DBMS > Dolt vs. Fauna vs. GeoMesa vs. Google BigQuery vs. Microsoft Azure Synapse Analytics

System Properties Comparison Dolt vs. Fauna vs. GeoMesa vs. Google BigQuery vs. Microsoft Azure Synapse Analytics

Editorial information provided by DB-Engines

Name

Description

A MySQL compatible DBMS with Git-like versioning of data and schema

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.

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

Large scale data warehouse service with append-only tables

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

Primary database model

Relational DBMS

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Spatial DBMS

Relational DBMS

Secondary database models

Document store

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.96
Rank	#193	Overall
	#90	Relational DBMS

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

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Website

github.com/dolthub/dolt
www.dolthub.com

fauna.com

www.geomesa.org

cloud.google.com/bigquery

azure.microsoft.com/services/synapse-analytics

Technical documentation

docs.dolthub.com

docs.fauna.com

www.geomesa.org/documentation/stable/user/index.html

cloud.google.com/bigquery/docs

docs.microsoft.com/azure/synapse-analytics

Developer

DoltHub Inc

Fauna, Inc.

CCRi and others

Google

Microsoft

Initial release

2018

2014

2010

2016

Current release

4.0.5, February 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache License 2.0

commercial

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

C++

Server operating systems

Linux
macOS
Windows

hosted

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

CLI Client
HTTP REST

RESTful HTTP API

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
Java
PHP

Server-side scripts

Stored procedures

yes

currently in alpha release

user defined functions

in JavaScript

Transact SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

consistent hashing

depending on storage layer

none

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

Multi-source replication

depending on storage layer

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

depending on storage layer

Immediate Consistency

Foreign keys

Referential integrity

yes

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

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

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.

depending on storage layer

User concepts

Access control

Only one user is configurable, and must be specified in the config file at startup

Identity management, authentication, and access control

yes

depending on the DBMS used for storage

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

yes

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