DBMS > GeoMesa vs. Google BigQuery vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. MySQL

System Properties Comparison GeoMesa vs. Google BigQuery vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. MySQL

Editorial information provided by DB-Engines

Name

Description

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

Large scale data warehouse service with append-only tables

Globally distributed, horizontally scalable, multi-model database service

A Wide Column Store for rapid development using massive semi-structured datasets

Widely used open source RDBMS

Primary database model

Spatial DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Wide column store

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

www.geomesa.org

cloud.google.com/bigquery

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/en-us/services/storage/tables

www.mysql.com

Technical documentation

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

cloud.google.com/bigquery/docs

learn.microsoft.com/azure/cosmos-db

dev.mysql.com/doc

Developer

CCRi and others

Google

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2014

2010

2014

2012

1995

Current release

4.0.5, February 2024

8.4.0, April 2024

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Scala

C and C++

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON 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

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP/JSON API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

JavaScript

yes

proprietary syntax

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

none

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

yes

Implicit feature of the cloud service

yes

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

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

depending on storage layer

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

Since BigQuery is designed for querying data

Multi-item ACID transactions with snapshot isolation within a partition

optimistic locking

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

yes

User concepts

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)

Access rights can be defined down to the item level

Access rights based on private key authentication or shared access signatures

Users with fine-grained authorization concept

no user groups or roles

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