DBMS > AnzoGraph DB vs. EsgynDB vs. Faircom DB vs. Microsoft Azure Data Explorer vs. Riak KV

System Properties Comparison AnzoGraph DB vs. EsgynDB vs. Faircom DB vs. Microsoft Azure Data Explorer vs. Riak KV

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

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Fully managed big data interactive analytics platform

Distributed, fault tolerant key-value store

Primary database model

Graph DBMS
RDF store

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

column oriented

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Document store

If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store

this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine

support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

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	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	0.29
Rank	#304	Overall
	#43	Key-value stores
	#136	Relational DBMS

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Website

cambridgesemantics.com/anzograph

www.esgyn.cn

www.faircom.com/products/faircom-db

azure.microsoft.com/services/data-explorer

Technical documentation

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

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

docs.microsoft.com/en-us/azure/data-explorer

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Cambridge Semantics

Esgyn

FairCom Corporation

Microsoft

OpenSource, formerly Basho Technologies

Initial release

2018

2015

1979

2019

2009

Current release

2.3, January 2021

V12, November 2020

cloud service with continuous releases

3.2.0, December 2022

License

Commercial or Open Source

commercial

free trial version available

commercial

Restricted, free version available

commercial

Open Source

Apache version 2, commercial enterprise edition

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++, Java

ANSI C, C++

Erlang

Server operating systems

Linux

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

hosted

Linux
OS X

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema free, schema optional, schema required, partial schema,

Fixed schema with schema-less datatypes (dynamic)

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types

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

all fields are automatically indexed

restricted

SQL

Support of SQL

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

yes

yes, ANSI SQL with proprietary extensions

Kusto Query Language (KQL), SQL subset

APIs and other access methods

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

ADO.NET
JDBC
ODBC

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

C++
Java
Python

All languages supporting JDBC/ODBC/ADO.Net

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

user defined functions and aggregates

Java Stored Procedures

yes

.Net, JavaScript, C/C++

Yes, possible languages: KQL, Python, R

Erlang

Triggers

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

Implicit feature of the cloud service

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Multi-source replication between multi datacenters

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

yes

Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

Spark connector (open source): github.com/Azure/azure-kusto-spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

links between data sets can be stored

Transaction concepts

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

ACID

tunable from ACID to Eventually Consistent

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

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

Fine grained access rights according to SQL-standard with additional protections for files

Azure Active Directory Authentication

yes, using Riak Security

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More resources

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