DBMS > BigObject vs. GeoMesa vs. GridGain vs. Realm

System Properties Comparison BigObject vs. GeoMesa vs. GridGain vs. Realm

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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.

GridGain is an in-memory computing platform, built on Apache Ignite

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

Key-value store
Relational DBMS

Document store

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

Score	0.13
Rank	#342	Overall
	#150	Relational DBMS

Score	0.81
Rank	#214	Overall
	#4	Spatial DBMS

Score	1.53
Rank	#155	Overall
	#26	Key-value stores
	#73	Relational DBMS

Score	7.71
Rank	#52	Overall
	#9	Document stores

Website

Technical documentation

docs.bigobject.io

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

www.gridgain.com/docs/index.html

realm.io/docs

Developer

BigObject, Inc.

CCRi and others

GridGain Systems, Inc.

Realm, acquired by MongoDB in May 2019

Initial release

2015

2014

2007

2014

Current release

4.0.5, February 2024

GridGain 8.5.1

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache License 2.0

commercial

Open Source

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Scala

Java, C++, .Net

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux
OS X
Solaris
Windows

Android
Backend: server-less
iOS
Windows

Data scheme

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

ANSI-99 for query and DML statements, subset of DDL

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
Java

with Android only
Objective-C
React Native
Swift

Server-side scripts

Stored procedures

Lua

yes (compute grid and cache interceptors can be used instead)

runs within the applications so server-side scripts are unnecessary

Triggers

yes (cache interceptors and events)

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

none

depending on storage layer

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

depending on storage layer

yes (replicated cache)

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

none

depending on storage layer

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

depending on storage layer

yes

In-Memory realm

User concepts

Access control

yes

depending on the DBMS used for storage

Security Hooks for custom implementations

yes

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More resources

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