DBMS > BigObject vs. Google Cloud Bigtable vs. Microsoft Azure AI Search vs. Microsoft Azure Cosmos DB

System Properties Comparison BigObject vs. Google Cloud Bigtable vs. Microsoft Azure AI Search vs. Microsoft Azure Cosmos DB

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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.

Search-as-a-service for web and mobile app development

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store
Wide column store

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Vector DBMS

Spatial 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	5.59
Rank	#63	Overall
	#7	Search engines

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

bigobject.io

cloud.google.com/bigtable

azure.microsoft.com/en-us/services/search

azure.microsoft.com/services/cosmos-db

Technical documentation

docs.bigobject.io

cloud.google.com/bigtable/docs

learn.microsoft.com/en-us/azure/search

learn.microsoft.com/azure/cosmos-db

Developer

BigObject, Inc.

Google

Microsoft

Initial release

2015

2014

Current release

License

Commercial or Open Source

commercial

free community edition available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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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

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON 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.

Secondary indexes

yes

All properties auto-indexed by default

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

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Supported programming languages

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

C#
Java
JavaScript
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

Lua

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

yes

Implicit feature of the cloud service

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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)

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session 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

Multi-item ACID transactions with snapshot isolation within a partition

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

User concepts

Access control

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

yes

using Azure authentication

Access rights can be defined down to the item level

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