DBMS > AgensGraph vs. Amazon DynamoDB vs. Datastax Enterprise vs. GridGain

System Properties Comparison AgensGraph vs. Amazon DynamoDB vs. Datastax Enterprise vs. GridGain

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Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

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

DataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.

GridGain is an in-memory computing platform, built on Apache Ignite

Primary database model

Graph DBMS
Relational DBMS

Document store
Key-value store

Wide column store

Key-value store
Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

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

Score	0.23
Rank	#315	Overall
	#26	Graph DBMS
	#140	Relational DBMS

Score	74.45
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	5.93
Rank	#56	Overall
	#4	Wide column stores

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Website

bitnine.net/agensgraph

aws.amazon.com/dynamodb

www.datastax.com/products/datastax-enterprise

www.gridgain.com

Technical documentation

bitnine.net/documentation

docs.aws.amazon.com/dynamodb

docs.datastax.com

www.gridgain.com/docs/index.html

Developer

Bitnine Global Inc.

Amazon

DataStax

GridGain Systems, Inc.

Initial release

2016

2012

2011

2007

Current release

2.1, December 2018

6.8, April 2020

GridGain 8.5.1

License

Commercial or Open Source

Open Source

Apache License 2.0

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.

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Implementation language

Java

Java, C++, .Net

Server operating systems

Linux
OS X
Windows

hosted

Linux
OS X

Linux
OS X
Solaris
Windows

Data scheme

depending on used data model

schema-free

yes

Typing

predefined data types such as float or date

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

SQL

Support of SQL

yes

SQL-like DML and DDL statements (CQL); Spark SQL

ANSI-99 for query and DML statements, subset of DDL

APIs and other access methods

Cypher Query Language
JDBC

RESTful HTTP API

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Supported programming languages

C
Java
JavaScript
Python

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

yes (compute grid and cache interceptors can be used instead)

Triggers

yes

by integration with AWS Lambda

yes

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

Sharding

no "single point of failure"

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

configurable replication factor, datacenter aware, advanced replication for edge computing

yes (replicated cache)

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Immediate Consistency

Foreign keys

Referential integrity

yes

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

Atomicity and isolation are supported for single operations

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

fine grained access rights according to SQL-standard

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

Access rights for users can be defined per object

Security Hooks for custom implementations

More information provided by the system vendor

Specific characteristics

DataStax Enterprise is scale-out data infrastructure for enterprises that need to...
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Competitive advantages

Supporting the following application requirements: Zero downtime - Built on Apache...
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Typical application scenarios

Applications that must be massively and linearly scalable with 100% uptime and able...
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Key customers

Capital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
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Market metrics

Among the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
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Licensing and pricing models

Annual subscription
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