DBMS > AnzoGraph DB vs. FatDB vs. Google Cloud Firestore vs. Microsoft SQL Server vs. OpenTSDB

System Properties Comparison AnzoGraph DB vs. FatDB vs. Google Cloud Firestore vs. Microsoft SQL Server vs. OpenTSDB

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

Microsofts flagship relational DBMS

Scalable Time Series DBMS based on HBase

Primary database model

Graph DBMS
RDF store

Document store
Key-value store

Document store

Relational DBMS

Time Series DBMS

Secondary database models

Document store
Graph DBMS
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	7.36
Rank	#53	Overall
	#9	Document stores

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	1.68
Rank	#142	Overall
	#12	Time Series DBMS

Website

cambridgesemantics.com/anzograph

firebase.google.com/products/firestore

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

opentsdb.net

Technical documentation

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

firebase.google.com/docs/firestore

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

opentsdb.net/docs/build/html/index.html

Developer

Cambridge Semantics

FatCloud

Google

Microsoft

currently maintained by Yahoo and other contributors

Initial release

2018

2012

2017

1989

2011

Current release

2.3, January 2021

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

free trial version available

commercial

restricted free version is available

Open Source

LGPL

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

Java

Server operating systems

Linux

Windows

hosted

Linux
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

numeric data for metrics, strings for tags

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.

Via inetgration in SQL Server

yes

APIs and other access methods

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

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

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

HTTP API
Telnet API

Supported programming languages

C++
Java
Python

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

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

Erlang
Go
Java
Python
R
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

via applications

yes, Firebase Rules & Cloud Functions

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

Triggers

yes

via applications

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

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

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

selectable replication factor

Multi-source replication

yes, but depending on the SQL-Server Edition

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

Using Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Eventual Consistency
Immediate Consistency

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

not needed in graphs

yes

Transaction concepts

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

ACID

yes

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

Can implement custom security layer via applications

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

fine grained access rights according to SQL-standard

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