DBMS > Fauna vs. Lovefield vs. Microsoft Azure Data Explorer vs. Microsoft Azure Synapse Analytics vs. Stardog

System Properties Comparison Fauna vs. Lovefield vs. Microsoft Azure Data Explorer vs. Microsoft Azure Synapse Analytics vs. Stardog

Editorial information provided by DB-Engines

Name

Description

Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.

Embeddable relational database for web apps written in pure JavaScript

Fully managed big data interactive analytics platform

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Relational DBMS

column oriented

Relational DBMS

Graph DBMS
RDF store

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	1.52
Rank	#153	Overall
	#26	Document stores
	#14	Graph DBMS
	#71	Relational DBMS
	#13	Time Series DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

fauna.com

google.github.io/lovefield

azure.microsoft.com/services/data-explorer

azure.microsoft.com/services/synapse-analytics

www.stardog.com

Technical documentation

docs.fauna.com

github.com/google/lovefield/blob/master/docs/spec_index.md

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

docs.microsoft.com/azure/synapse-analytics

docs.stardog.com

Developer

Fauna, Inc.

Google

Microsoft

Stardog-Union

Initial release

2014

2019

2016

2010

Current release

2.1.12, February 2017

cloud service with continuous releases

7.3.0, May 2020

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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

Scala

JavaScript

C++

Java

Server operating systems

hosted

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

hosted

Linux
macOS
Windows

Data scheme

schema-free

yes

Fixed schema with schema-less datatypes (dynamic)

yes

schema-free and OWL/RDFS-schema support

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

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

Import/export of XML data possible

Secondary indexes

yes

all fields are automatically indexed

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

Kusto Query Language (KQL), SQL subset

yes

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

RESTful HTTP API

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

ADO.NET
JDBC
ODBC

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

JavaScript

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

C#
Java
PHP

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

Yes, possible languages: KQL, Python, R

Transact SQL

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

Using read-only observers

yes

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

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

consistent hashing

none

Sharding

Implicit feature of the cloud service

Sharding, horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

yes

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

yes

Multi-source replication in HA-Cluster

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

Eventual Consistency
Immediate Consistency

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

relationships in graphs

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

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

yes

User concepts

Access control

Identity management, authentication, and access control

Azure Active Directory Authentication

yes

Access rights for users and roles

More information provided by the system vendor

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