DBMS > Apache IoTDB vs. Firebase Realtime Database vs. GridGain vs. SQLite vs. WakandaDB

System Properties Comparison Apache IoTDB vs. Firebase Realtime Database vs. GridGain vs. SQLite vs. WakandaDB

Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

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

Widely used embeddable, in-process RDBMS

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Time Series DBMS

Document store

Key-value store
Relational DBMS

Relational DBMS

Object oriented DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

iotdb.apache.org

firebase.google.com/products/realtime-database

www.gridgain.com

www.sqlite.org

wakanda.github.io

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

firebase.google.com/docs/database

www.gridgain.com/docs/index.html

www.sqlite.org/docs.html

wakanda.github.io/doc

Developer

Apache Software Foundation

Google

acquired by Google 2014

GridGain Systems, Inc.

Dwayne Richard Hipp

Wakanda SAS

Initial release

2018

2012

2007

2000

2012

Current release

1.1.0, April 2023

GridGain 8.5.1

3.45.3 (15 April 2024), April 2024

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Public Domain

Open Source

AGPLv3, extended commercial license available

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

Java

Java, C++, .Net

C++, JavaScript

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

Linux
OS X
Solaris
Windows

server-less

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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 query language

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

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
Native API

Android
iOS
JavaScript API
RESTful HTTP API

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

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Java
JavaScript
Objective-C

C#
C++
Java
PHP
Python
Ruby
Scala

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

JavaScript

Server-side scripts

Stored procedures

yes

limited functionality with using 'rules'

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

yes

Triggers

yes

Callbacks are triggered when data changes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes (replicated cache)

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

User concepts

Access control

yes

yes, based on authentication and database rules

Security Hooks for custom implementations

yes

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