DBMS > BigObject vs. GeoMesa vs. Microsoft Azure Cosmos DB vs. NSDb vs. Realm

System Properties Comparison BigObject vs. GeoMesa vs. Microsoft Azure Cosmos DB vs. NSDb vs. Realm

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

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

Globally distributed, horizontally scalable, multi-model database service

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Spatial DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Document store

Secondary database models

Spatial DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

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

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	7.60
Rank	#52	Overall
	#9	Document stores

Website

bigobject.io

www.geomesa.org

azure.microsoft.com/services/cosmos-db

nsdb.io

realm.io

Technical documentation

docs.bigobject.io

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

learn.microsoft.com/azure/cosmos-db

nsdb.io/Architecture

realm.io/docs

Developer

BigObject, Inc.

CCRi and others

Microsoft

Realm, acquired by MongoDB in May 2019

Initial release

2015

2014

2017

2014

Current release

4.0.5, February 2024

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache License 2.0

commercial

Open Source

Apache Version 2.0

Open Source

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

Java, Scala

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

hosted

Linux
macOS

Android
Backend: server-less
iOS
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

yes: int, bigint, decimal, string

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

All properties auto-indexed by default

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

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

gRPC
HTTP REST
WebSocket

Supported programming languages

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

Java
Scala

.Net
Java

with Android only
Objective-C
React Native
Swift

Server-side scripts

Stored procedures

Lua

JavaScript

runs within the applications so server-side scripts are unnecessary

Triggers

JavaScript

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

none

depending on storage layer

Sharding

Implicit feature of the cloud service

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

depending on storage layer

yes

Implicit feature of the cloud service

none

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

none

depending on storage layer

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

yes

In-memory capabilities

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

yes

depending on storage layer

yes

In-Memory realm

User concepts

Access control

yes

depending on the DBMS used for storage

Access rights can be defined down to the item level

yes

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