DBMS > BigObject vs. Google Cloud Bigtable vs. Oracle NoSQL vs. Sequoiadb

System Properties Comparison BigObject vs. Google Cloud Bigtable vs. Oracle NoSQL vs. Sequoiadb

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

NewSQL database with distributed OLTP and SQL

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store
Wide column store

Document store
Key-value store
Relational DBMS

Document store
Relational DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Score	0.45
Rank	#261	Overall
	#41	Document stores
	#122	Relational DBMS

Website

bigobject.io

cloud.google.com/bigtable

www.oracle.com/database/nosql/technologies/nosql

www.sequoiadb.com

Technical documentation

docs.bigobject.io

cloud.google.com/bigtable/docs

docs.oracle.com/en/database/other-databases/nosql-database/index.html

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

BigObject, Inc.

Google

Oracle

Sequoiadb Ltd.

Initial release

2015

2011

2013

Current release

23.3, December 2023

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Open Source

Server: AGPL; Client: Apache V2

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++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

hosted

Linux
Solaris SPARC/x86

Linux

Data scheme

yes

schema-free

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

schema-free

Typing

predefined data types such as float or date

yes

optional

yes

oid, date, timestamp, binary, regex

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

RESTful HTTP API

proprietary protocol using JSON

Supported programming languages

C#
C++
Go
Java
JavaScript (Node.js)
Python

C
C#
Go
Java
JavaScript (Node.js)
Python

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

Lua

JavaScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Internal replication in Colossus, and regional replication between two clusters in different zones

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Eventual Consistency
Immediate Consistency

depending on configuration

Eventual 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

Atomic single-row operations

configurable

ACID within a storage node (=shard)

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

off heap cache

User concepts

Access control

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights for users and roles

simple password-based access control

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