DBMS > Amazon DynamoDB vs. Google Cloud Datastore vs. Microsoft Azure Cosmos DB vs. Tibero vs. TigerGraph

System Properties Comparison Amazon DynamoDB vs. Google Cloud Datastore vs. Microsoft Azure Cosmos DB vs. Tibero vs. TigerGraph

Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Globally distributed, horizontally scalable, multi-model database service

A secure RDBMS, designed for easy portability from Oracle

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Document store
Key-value store

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Graph DBMS

Secondary database models

Spatial DBMS

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

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

Score	1.78
Rank	#140	Overall
	#64	Relational DBMS

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Website

aws.amazon.com/dynamodb

cloud.google.com/datastore

azure.microsoft.com/services/cosmos-db

us.tmaxsoft.com/products/tibero

www.tigergraph.com

Technical documentation

docs.aws.amazon.com/dynamodb

cloud.google.com/datastore/docs

learn.microsoft.com/azure/cosmos-db

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

docs.tigergraph.com

Developer

Amazon

Google

Microsoft

TmaxSoft

Initial release

2012

2008

2014

2003

2017

Current release

6, April 2015

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

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

C and Assembler

C++

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Windows

Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

yes

JSON types

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.

yes

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language (GQL)

SQL-like query language

yes

SQL-like query language (GSQL)

APIs and other access methods

RESTful HTTP API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

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

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

C++
Java

Server-side scripts

Stored procedures

using Google App Engine

JavaScript

Persistent Stored Procedure (PSM)

yes

Triggers

yes

by integration with AWS Lambda

Callbacks using the Google Apps Engine

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

using Google Cloud Dataflow

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Relationships in graphs

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Multi-item ACID transactions with snapshot isolation within a partition

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.

planned for next version

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

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

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

Role-based access control

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