation language

Java

C++

Server operating systems

Linux

Linux
Unix
Windows

server-less

Linux
Windows

Appliance or cloud-service

Data scheme

Schema-free and OWL/RDFS-schema support

yes

late-bound, schema-on-read capabilities

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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.

yes

APIs and other access methods

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

JDBC

in-process shared library

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JDBC
ODBC

Supported programming languages

C++
Java
Python

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Multi-source replication
Source-replica replication

none

yes, but depending on the SQL-Server Edition

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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 Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

fine grained access rights according to SQL-standard

yes

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