DBMS > Amazon DocumentDB vs. Google BigQuery vs. Graphite vs. Microsoft Azure Cosmos DB

System Properties Comparison Amazon DocumentDB vs. Google BigQuery vs. Graphite vs. Microsoft Azure Cosmos DB

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Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Large scale data warehouse service with append-only tables

Data logging and graphing tool for time series data

The storage layer (fixed size database) is called Whisper

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Document store

Relational DBMS

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	4.57
Rank	#73	Overall
	#5	Time Series DBMS

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

Website

aws.amazon.com/documentdb

cloud.google.com/bigquery

github.com/graphite-project/graphite-web

azure.microsoft.com/services/cosmos-db

Technical documentation

aws.amazon.com/documentdb/resources

cloud.google.com/bigquery/docs

graphite.readthedocs.io

learn.microsoft.com/azure/cosmos-db

Developer

Google

Chris Davis

Microsoft

Initial release

2019

2010

2006

2014

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

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

Python

Server operating systems

hosted

Linux
Unix

hosted

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

Numeric data only

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

yes

SQL-like query language

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

RESTful HTTP/JSON API

HTTP API
Sockets

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

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

none

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

Atomic single-document operations

Since BigQuery is designed for querying data

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

locking

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.

User concepts

Access control

Access rights for users and roles

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

Google Cloud Identity & Access Management (IAM)

Access rights can be defined down to the item level

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