DBMS > Google BigQuery vs. GridGain vs. ObjectBox vs. XTDB

System Properties Comparison Google BigQuery vs. GridGain vs. ObjectBox vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

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

Lightweight, fast on-device database for IoT, Mobile and Embedded devices, persisting and synchronising objects and vectors

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Object oriented DBMS
Vector DBMS

Document store

Secondary database models

Time Series DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	1.29
Rank	#166	Overall
	#5	Object oriented DBMS
	#7	Vector DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

cloud.google.com/bigquery

www.gridgain.com

github.com/objectbox
objectbox.io

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

cloud.google.com/bigquery/docs

www.gridgain.com/docs/index.html

docs.objectbox.io

www.xtdb.com/docs

Developer

Google

GridGain Systems, Inc.

ObjectBox Limited

Juxt Ltd.

Initial release

2010

2007

2017

2019

Current release

GridGain 8.5.1

4.0 (May 2024)

1.19, September 2021

License

Commercial or Open Source

commercial

Bindings are released under Apache 2.0

Apache License 2.0

Open Source

MIT License

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, C++, .Net

C and C++

Clojure

Server operating systems

hosted

Linux
OS X
Solaris
Windows

Android
Any POSIX system
Docker
iOS
Linux
macOS
QNX
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, plus "flex" map-like types

yes, extensible-data-notation format

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

yes

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

limited SQL, making use of Apache Calcite

APIs and other access methods

RESTful HTTP/JSON API

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

Proprietary native API

HTTP REST
JDBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

C
C++
Dart (Flutter)
Go
Java
Kotlin
Python
Swift

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

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

Triggers

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Data sync between devices allowing occasional connected databases to work completely offline

yes, each node contains all data

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Security Hooks for custom implementations

yes

More information provided by the system vendor

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