DBMS > AllegroGraph vs. Fujitsu Enterprise Postgres vs. LeanXcale vs. Microsoft Azure Data Explorer vs. SQLite

System Properties Comparison AllegroGraph vs. Fujitsu Enterprise Postgres vs. LeanXcale vs. Microsoft Azure Data Explorer vs. SQLite

Editorial information provided by DB-Engines

Name

Description

High performance, persistent RDF store with additional support for Graph DBMS

Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Fully managed big data interactive analytics platform

Widely used embeddable, in-process RDBMS

Primary database model

Document store

with version 6.5
Graph DBMS
RDF store
Vector DBMS

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

column oriented

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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	1.06
Rank	#187	Overall
	#30	Document stores
	#17	Graph DBMS
	#7	RDF stores
	#7	Vector DBMS

Score	0.31
Rank	#285	Overall
	#129	Relational DBMS

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

allegrograph.com

www.postgresql.fastware.com

www.leanxcale.com

azure.microsoft.com/services/data-explorer

www.sqlite.org

Technical documentation

franz.com/agraph/support/documentation/current/agraph-introduction.html

www.postgresql.fastware.com/product-manuals

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

www.sqlite.org/docs.html

Developer

Franz Inc.

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

LeanXcale

Microsoft

Dwayne Richard Hipp

Initial release

2004

2015

2019

2000

Current release

8.0, December 2023

Fujitsu Enterprise Postgres 14, January 2022

cloud service with continuous releases

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

Limited community edition free

commercial

Open Source

Public Domain

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

Server operating systems

Linux
OS X
Windows

Linux
Windows

hosted

server-less

Data scheme

yes

RDF schemas

yes

Fixed schema with schema-less datatypes (dynamic)

yes

dynamic column types

Typing

predefined data types such as float or date

yes

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

yes

not rigid because of 'dynamic typing' concept.

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.

bulk load of XML files possible

yes

Secondary indexes

yes

all fields are automatically indexed

yes

SQL

Support of SQL

SPARQL is used as query language

yes

through Apache Derby

Kusto Query Language (KQL), SQL subset

yes

SQL-92 is not fully supported

APIs and other access methods

RESTful HTTP API
SPARQL

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

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

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

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C
Java
Scala

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

JavaScript or Common Lisp

user defined functions

Yes, possible languages: KQL, Python, R

Triggers

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

with Federation

partitioning by range, list and by hash

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

yes

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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

via file-system locks

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

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

fine grained access rights according to SQL-standard

Azure Active Directory Authentication

More information provided by the system vendor

Specific characteristics

Knowledge Graph Platform Leader FedShard - Designed for Entity-Event Knowledge Graph...
» more

100% compatible with community PostgreSQL
» more

Competitive advantages

AllegroGraph is uniquely suited to support adhoc queries through SPARQL, Prolog and...
» more

Built-in TDE and Data Masking security. In-Memory Columnar Index, and a high speed...
» more

Typical application scenarios

Transactional payments applications, reporting and mixed workloads.
» more

Market metrics

Over 30 years experience in database technology. Over 20 years in Postgres development...
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Licensing and pricing models

Core based licensing
» more

News

Can Neuro-Symbolic AI Solve AI’s Weaknesses?
17 April 2024

100 Companies That Matter in KM – Franz Inc.
3 April 2024

Exploring AllegroGraph v8 – Unleashing the Power of Neuro-Symbolic AI (Recorded Webinar)
9 February 2024

What is Neuro-Symbolic AI?
23 January 2024

Allegro CL v11 – Now Available! – The Neuro-Symbolic AI Programming Platform
8 January 2024

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