DBMS > Dgraph vs. Google Cloud Bigtable vs. Hazelcast vs. Microsoft Azure Cosmos DB vs. NSDb

System Properties Comparison Dgraph vs. Google Cloud Bigtable vs. Hazelcast vs. Microsoft Azure Cosmos DB vs. NSDb

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

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 widely adopted in-memory data grid

Globally distributed, horizontally scalable, multi-model database service

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Primary database model

Graph DBMS

Key-value store
Wide column store

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS

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

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

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

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

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

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

dgraph.io

cloud.google.com/bigtable

hazelcast.com

azure.microsoft.com/services/cosmos-db

nsdb.io

Technical documentation

dgraph.io/docs

cloud.google.com/bigtable/docs

hazelcast.org/imdg/docs

learn.microsoft.com/azure/cosmos-db

nsdb.io/Architecture

Developer

Dgraph Labs, Inc.

Google

Hazelcast

Microsoft

Initial release

2016

2015

2008

2014

2017

Current release

5.3.6, November 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

Apache Version 2.0

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

Java, Scala

Server operating systems

Linux
OS X
Windows

hosted

All OS with a Java VM

hosted

Linux
macOS

Data scheme

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes: int, bigint, decimal, string

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

the object must implement a serialization strategy

Secondary indexes

yes

All properties auto-indexed by default

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language

APIs and other access methods

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

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

JCache
JPA
Memcached protocol
RESTful HTTP API

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

gRPC
HTTP REST
WebSocket

Supported programming languages

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

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

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

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

Java
Scala

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

JavaScript

Triggers

yes

Events

JavaScript

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

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

yes

Replicated Map

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

Immediate Consistency

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

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Atomic single-row operations

one or two-phase-commit; repeatable reads; read commited

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Planned for future releases

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

Role-based access control

Access rights can be defined down to the item level

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