DBMS > Atos Standard Common Repository vs. Google BigQuery vs. Heroic vs. Hypertable vs. ObjectBox

System Properties Comparison Atos Standard Common Repository vs. Google BigQuery vs. Heroic vs. Hypertable vs. ObjectBox

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

Large scale data warehouse service with append-only tables

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

An open source BigTable implementation based on distributed file systems such as Hadoop

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

Primary database model

Document store
Key-value store

Relational DBMS

Time Series DBMS

Wide column store

Object oriented DBMS
Vector DBMS

Secondary database models

Time Series DBMS

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

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	0.13
Rank	#335	Overall
	#29	Time Series DBMS

Score	1.08
Rank	#179	Overall
	#6	Object oriented DBMS
	#9	Vector DBMS

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

cloud.google.com/bigquery

github.com/spotify/heroic

github.com/objectbox
objectbox.io

Technical documentation

cloud.google.com/bigquery/docs

spotify.github.io/heroic

docs.objectbox.io

Developer

Atos Convergence Creators

Google

Spotify

Hypertable Inc.

ObjectBox Limited

Initial release

2016

2010

2014

2009

2017

Current release

1703

0.9.8.11, March 2016

4.0 (May 2024)

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GNU version 3. Commercial license available

Bindings are released under Apache 2.0

Apache License 2.0

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++

C and C++

Server operating systems

Linux

hosted

Linux
OS X
Windows

an inofficial Windows port is available

Android
Any POSIX system
Docker
iOS
Linux
macOS
QNX
Windows

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

yes

Typing

predefined data types such as float or date

optional

yes

yes, plus "flex" map-like types

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

via Elasticsearch

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

yes

APIs and other access methods

LDAP

RESTful HTTP/JSON API

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

C++ API
Thrift

Proprietary native API

Supported programming languages

All languages with LDAP bindings

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
Perl
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor on file system level

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

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

Since BigQuery is designed for querying data

ACID

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

LDAP bind authentication

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

Google Cloud Identity & Access Management (IAM)

yes

More information provided by the system vendor

News

MongoDB Realm & Device Sync alternatives – ObjectBox
17 September 2024

Local AI – what it is and why we need it
11 September 2024

First on-device Vector Database (aka Semantic Index) for iOS
24 July 2024

Retrieval Augmented Generation (RAG) with vector databases: Expanding AI Capabilities
18 June 2024

The on-device Vector Database for Android and Java
29 May 2024

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