DBMS > Apache Ignite vs. BigObject vs. Datomic

System Properties Comparison Apache Ignite vs. BigObject vs. Datomic

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Editorial information provided by DB-Engines

Name

Description

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

Analytic DBMS for real-time computations and queries

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Primary database model

Key-value store
Relational DBMS

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

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

Score	2.94
Rank	#92	Overall
	#14	Key-value stores
	#48	Relational DBMS

Score	0.13
Rank	#332	Overall
	#147	Relational DBMS

Score	1.50
Rank	#148	Overall
	#68	Relational DBMS

Website

ignite.apache.org

bigobject.io

www.datomic.com

Technical documentation

apacheignite.readme.io/docs

docs.bigobject.io

docs.datomic.com

Developer

Apache Software Foundation

BigObject, Inc.

Cognitect

Initial release

2015

2012

Current release

2.16.0, December 2023

1.0.7180, July 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free community edition available

commercial

limited edition free

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++, Java, .Net

Java, Clojure

Server operating systems

Linux
OS X
Solaris
Windows

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

All OS with a Java VM

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

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

SQL-like DML and DDL statements

APIs and other access methods

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

fluentd
ODBC
RESTful HTTP API

RESTful HTTP API

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

Clojure
Java

Server-side scripts

Stored procedures

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

Lua

yes

Transaction Functions

Triggers

yes (cache interceptors and events)

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

none

But extensive use of caching in the application peers

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

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

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

In-memory capabilities

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

yes

recommended only for testing and development

User concepts

Access control

Security Hooks for custom implementations

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More resources

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