DBMS > Apache Impala vs. BigObject vs. GeoMesa vs. Realm

System Properties Comparison Apache Impala vs. BigObject vs. GeoMesa vs. Realm

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Analytic DBMS for real-time computations and queries

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

Spatial DBMS

Document store

Secondary database models

Document store

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

Score	14.03
Rank	#40	Overall
	#24	Relational DBMS

Score	0.13
Rank	#342	Overall
	#150	Relational DBMS

Score	0.81
Rank	#214	Overall
	#4	Spatial DBMS

Score	7.71
Rank	#52	Overall
	#9	Document stores

Website

Technical documentation

impala.apache.org/impala-docs.html

docs.bigobject.io

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

realm.io/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

BigObject, Inc.

CCRi and others

Realm, acquired by MongoDB in May 2019

Initial release

2013

2015

2014

Current release

4.1.0, June 2022

4.0.5, February 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

free community edition available

Open Source

Apache License 2.0

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

C++

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)

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

fluentd
ODBC
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

.Net
Java

with Android only
Objective-C
React Native
Swift

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Lua

runs within the applications so server-side scripts are unnecessary

Triggers

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

depending on storage layer

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

depending on storage layer

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

yes

depending on the DBMS used for storage

yes

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