DBMS > BigObject vs. Databricks vs. GeoMesa vs. Realm

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

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

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

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

Document store
Relational DBMS

Spatial 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	76.33
Rank	#17	Overall
	#3	Document stores
	#11	Relational DBMS

Score	0.81
Rank	#214	Overall
	#4	Spatial DBMS

Score	7.71
Rank	#52	Overall
	#9	Document stores

Website

Technical documentation

docs.bigobject.io

docs.databricks.com

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

realm.io/docs

Developer

BigObject, Inc.

Databricks

CCRi and others

Realm, acquired by MongoDB in May 2019

Initial release

2015

2013

2014

Current release

4.0.5, February 2024

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

Apache License 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

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

Android
Backend: server-less
iOS
Windows

Data scheme

yes

Flexible Schema (defined schema, partial schema, 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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

with Databricks SQL

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

Supported programming languages

Python
R
Scala

.Net
Java

with Android only
Objective-C
React Native
Swift

Server-side scripts

Stored procedures

Lua

user defined functions and aggregates

runs within the applications so server-side scripts are unnecessary

Triggers

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

none

depending on storage layer

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

depending on storage layer

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

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

yes

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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