DBMS > Badger vs. GigaSpaces vs. Google BigQuery vs. Heroic vs. JaguarDB

System Properties Comparison Badger vs. GigaSpaces vs. Google BigQuery vs. Heroic vs. JaguarDB

Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Performant, highly scalable DBMS for AI and IoT applications

Primary database model

Key-value store

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Time Series DBMS

Key-value store
Vector DBMS

Secondary database models

Graph DBMS
Search engine

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Website

github.com/dgraph-io/badger

www.gigaspaces.com

cloud.google.com/bigquery

github.com/spotify/heroic

www.jaguardb.com

Technical documentation

godoc.org/github.com/dgraph-io/badger

docs.gigaspaces.com/latest/landing.html

cloud.google.com/bigquery/docs

spotify.github.io/heroic

www.jaguardb.com/support.html

Developer

DGraph Labs

Gigaspaces Technologies

Google

Spotify

DataJaguar, Inc.

Initial release

2017

2000

2010

2014

2015

Current release

15.5, September 2020

3.3 July 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2; Commercial licenses available

commercial

Open Source

Apache 2.0

Open Source

GPL V3.0

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

Java

C++

the server part. Clients available in other languages

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
macOS
Solaris
Windows

hosted

Linux

Data scheme

schema-free

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

via Elasticsearch

yes

SQL

Support of SQL

SQL-99 for query and DML statements

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

APIs and other access methods

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

RESTful HTTP/JSON API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
ODBC

Supported programming languages

.Net
C++
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

Triggers

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

Multi-source replication

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

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

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

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

Role-based access control

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

Google Cloud Identity & Access Management (IAM)

rights management via user accounts

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.