DBMS > BigObject vs. Datomic vs. DuckDB vs. GigaSpaces vs. Google BigQuery

System Properties Comparison BigObject vs. Datomic vs. DuckDB vs. GigaSpaces vs. Google BigQuery

Editorial information provided by DB-Engines

Name

Description

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

An embeddable, in-process, column-oriented SQL OLAP RDBMS

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Large scale data warehouse service with append-only tables

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Secondary database models

Graph DBMS
Search engine

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	4.63
Rank	#69	Overall
	#37	Relational DBMS

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Website

cloud.google.com/bigquery

Technical documentation

docs.bigobject.io

docs.datomic.com

duckdb.org/docs

docs.gigaspaces.com/latest/landing.html

cloud.google.com/bigquery/docs

Developer

BigObject, Inc.

Cognitect

Gigaspaces Technologies

Google

Initial release

2015

2012

2018

2000

2010

Current release

1.0.6735, June 2023

0.10, February 2024

15.5, September 2020

License

Commercial or Open Source

commercial

free community edition available

commercial

limited edition free

Open Source

MIT License

Open Source

Apache Version 2; Commercial licenses available

commercial

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, Clojure

C++

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)

All OS with a Java VM

server-less

Linux
macOS
Solaris
Windows

hosted

Data scheme

yes

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.

XML can be used for describing objects metadata

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-99 for query and DML statements

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

RESTful HTTP API

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

RESTful HTTP/JSON API

Supported programming languages

Clojure
Java

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

.Net
C++
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Server-side scripts

Stored procedures

Lua

yes

Transaction Functions

yes

user defined functions

in JavaScript

Triggers

By using transaction functions

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

none

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

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

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

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

yes

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

yes

User concepts

Access control

Role-based access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

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